diff --git a/data/trained_openclip/negative_logs/dvqa_qa_captions_new_sampled/2024_11_18-16_47_17-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/out.log b/data/trained_openclip/negative_logs/dvqa_qa_captions_new_sampled/2024_11_18-16_47_17-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/out.log deleted file mode 100644 index 1501b13a601290ddc77bd2ca50a709a0b6bca43a..0000000000000000000000000000000000000000 --- a/data/trained_openclip/negative_logs/dvqa_qa_captions_new_sampled/2024_11_18-16_47_17-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/out.log +++ /dev/null @@ -1,113 +0,0 @@ -2024-11-18,16:47:17 | INFO | Running in distributed mode with multiple processes. Device: cuda:0.Process (global: 0, local 0), total 8. -2024-11-18,16:47:17 | INFO | Loading ViT-L-14-336 model config. -2024-11-18,16:47:20 | INFO | Loading pretrained ViT-L-14-336 weights (data/openclip-vit-14-336/openclip_model.pt). -2024-11-18,16:47:28 | INFO | Model: -2024-11-18,16:47:28 | INFO | CLIP( - (visual): VisualTransformer( - (conv1): Conv2d(3, 1024, kernel_size=(14, 14), stride=(14, 14), bias=False) - (ln_pre): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) - (transformer): Transformer( - (resblocks): ModuleList( - (0-23): 24 x ResidualAttentionBlock( - (attn): MultiheadAttention( - (out_proj): NonDynamicallyQuantizableLinear(in_features=1024, out_features=1024, bias=True) - ) - (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) - (mlp): Sequential( - (c_fc): Linear(in_features=1024, out_features=4096, bias=True) - (gelu): QuickGELU() - (c_proj): Linear(in_features=4096, out_features=1024, bias=True) - ) - (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) - ) - ) - ) - (ln_post): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) - ) - (transformer): Transformer( - (resblocks): ModuleList( - (0-11): 12 x ResidualAttentionBlock( - (attn): MultiheadAttention( - (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) - ) - (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) - (mlp): Sequential( - (c_fc): Linear(in_features=768, out_features=3072, bias=True) - (gelu): QuickGELU() - (c_proj): Linear(in_features=3072, out_features=768, bias=True) - ) - (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) - ) - ) - ) - (token_embedding): Embedding(49408, 768) - (ln_final): LayerNorm((768,), eps=1e-05, elementwise_affine=True) -) -2024-11-18,16:47:28 | INFO | Params: -2024-11-18,16:47:28 | INFO | batch_size: 64 -2024-11-18,16:47:28 | INFO | beta1: 0.9 -2024-11-18,16:47:28 | INFO | beta2: 0.98 -2024-11-18,16:47:28 | INFO | checkpoint_path: data/trained_openclip/negative_logs/dvqa_qa_captions_new_sampled/2024_11_18-16_47_17-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/checkpoints -2024-11-18,16:47:28 | INFO | copy_codebase: False -2024-11-18,16:47:28 | INFO | csv_caption_key: caption -2024-11-18,16:47:28 | INFO | csv_hard_captions_key: neg_caption -2024-11-18,16:47:28 | INFO | csv_img_key: img_path -2024-11-18,16:47:28 | INFO | csv_separator: , -2024-11-18,16:47:28 | INFO | dataset_resampled: False -2024-11-18,16:47:28 | INFO | dataset_type: csv -2024-11-18,16:47:28 | INFO | ddp_static_graph: False -2024-11-18,16:47:28 | INFO | debug: False -2024-11-18,16:47:28 | INFO | device: cuda:0 -2024-11-18,16:47:28 | INFO | dist_backend: nccl -2024-11-18,16:47:28 | INFO | dist_url: env:// -2024-11-18,16:47:28 | INFO | distributed: True -2024-11-18,16:47:28 | INFO | epochs: 3 -2024-11-18,16:47:28 | INFO | eps: 1e-06 -2024-11-18,16:47:28 | INFO | force_quick_gelu: True -2024-11-18,16:47:28 | INFO | gather_with_grad: False -2024-11-18,16:47:28 | INFO | grad_checkpointing: False -2024-11-18,16:47:28 | INFO | horovod: False -2024-11-18,16:47:28 | INFO | imagenet_v2: None -2024-11-18,16:47:28 | INFO | imagenet_val: None -2024-11-18,16:47:28 | INFO | local_loss: False -2024-11-18,16:47:28 | INFO | local_rank: 0 -2024-11-18,16:47:28 | INFO | lock_image: False -2024-11-18,16:47:28 | INFO | lock_image_freeze_bn_stats: False -2024-11-18,16:47:28 | INFO | lock_image_unlocked_groups: 0 -2024-11-18,16:47:28 | INFO | log_level: 20 -2024-11-18,16:47:28 | INFO | log_local: False -2024-11-18,16:47:28 | INFO | log_path: data/trained_openclip/negative_logs/dvqa_qa_captions_new_sampled/2024_11_18-16_47_17-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/out.log -2024-11-18,16:47:28 | INFO | logs: data/trained_openclip/negative_logs/dvqa_qa_captions_new_sampled -2024-11-18,16:47:28 | INFO | lr: 5e-06 -2024-11-18,16:47:28 | INFO | model: ViT-L-14-336 -2024-11-18,16:47:28 | INFO | name: 2024_11_18-16_47_17-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp -2024-11-18,16:47:28 | INFO | no_set_device_rank: False -2024-11-18,16:47:28 | INFO | norm_gradient_clip: None -2024-11-18,16:47:28 | INFO | precision: amp -2024-11-18,16:47:28 | INFO | pretrained: data/openclip-vit-14-336/openclip_model.pt -2024-11-18,16:47:28 | INFO | pretrained_image: False -2024-11-18,16:47:28 | INFO | rank: 0 -2024-11-18,16:47:28 | INFO | report_to: wandb -2024-11-18,16:47:28 | INFO | resume: None -2024-11-18,16:47:28 | INFO | save_frequency: 1 -2024-11-18,16:47:28 | INFO | save_most_recent: False -2024-11-18,16:47:28 | INFO | seed: 0 -2024-11-18,16:47:28 | INFO | skip_scheduler: False -2024-11-18,16:47:28 | INFO | tensorboard: False -2024-11-18,16:47:28 | INFO | tensorboard_path: -2024-11-18,16:47:28 | INFO | torchscript: False -2024-11-18,16:47:28 | INFO | trace: False -2024-11-18,16:47:28 | INFO | train_data: csv_data/dvqa_qa_captions_new_sampled.csv -2024-11-18,16:47:28 | INFO | train_num_samples: None -2024-11-18,16:47:28 | INFO | use_bn_sync: False -2024-11-18,16:47:28 | INFO | val_data: None -2024-11-18,16:47:28 | INFO | val_frequency: 1 -2024-11-18,16:47:28 | INFO | val_num_samples: None -2024-11-18,16:47:28 | INFO | wandb: True -2024-11-18,16:47:28 | INFO | wandb_notes: -2024-11-18,16:47:28 | INFO | wandb_project: neg-clip-dvqa_qa_captions_new_sampled -2024-11-18,16:47:28 | INFO | warmup: 0 -2024-11-18,16:47:28 | INFO | wd: 0.1 -2024-11-18,16:47:28 | INFO | workers: 4 -2024-11-18,16:47:28 | INFO | world_size: 8 -2024-11-18,16:47:28 | INFO | zeroshot_frequency: 2 diff --git a/data/trained_openclip/negative_logs/dvqa_qa_captions_new_sampled/2024_11_18-16_47_17-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/params.txt b/data/trained_openclip/negative_logs/dvqa_qa_captions_new_sampled/2024_11_18-16_47_17-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/params.txt deleted file mode 100644 index fdf8ea6ab47a4189c9389c4b94e1fe8a464bd558..0000000000000000000000000000000000000000 --- a/data/trained_openclip/negative_logs/dvqa_qa_captions_new_sampled/2024_11_18-16_47_17-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/params.txt +++ /dev/null @@ -1,67 +0,0 @@ -batch_size: 64 -beta1: 0.9 -beta2: 0.98 -checkpoint_path: data/trained_openclip/negative_logs/dvqa_qa_captions_new_sampled/2024_11_18-16_47_17-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/checkpoints -copy_codebase: False -csv_caption_key: caption -csv_hard_captions_key: neg_caption -csv_img_key: img_path -csv_separator: , -dataset_resampled: False -dataset_type: csv -ddp_static_graph: False -debug: False -device: cuda:0 -dist_backend: nccl -dist_url: env:// -distributed: True -epochs: 3 -eps: 1e-06 -force_quick_gelu: True -gather_with_grad: False -grad_checkpointing: False -horovod: False -imagenet_v2: None -imagenet_val: None -local_loss: False -local_rank: 0 -lock_image: False -lock_image_freeze_bn_stats: False -lock_image_unlocked_groups: 0 -log_level: 20 -log_local: False -log_path: data/trained_openclip/negative_logs/dvqa_qa_captions_new_sampled/2024_11_18-16_47_17-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/out.log -logs: data/trained_openclip/negative_logs/dvqa_qa_captions_new_sampled -lr: 5e-06 -model: ViT-L-14-336 -name: 2024_11_18-16_47_17-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp -no_set_device_rank: False -norm_gradient_clip: None -precision: amp -pretrained: data/openclip-vit-14-336/openclip_model.pt -pretrained_image: False -rank: 0 -report_to: wandb -resume: None -save_frequency: 1 -save_most_recent: False -seed: 0 -skip_scheduler: False -tensorboard: False -tensorboard_path: -torchscript: False -trace: False -train_data: csv_data/dvqa_qa_captions_new_sampled.csv -train_num_samples: None -use_bn_sync: False -val_data: None -val_frequency: 1 -val_num_samples: None -wandb: True -wandb_notes: -wandb_project: neg-clip-dvqa_qa_captions_new_sampled -warmup: 0 -wd: 0.1 -workers: 4 -world_size: 8 -zeroshot_frequency: 2 diff --git a/data/trained_openclip/negative_logs/dvqa_qa_captions_new_sampled/2024_11_18-16_48_21-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/out.log b/data/trained_openclip/negative_logs/dvqa_qa_captions_new_sampled/2024_11_18-16_48_21-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/out.log deleted file mode 100644 index 3af0e478bea2c423a97dd4cc460c19733375bc85..0000000000000000000000000000000000000000 --- a/data/trained_openclip/negative_logs/dvqa_qa_captions_new_sampled/2024_11_18-16_48_21-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/out.log +++ /dev/null @@ -1,113 +0,0 @@ -2024-11-18,16:48:21 | INFO | Running in distributed mode with multiple processes. Device: cuda:0.Process (global: 0, local 0), total 8. -2024-11-18,16:48:21 | INFO | Loading ViT-L-14-336 model config. -2024-11-18,16:48:25 | INFO | Loading pretrained ViT-L-14-336 weights (data/openclip-vit-14-336/openclip_model.pt). -2024-11-18,16:48:31 | INFO | Model: -2024-11-18,16:48:31 | INFO | CLIP( - (visual): VisualTransformer( - (conv1): Conv2d(3, 1024, kernel_size=(14, 14), stride=(14, 14), bias=False) - (ln_pre): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) - (transformer): Transformer( - (resblocks): ModuleList( - (0-23): 24 x ResidualAttentionBlock( - (attn): MultiheadAttention( - (out_proj): NonDynamicallyQuantizableLinear(in_features=1024, out_features=1024, bias=True) - ) - (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) - (mlp): Sequential( - (c_fc): Linear(in_features=1024, out_features=4096, bias=True) - (gelu): QuickGELU() - (c_proj): Linear(in_features=4096, out_features=1024, bias=True) - ) - (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) - ) - ) - ) - (ln_post): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) - ) - (transformer): Transformer( - (resblocks): ModuleList( - (0-11): 12 x ResidualAttentionBlock( - (attn): MultiheadAttention( - (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) - ) - (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) - (mlp): Sequential( - (c_fc): Linear(in_features=768, out_features=3072, bias=True) - (gelu): QuickGELU() - (c_proj): Linear(in_features=3072, out_features=768, bias=True) - ) - (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) - ) - ) - ) - (token_embedding): Embedding(49408, 768) - (ln_final): LayerNorm((768,), eps=1e-05, elementwise_affine=True) -) -2024-11-18,16:48:31 | INFO | Params: -2024-11-18,16:48:31 | INFO | batch_size: 64 -2024-11-18,16:48:31 | INFO | beta1: 0.9 -2024-11-18,16:48:31 | INFO | beta2: 0.98 -2024-11-18,16:48:31 | INFO | checkpoint_path: data/trained_openclip/negative_logs/dvqa_qa_captions_new_sampled/2024_11_18-16_48_21-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/checkpoints -2024-11-18,16:48:31 | INFO | copy_codebase: False -2024-11-18,16:48:31 | INFO | csv_caption_key: caption -2024-11-18,16:48:31 | INFO | csv_hard_captions_key: neg_caption -2024-11-18,16:48:31 | INFO | csv_img_key: img_path -2024-11-18,16:48:31 | INFO | csv_separator: , -2024-11-18,16:48:31 | INFO | dataset_resampled: False -2024-11-18,16:48:31 | INFO | dataset_type: csv -2024-11-18,16:48:31 | INFO | ddp_static_graph: False -2024-11-18,16:48:31 | INFO | debug: False -2024-11-18,16:48:31 | INFO | device: cuda:0 -2024-11-18,16:48:31 | INFO | dist_backend: nccl -2024-11-18,16:48:31 | INFO | dist_url: env:// -2024-11-18,16:48:31 | INFO | distributed: True -2024-11-18,16:48:31 | INFO | epochs: 3 -2024-11-18,16:48:31 | INFO | eps: 1e-06 -2024-11-18,16:48:31 | INFO | force_quick_gelu: True -2024-11-18,16:48:31 | INFO | gather_with_grad: False -2024-11-18,16:48:31 | INFO | grad_checkpointing: False -2024-11-18,16:48:31 | INFO | horovod: False -2024-11-18,16:48:31 | INFO | imagenet_v2: None -2024-11-18,16:48:31 | INFO | imagenet_val: None -2024-11-18,16:48:31 | INFO | local_loss: False -2024-11-18,16:48:31 | INFO | local_rank: 0 -2024-11-18,16:48:31 | INFO | lock_image: False -2024-11-18,16:48:31 | INFO | lock_image_freeze_bn_stats: False -2024-11-18,16:48:31 | INFO | lock_image_unlocked_groups: 0 -2024-11-18,16:48:31 | INFO | log_level: 20 -2024-11-18,16:48:31 | INFO | log_local: False -2024-11-18,16:48:31 | INFO | log_path: data/trained_openclip/negative_logs/dvqa_qa_captions_new_sampled/2024_11_18-16_48_21-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/out.log -2024-11-18,16:48:31 | INFO | logs: data/trained_openclip/negative_logs/dvqa_qa_captions_new_sampled -2024-11-18,16:48:31 | INFO | lr: 5e-06 -2024-11-18,16:48:31 | INFO | model: ViT-L-14-336 -2024-11-18,16:48:31 | INFO | name: 2024_11_18-16_48_21-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp -2024-11-18,16:48:31 | INFO | no_set_device_rank: False -2024-11-18,16:48:31 | INFO | norm_gradient_clip: None -2024-11-18,16:48:31 | INFO | precision: amp -2024-11-18,16:48:31 | INFO | pretrained: data/openclip-vit-14-336/openclip_model.pt -2024-11-18,16:48:31 | INFO | pretrained_image: False -2024-11-18,16:48:31 | INFO | rank: 0 -2024-11-18,16:48:31 | INFO | report_to: wandb -2024-11-18,16:48:31 | INFO | resume: None -2024-11-18,16:48:31 | INFO | save_frequency: 1 -2024-11-18,16:48:31 | INFO | save_most_recent: False -2024-11-18,16:48:31 | INFO | seed: 0 -2024-11-18,16:48:31 | INFO | skip_scheduler: False -2024-11-18,16:48:31 | INFO | tensorboard: False -2024-11-18,16:48:31 | INFO | tensorboard_path: -2024-11-18,16:48:31 | INFO | torchscript: False -2024-11-18,16:48:31 | INFO | trace: False -2024-11-18,16:48:31 | INFO | train_data: csv_data/dvqa_qa_captions_new_sampled.csv -2024-11-18,16:48:31 | INFO | train_num_samples: None -2024-11-18,16:48:31 | INFO | use_bn_sync: False -2024-11-18,16:48:31 | INFO | val_data: None -2024-11-18,16:48:31 | INFO | val_frequency: 1 -2024-11-18,16:48:31 | INFO | val_num_samples: None -2024-11-18,16:48:31 | INFO | wandb: True -2024-11-18,16:48:31 | INFO | wandb_notes: -2024-11-18,16:48:31 | INFO | wandb_project: neg-clip-dvqa_qa_captions_new_sampled -2024-11-18,16:48:31 | INFO | warmup: 0 -2024-11-18,16:48:31 | INFO | wd: 0.1 -2024-11-18,16:48:31 | INFO | workers: 4 -2024-11-18,16:48:31 | INFO | world_size: 8 -2024-11-18,16:48:31 | INFO | zeroshot_frequency: 2 diff --git a/data/trained_openclip/negative_logs/dvqa_qa_captions_new_sampled/2024_11_18-16_48_21-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/params.txt b/data/trained_openclip/negative_logs/dvqa_qa_captions_new_sampled/2024_11_18-16_48_21-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/params.txt deleted file mode 100644 index 8c700a2c3bfabff6be5a7a0a5247ca945bd10188..0000000000000000000000000000000000000000 --- a/data/trained_openclip/negative_logs/dvqa_qa_captions_new_sampled/2024_11_18-16_48_21-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/params.txt +++ /dev/null @@ -1,67 +0,0 @@ -batch_size: 64 -beta1: 0.9 -beta2: 0.98 -checkpoint_path: data/trained_openclip/negative_logs/dvqa_qa_captions_new_sampled/2024_11_18-16_48_21-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/checkpoints -copy_codebase: False -csv_caption_key: caption -csv_hard_captions_key: neg_caption -csv_img_key: img_path -csv_separator: , -dataset_resampled: False -dataset_type: csv -ddp_static_graph: False -debug: False -device: cuda:0 -dist_backend: nccl -dist_url: env:// -distributed: True -epochs: 3 -eps: 1e-06 -force_quick_gelu: True -gather_with_grad: False -grad_checkpointing: False -horovod: False -imagenet_v2: None -imagenet_val: None -local_loss: False -local_rank: 0 -lock_image: False -lock_image_freeze_bn_stats: False -lock_image_unlocked_groups: 0 -log_level: 20 -log_local: False -log_path: data/trained_openclip/negative_logs/dvqa_qa_captions_new_sampled/2024_11_18-16_48_21-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/out.log -logs: data/trained_openclip/negative_logs/dvqa_qa_captions_new_sampled -lr: 5e-06 -model: ViT-L-14-336 -name: 2024_11_18-16_48_21-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp -no_set_device_rank: False -norm_gradient_clip: None -precision: amp -pretrained: data/openclip-vit-14-336/openclip_model.pt -pretrained_image: False -rank: 0 -report_to: wandb -resume: None -save_frequency: 1 -save_most_recent: False -seed: 0 -skip_scheduler: False -tensorboard: False -tensorboard_path: -torchscript: False -trace: False -train_data: csv_data/dvqa_qa_captions_new_sampled.csv -train_num_samples: None -use_bn_sync: False -val_data: None -val_frequency: 1 -val_num_samples: None -wandb: True -wandb_notes: -wandb_project: neg-clip-dvqa_qa_captions_new_sampled -warmup: 0 -wd: 0.1 -workers: 4 -world_size: 8 -zeroshot_frequency: 2 diff --git a/data/trained_openclip/negative_logs/dvqa_qa_captions_new_sampled/2024_11_18-16_52_06-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/out.log b/data/trained_openclip/negative_logs/dvqa_qa_captions_new_sampled/2024_11_18-16_52_06-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/out.log deleted file mode 100644 index 3bf3e36ff9246932075b54a2d5f39f08832271b6..0000000000000000000000000000000000000000 --- a/data/trained_openclip/negative_logs/dvqa_qa_captions_new_sampled/2024_11_18-16_52_06-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/out.log +++ /dev/null @@ -1,113 +0,0 @@ -2024-11-18,16:52:06 | INFO | Running in distributed mode with multiple processes. Device: cuda:0.Process (global: 0, local 0), total 8. -2024-11-18,16:52:06 | INFO | Loading ViT-L-14-336 model config. -2024-11-18,16:52:10 | INFO | Loading pretrained ViT-L-14-336 weights (data/openclip-vit-14-336/openclip_model.pt). -2024-11-18,16:52:19 | INFO | Model: -2024-11-18,16:52:19 | INFO | CLIP( - (visual): VisualTransformer( - (conv1): Conv2d(3, 1024, kernel_size=(14, 14), stride=(14, 14), bias=False) - (ln_pre): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) - (transformer): Transformer( - (resblocks): ModuleList( - (0-23): 24 x ResidualAttentionBlock( - (attn): MultiheadAttention( - (out_proj): NonDynamicallyQuantizableLinear(in_features=1024, out_features=1024, bias=True) - ) - (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) - (mlp): Sequential( - (c_fc): Linear(in_features=1024, out_features=4096, bias=True) - (gelu): QuickGELU() - (c_proj): Linear(in_features=4096, out_features=1024, bias=True) - ) - (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) - ) - ) - ) - (ln_post): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) - ) - (transformer): Transformer( - (resblocks): ModuleList( - (0-11): 12 x ResidualAttentionBlock( - (attn): MultiheadAttention( - (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) - ) - (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) - (mlp): Sequential( - (c_fc): Linear(in_features=768, out_features=3072, bias=True) - (gelu): QuickGELU() - (c_proj): Linear(in_features=3072, out_features=768, bias=True) - ) - (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) - ) - ) - ) - (token_embedding): Embedding(49408, 768) - (ln_final): LayerNorm((768,), eps=1e-05, elementwise_affine=True) -) -2024-11-18,16:52:19 | INFO | Params: -2024-11-18,16:52:19 | INFO | batch_size: 64 -2024-11-18,16:52:19 | INFO | beta1: 0.9 -2024-11-18,16:52:19 | INFO | beta2: 0.98 -2024-11-18,16:52:19 | INFO | checkpoint_path: data/trained_openclip/negative_logs/dvqa_qa_captions_new_sampled/2024_11_18-16_52_06-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/checkpoints -2024-11-18,16:52:19 | INFO | copy_codebase: False -2024-11-18,16:52:19 | INFO | csv_caption_key: caption -2024-11-18,16:52:19 | INFO | csv_hard_captions_key: neg_caption -2024-11-18,16:52:19 | INFO | csv_img_key: img_path -2024-11-18,16:52:19 | INFO | csv_separator: , -2024-11-18,16:52:19 | INFO | dataset_resampled: False -2024-11-18,16:52:19 | INFO | dataset_type: csv -2024-11-18,16:52:19 | INFO | ddp_static_graph: False -2024-11-18,16:52:19 | INFO | debug: False -2024-11-18,16:52:19 | INFO | device: cuda:0 -2024-11-18,16:52:19 | INFO | dist_backend: nccl -2024-11-18,16:52:19 | INFO | dist_url: env:// -2024-11-18,16:52:19 | INFO | distributed: True -2024-11-18,16:52:19 | INFO | epochs: 3 -2024-11-18,16:52:19 | INFO | eps: 1e-06 -2024-11-18,16:52:19 | INFO | force_quick_gelu: True -2024-11-18,16:52:19 | INFO | gather_with_grad: False -2024-11-18,16:52:19 | INFO | grad_checkpointing: False -2024-11-18,16:52:19 | INFO | horovod: False -2024-11-18,16:52:19 | INFO | imagenet_v2: None -2024-11-18,16:52:19 | INFO | imagenet_val: None -2024-11-18,16:52:19 | INFO | local_loss: False -2024-11-18,16:52:19 | INFO | local_rank: 0 -2024-11-18,16:52:19 | INFO | lock_image: False -2024-11-18,16:52:19 | INFO | lock_image_freeze_bn_stats: False -2024-11-18,16:52:19 | INFO | lock_image_unlocked_groups: 0 -2024-11-18,16:52:19 | INFO | log_level: 20 -2024-11-18,16:52:19 | INFO | log_local: False -2024-11-18,16:52:19 | INFO | log_path: data/trained_openclip/negative_logs/dvqa_qa_captions_new_sampled/2024_11_18-16_52_06-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/out.log -2024-11-18,16:52:19 | INFO | logs: data/trained_openclip/negative_logs/dvqa_qa_captions_new_sampled -2024-11-18,16:52:19 | INFO | lr: 5e-06 -2024-11-18,16:52:19 | INFO | model: ViT-L-14-336 -2024-11-18,16:52:19 | INFO | name: 2024_11_18-16_52_06-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp -2024-11-18,16:52:19 | INFO | no_set_device_rank: False -2024-11-18,16:52:19 | INFO | norm_gradient_clip: None -2024-11-18,16:52:19 | INFO | precision: amp -2024-11-18,16:52:19 | INFO | pretrained: data/openclip-vit-14-336/openclip_model.pt -2024-11-18,16:52:19 | INFO | pretrained_image: False -2024-11-18,16:52:19 | INFO | rank: 0 -2024-11-18,16:52:19 | INFO | report_to: wandb -2024-11-18,16:52:19 | INFO | resume: None -2024-11-18,16:52:19 | INFO | save_frequency: 1 -2024-11-18,16:52:19 | INFO | save_most_recent: False -2024-11-18,16:52:19 | INFO | seed: 0 -2024-11-18,16:52:19 | INFO | skip_scheduler: False -2024-11-18,16:52:19 | INFO | tensorboard: False -2024-11-18,16:52:19 | INFO | tensorboard_path: -2024-11-18,16:52:19 | INFO | torchscript: False -2024-11-18,16:52:19 | INFO | trace: False -2024-11-18,16:52:19 | INFO | train_data: csv_data/dvqa_qa_captions_new_sampled.csv -2024-11-18,16:52:19 | INFO | train_num_samples: None -2024-11-18,16:52:19 | INFO | use_bn_sync: False -2024-11-18,16:52:19 | INFO | val_data: None -2024-11-18,16:52:19 | INFO | val_frequency: 1 -2024-11-18,16:52:19 | INFO | val_num_samples: None -2024-11-18,16:52:19 | INFO | wandb: True -2024-11-18,16:52:19 | INFO | wandb_notes: -2024-11-18,16:52:19 | INFO | wandb_project: neg-clip-dvqa_qa_captions_new_sampled -2024-11-18,16:52:19 | INFO | warmup: 0 -2024-11-18,16:52:19 | INFO | wd: 0.1 -2024-11-18,16:52:19 | INFO | workers: 4 -2024-11-18,16:52:19 | INFO | world_size: 8 -2024-11-18,16:52:19 | INFO | zeroshot_frequency: 2 diff --git a/data/trained_openclip/negative_logs/dvqa_qa_captions_new_sampled/2024_11_18-16_52_06-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/params.txt b/data/trained_openclip/negative_logs/dvqa_qa_captions_new_sampled/2024_11_18-16_52_06-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/params.txt deleted file mode 100644 index 1607f20c1c9706298b4996a5a42ffd64f433ed29..0000000000000000000000000000000000000000 --- a/data/trained_openclip/negative_logs/dvqa_qa_captions_new_sampled/2024_11_18-16_52_06-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/params.txt +++ /dev/null @@ -1,67 +0,0 @@ -batch_size: 64 -beta1: 0.9 -beta2: 0.98 -checkpoint_path: data/trained_openclip/negative_logs/dvqa_qa_captions_new_sampled/2024_11_18-16_52_06-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/checkpoints -copy_codebase: False -csv_caption_key: caption -csv_hard_captions_key: neg_caption -csv_img_key: img_path -csv_separator: , -dataset_resampled: False -dataset_type: csv -ddp_static_graph: False -debug: False -device: cuda:0 -dist_backend: nccl -dist_url: env:// -distributed: True -epochs: 3 -eps: 1e-06 -force_quick_gelu: True -gather_with_grad: False -grad_checkpointing: False -horovod: False -imagenet_v2: None -imagenet_val: None -local_loss: False -local_rank: 0 -lock_image: False -lock_image_freeze_bn_stats: False -lock_image_unlocked_groups: 0 -log_level: 20 -log_local: False -log_path: data/trained_openclip/negative_logs/dvqa_qa_captions_new_sampled/2024_11_18-16_52_06-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/out.log -logs: data/trained_openclip/negative_logs/dvqa_qa_captions_new_sampled -lr: 5e-06 -model: ViT-L-14-336 -name: 2024_11_18-16_52_06-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp -no_set_device_rank: False -norm_gradient_clip: None -precision: amp -pretrained: data/openclip-vit-14-336/openclip_model.pt -pretrained_image: False -rank: 0 -report_to: wandb -resume: None -save_frequency: 1 -save_most_recent: False -seed: 0 -skip_scheduler: False -tensorboard: False -tensorboard_path: -torchscript: False -trace: False -train_data: csv_data/dvqa_qa_captions_new_sampled.csv -train_num_samples: None -use_bn_sync: False -val_data: None -val_frequency: 1 -val_num_samples: None -wandb: True -wandb_notes: -wandb_project: neg-clip-dvqa_qa_captions_new_sampled -warmup: 0 -wd: 0.1 -workers: 4 -world_size: 8 -zeroshot_frequency: 2 diff --git a/data/trained_openclip/negative_logs/dvqa_qa_captions_new_sampled/2024_11_19-13_26_10-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/out.log b/data/trained_openclip/negative_logs/dvqa_qa_captions_new_sampled/2024_11_19-13_26_10-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/out.log deleted file mode 100644 index 001f817937eb7b8c3ce1ece631271a0be3389d8b..0000000000000000000000000000000000000000 --- a/data/trained_openclip/negative_logs/dvqa_qa_captions_new_sampled/2024_11_19-13_26_10-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/out.log +++ /dev/null @@ -1,114 +0,0 @@ -2024-11-19,13:26:10 | INFO | Running in distributed mode with multiple processes. Device: cuda:0.Process (global: 0, local 0), total 8. -2024-11-19,13:26:10 | INFO | Loading ViT-L-14-336 model config. -2024-11-19,13:26:13 | INFO | Loading pretrained ViT-L-14-336 weights (data/openclip-vit-14-336/openclip_model.pt). -2024-11-19,13:26:20 | INFO | Model: -2024-11-19,13:26:20 | INFO | CLIP( - (visual): VisualTransformer( - (conv1): Conv2d(3, 1024, kernel_size=(14, 14), stride=(14, 14), bias=False) - (ln_pre): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) - (transformer): Transformer( - (resblocks): ModuleList( - (0-23): 24 x ResidualAttentionBlock( - (attn): MultiheadAttention( - (out_proj): NonDynamicallyQuantizableLinear(in_features=1024, out_features=1024, bias=True) - ) - (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) - (mlp): Sequential( - (c_fc): Linear(in_features=1024, out_features=4096, bias=True) - (gelu): QuickGELU() - (c_proj): Linear(in_features=4096, out_features=1024, bias=True) - ) - (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) - ) - ) - ) - (ln_post): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) - ) - (transformer): Transformer( - (resblocks): ModuleList( - (0-11): 12 x ResidualAttentionBlock( - (attn): MultiheadAttention( - (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) - ) - (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) - (mlp): Sequential( - (c_fc): Linear(in_features=768, out_features=3072, bias=True) - (gelu): QuickGELU() - (c_proj): Linear(in_features=3072, out_features=768, bias=True) - ) - (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) - ) - ) - ) - (token_embedding): Embedding(49408, 768) - (ln_final): LayerNorm((768,), eps=1e-05, elementwise_affine=True) -) -2024-11-19,13:26:20 | INFO | Params: -2024-11-19,13:26:20 | INFO | batch_size: 64 -2024-11-19,13:26:20 | INFO | beta1: 0.9 -2024-11-19,13:26:20 | INFO | beta2: 0.98 -2024-11-19,13:26:20 | INFO | checkpoint_path: data/trained_openclip/negative_logs/dvqa_qa_captions_new_sampled/2024_11_19-13_26_10-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/checkpoints -2024-11-19,13:26:20 | INFO | copy_codebase: False -2024-11-19,13:26:20 | INFO | csv_caption_key: caption -2024-11-19,13:26:20 | INFO | csv_hard_captions_key: neg_caption -2024-11-19,13:26:20 | INFO | csv_img_key: img_path -2024-11-19,13:26:20 | INFO | csv_separator: , -2024-11-19,13:26:20 | INFO | dataset_resampled: False -2024-11-19,13:26:20 | INFO | dataset_type: csv -2024-11-19,13:26:20 | INFO | ddp_static_graph: False -2024-11-19,13:26:20 | INFO | debug: False -2024-11-19,13:26:20 | INFO | device: cuda:0 -2024-11-19,13:26:20 | INFO | dist_backend: nccl -2024-11-19,13:26:20 | INFO | dist_url: env:// -2024-11-19,13:26:20 | INFO | distributed: True -2024-11-19,13:26:20 | INFO | epochs: 3 -2024-11-19,13:26:20 | INFO | eps: 1e-06 -2024-11-19,13:26:20 | INFO | force_quick_gelu: True -2024-11-19,13:26:20 | INFO | gather_with_grad: False -2024-11-19,13:26:20 | INFO | grad_checkpointing: False -2024-11-19,13:26:20 | INFO | horovod: False -2024-11-19,13:26:20 | INFO | imagenet_v2: None -2024-11-19,13:26:20 | INFO | imagenet_val: None -2024-11-19,13:26:20 | INFO | local_loss: False -2024-11-19,13:26:20 | INFO | local_rank: 0 -2024-11-19,13:26:20 | INFO | lock_image: False -2024-11-19,13:26:20 | INFO | lock_image_freeze_bn_stats: False -2024-11-19,13:26:20 | INFO | lock_image_unlocked_groups: 0 -2024-11-19,13:26:20 | INFO | log_level: 20 -2024-11-19,13:26:20 | INFO | log_local: False -2024-11-19,13:26:20 | INFO | log_path: data/trained_openclip/negative_logs/dvqa_qa_captions_new_sampled/2024_11_19-13_26_10-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/out.log -2024-11-19,13:26:20 | INFO | logs: data/trained_openclip/negative_logs/dvqa_qa_captions_new_sampled -2024-11-19,13:26:20 | INFO | lr: 5e-06 -2024-11-19,13:26:20 | INFO | model: ViT-L-14-336 -2024-11-19,13:26:20 | INFO | name: 2024_11_19-13_26_10-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp -2024-11-19,13:26:20 | INFO | no_set_device_rank: False -2024-11-19,13:26:20 | INFO | norm_gradient_clip: None -2024-11-19,13:26:20 | INFO | precision: amp -2024-11-19,13:26:20 | INFO | pretrained: data/openclip-vit-14-336/openclip_model.pt -2024-11-19,13:26:20 | INFO | pretrained_image: False -2024-11-19,13:26:20 | INFO | rank: 0 -2024-11-19,13:26:20 | INFO | report_to: wandb -2024-11-19,13:26:20 | INFO | resume: None -2024-11-19,13:26:20 | INFO | save_frequency: 1 -2024-11-19,13:26:20 | INFO | save_most_recent: False -2024-11-19,13:26:20 | INFO | seed: 0 -2024-11-19,13:26:20 | INFO | skip_scheduler: False -2024-11-19,13:26:20 | INFO | tensorboard: False -2024-11-19,13:26:20 | INFO | tensorboard_path: -2024-11-19,13:26:20 | INFO | torchscript: False -2024-11-19,13:26:20 | INFO | trace: False -2024-11-19,13:26:20 | INFO | train_data: csv_data/dvqa_qa_captions_new_sampled.csv -2024-11-19,13:26:20 | INFO | train_num_samples: None -2024-11-19,13:26:20 | INFO | use_bn_sync: False -2024-11-19,13:26:20 | INFO | val_data: None -2024-11-19,13:26:20 | INFO | val_frequency: 1 -2024-11-19,13:26:20 | INFO | val_num_samples: None -2024-11-19,13:26:20 | INFO | wandb: True -2024-11-19,13:26:20 | INFO | wandb_notes: -2024-11-19,13:26:20 | INFO | wandb_project: neg-clip-dvqa_qa_captions_new_sampled -2024-11-19,13:26:20 | INFO | warmup: 0 -2024-11-19,13:26:20 | INFO | wd: 0.1 -2024-11-19,13:26:20 | INFO | workers: 4 -2024-11-19,13:26:20 | INFO | world_size: 8 -2024-11-19,13:26:20 | INFO | zeroshot_frequency: 2 -2024-11-19,13:26:28 | INFO | wrong parsering the python class diff --git a/data/trained_openclip/negative_logs/dvqa_qa_captions_new_sampled/2024_11_19-13_26_10-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/params.txt b/data/trained_openclip/negative_logs/dvqa_qa_captions_new_sampled/2024_11_19-13_26_10-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/params.txt deleted file mode 100644 index c03946a90ccf65e45f143cc89dd42c2e1e9fc93b..0000000000000000000000000000000000000000 --- a/data/trained_openclip/negative_logs/dvqa_qa_captions_new_sampled/2024_11_19-13_26_10-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/params.txt +++ /dev/null @@ -1,67 +0,0 @@ -batch_size: 64 -beta1: 0.9 -beta2: 0.98 -checkpoint_path: data/trained_openclip/negative_logs/dvqa_qa_captions_new_sampled/2024_11_19-13_26_10-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/checkpoints -copy_codebase: False -csv_caption_key: caption -csv_hard_captions_key: neg_caption -csv_img_key: img_path -csv_separator: , -dataset_resampled: False -dataset_type: csv -ddp_static_graph: False -debug: False -device: cuda:0 -dist_backend: nccl -dist_url: env:// -distributed: True -epochs: 3 -eps: 1e-06 -force_quick_gelu: True -gather_with_grad: False -grad_checkpointing: False -horovod: False -imagenet_v2: None -imagenet_val: None -local_loss: False -local_rank: 0 -lock_image: False -lock_image_freeze_bn_stats: False -lock_image_unlocked_groups: 0 -log_level: 20 -log_local: False -log_path: data/trained_openclip/negative_logs/dvqa_qa_captions_new_sampled/2024_11_19-13_26_10-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/out.log -logs: data/trained_openclip/negative_logs/dvqa_qa_captions_new_sampled -lr: 5e-06 -model: ViT-L-14-336 -name: 2024_11_19-13_26_10-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp -no_set_device_rank: False -norm_gradient_clip: None -precision: amp -pretrained: data/openclip-vit-14-336/openclip_model.pt -pretrained_image: False -rank: 0 -report_to: wandb -resume: None -save_frequency: 1 -save_most_recent: False -seed: 0 -skip_scheduler: False -tensorboard: False -tensorboard_path: -torchscript: False -trace: False -train_data: csv_data/dvqa_qa_captions_new_sampled.csv -train_num_samples: None -use_bn_sync: False -val_data: None -val_frequency: 1 -val_num_samples: None -wandb: True -wandb_notes: -wandb_project: neg-clip-dvqa_qa_captions_new_sampled -warmup: 0 -wd: 0.1 -workers: 4 -world_size: 8 -zeroshot_frequency: 2 diff --git a/data/trained_openclip/negative_logs/dvqa_qa_captions_new_sampled/2024_11_19-15_28_36-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/checkpoints/epoch_1.pt b/data/trained_openclip/negative_logs/dvqa_qa_captions_new_sampled/2024_11_19-15_28_36-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/checkpoints/epoch_1.pt deleted file mode 100644 index 8a70d35b3b31b27f5aa307eed6ac626a02886e57..0000000000000000000000000000000000000000 --- a/data/trained_openclip/negative_logs/dvqa_qa_captions_new_sampled/2024_11_19-15_28_36-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/checkpoints/epoch_1.pt +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:1decf3db941e392cdd3b2e3cab3fb44171bede8cf93da26572e4f8d7e791a771 -size 5135890710 diff --git a/data/trained_openclip/negative_logs/dvqa_qa_captions_new_sampled/2024_11_19-15_28_36-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/checkpoints/epoch_2.pt b/data/trained_openclip/negative_logs/dvqa_qa_captions_new_sampled/2024_11_19-15_28_36-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/checkpoints/epoch_2.pt deleted file mode 100644 index 700dcfca8f6291cba550737388f9adbd0841b101..0000000000000000000000000000000000000000 --- a/data/trained_openclip/negative_logs/dvqa_qa_captions_new_sampled/2024_11_19-15_28_36-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/checkpoints/epoch_2.pt +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:ece5ac6ad7d70789e213f8dead83a224ec7a1fddf8740733193e9267c6c6a389 -size 5135890710 diff --git a/data/trained_openclip/negative_logs/dvqa_qa_captions_new_sampled/2024_11_19-15_28_36-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/checkpoints/epoch_3.pt b/data/trained_openclip/negative_logs/dvqa_qa_captions_new_sampled/2024_11_19-15_28_36-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/checkpoints/epoch_3.pt deleted file mode 100644 index 17463a8e25fecf13ab1dd3ffad5ed8d4faae1a4d..0000000000000000000000000000000000000000 --- a/data/trained_openclip/negative_logs/dvqa_qa_captions_new_sampled/2024_11_19-15_28_36-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/checkpoints/epoch_3.pt +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:43655e9d4a232f310e156a8ffc265e3528050162841f73a55e90f86cfa7ce294 -size 5135890710 diff --git a/data/trained_openclip/negative_logs/dvqa_qa_captions_new_sampled/2024_11_19-15_28_36-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/out.log b/data/trained_openclip/negative_logs/dvqa_qa_captions_new_sampled/2024_11_19-15_28_36-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/out.log deleted file mode 100644 index 640a0528b44d228dce1625cd1db09a6798a7fc93..0000000000000000000000000000000000000000 --- a/data/trained_openclip/negative_logs/dvqa_qa_captions_new_sampled/2024_11_19-15_28_36-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/out.log +++ /dev/null @@ -1,240 +0,0 @@ -2024-11-19,15:28:36 | INFO | Running in distributed mode with multiple processes. Device: cuda:0.Process (global: 0, local 0), total 8. -2024-11-19,15:28:36 | INFO | Loading ViT-L-14-336 model config. -2024-11-19,15:28:39 | INFO | Loading pretrained ViT-L-14-336 weights (data/openclip-vit-14-336/openclip_model.pt). -2024-11-19,15:28:47 | INFO | Model: -2024-11-19,15:28:47 | INFO | CLIP( - (visual): VisualTransformer( - (conv1): Conv2d(3, 1024, kernel_size=(14, 14), stride=(14, 14), bias=False) - (ln_pre): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) - (transformer): Transformer( - (resblocks): ModuleList( - (0-23): 24 x ResidualAttentionBlock( - (attn): MultiheadAttention( - (out_proj): NonDynamicallyQuantizableLinear(in_features=1024, out_features=1024, bias=True) - ) - (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) - (mlp): Sequential( - (c_fc): Linear(in_features=1024, out_features=4096, bias=True) - (gelu): QuickGELU() - (c_proj): Linear(in_features=4096, out_features=1024, bias=True) - ) - (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) - ) - ) - ) - (ln_post): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) - ) - (transformer): Transformer( - (resblocks): ModuleList( - (0-11): 12 x ResidualAttentionBlock( - (attn): MultiheadAttention( - (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) - ) - (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) - (mlp): Sequential( - (c_fc): Linear(in_features=768, out_features=3072, bias=True) - (gelu): QuickGELU() - (c_proj): Linear(in_features=3072, out_features=768, bias=True) - ) - (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) - ) - ) - ) - (token_embedding): Embedding(49408, 768) - (ln_final): LayerNorm((768,), eps=1e-05, elementwise_affine=True) -) -2024-11-19,15:28:47 | INFO | Params: -2024-11-19,15:28:47 | INFO | batch_size: 64 -2024-11-19,15:28:47 | INFO | beta1: 0.9 -2024-11-19,15:28:47 | INFO | beta2: 0.98 -2024-11-19,15:28:47 | INFO | checkpoint_path: data/trained_openclip/negative_logs/dvqa_qa_captions_new_sampled/2024_11_19-15_28_36-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/checkpoints -2024-11-19,15:28:47 | INFO | copy_codebase: False -2024-11-19,15:28:47 | INFO | csv_caption_key: caption -2024-11-19,15:28:47 | INFO | csv_hard_captions_key: neg_caption -2024-11-19,15:28:47 | INFO | csv_img_key: img_path -2024-11-19,15:28:47 | INFO | csv_separator: , -2024-11-19,15:28:47 | INFO | dataset_resampled: False -2024-11-19,15:28:47 | INFO | dataset_type: csv -2024-11-19,15:28:47 | INFO | ddp_static_graph: False -2024-11-19,15:28:47 | INFO | debug: False -2024-11-19,15:28:47 | INFO | device: cuda:0 -2024-11-19,15:28:47 | INFO | dist_backend: nccl -2024-11-19,15:28:47 | INFO | dist_url: env:// -2024-11-19,15:28:47 | INFO | distributed: True -2024-11-19,15:28:47 | INFO | epochs: 3 -2024-11-19,15:28:47 | INFO | eps: 1e-06 -2024-11-19,15:28:47 | INFO | force_quick_gelu: True -2024-11-19,15:28:47 | INFO | gather_with_grad: False -2024-11-19,15:28:47 | INFO | grad_checkpointing: False -2024-11-19,15:28:47 | INFO | horovod: False -2024-11-19,15:28:47 | INFO | imagenet_v2: None -2024-11-19,15:28:47 | INFO | imagenet_val: None -2024-11-19,15:28:47 | INFO | local_loss: False -2024-11-19,15:28:47 | INFO | local_rank: 0 -2024-11-19,15:28:47 | INFO | lock_image: False -2024-11-19,15:28:47 | INFO | lock_image_freeze_bn_stats: False -2024-11-19,15:28:47 | INFO | lock_image_unlocked_groups: 0 -2024-11-19,15:28:47 | INFO | log_level: 20 -2024-11-19,15:28:47 | INFO | log_local: False -2024-11-19,15:28:47 | INFO | log_path: data/trained_openclip/negative_logs/dvqa_qa_captions_new_sampled/2024_11_19-15_28_36-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/out.log -2024-11-19,15:28:47 | INFO | logs: data/trained_openclip/negative_logs/dvqa_qa_captions_new_sampled -2024-11-19,15:28:47 | INFO | lr: 5e-06 -2024-11-19,15:28:47 | INFO | model: ViT-L-14-336 -2024-11-19,15:28:47 | INFO | name: 2024_11_19-15_28_36-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp -2024-11-19,15:28:47 | INFO | no_set_device_rank: False -2024-11-19,15:28:47 | INFO | norm_gradient_clip: None -2024-11-19,15:28:47 | INFO | precision: amp -2024-11-19,15:28:47 | INFO | pretrained: data/openclip-vit-14-336/openclip_model.pt -2024-11-19,15:28:47 | INFO | pretrained_image: False -2024-11-19,15:28:47 | INFO | rank: 0 -2024-11-19,15:28:47 | INFO | report_to: wandb -2024-11-19,15:28:47 | INFO | resume: None -2024-11-19,15:28:47 | INFO | save_frequency: 1 -2024-11-19,15:28:47 | INFO | save_most_recent: False -2024-11-19,15:28:47 | INFO | seed: 0 -2024-11-19,15:28:47 | INFO | skip_scheduler: False -2024-11-19,15:28:47 | INFO | tensorboard: False -2024-11-19,15:28:47 | INFO | tensorboard_path: -2024-11-19,15:28:47 | INFO | torchscript: False -2024-11-19,15:28:47 | INFO | trace: False -2024-11-19,15:28:47 | INFO | train_data: csv_data/dvqa_qa_captions_new_sampled.csv -2024-11-19,15:28:47 | INFO | train_num_samples: None -2024-11-19,15:28:47 | INFO | use_bn_sync: False -2024-11-19,15:28:47 | INFO | val_data: None -2024-11-19,15:28:47 | INFO | val_frequency: 1 -2024-11-19,15:28:47 | INFO | val_num_samples: None -2024-11-19,15:28:47 | INFO | wandb: True -2024-11-19,15:28:47 | INFO | wandb_notes: -2024-11-19,15:28:47 | INFO | wandb_project: neg-clip-dvqa_qa_captions_new_sampled -2024-11-19,15:28:47 | INFO | warmup: 0 -2024-11-19,15:28:47 | INFO | wd: 0.1 -2024-11-19,15:28:47 | INFO | workers: 4 -2024-11-19,15:28:47 | INFO | world_size: 8 -2024-11-19,15:28:47 | INFO | zeroshot_frequency: 2 -2024-11-19,15:28:56 | INFO | Init a wandb project! -2024-11-19,15:29:01 | INFO | Start epoch 0 -2024-11-19,15:29:05 | INFO | Train Epoch: 0 [ 512/2000000 (0%)] Loss: 5.9957 (5.996) Data (t): 1.197 Batch (t): 4.650, 110.096/s LR: 0.000005 Logit Scale: 100.000 - V4 -2024-11-19,15:30:36 | INFO | Train Epoch: 0 [ 51712/2000000 (3%)] Loss: 2.7877 (4.392) Data (t): 0.000 Batch (t): 0.901, 569.369/s LR: 0.000005 Logit Scale: 99.996 - V4 -2024-11-19,15:32:05 | INFO | Train Epoch: 0 [ 102912/2000000 (5%)] Loss: 2.4170 (3.733) Data (t): 0.001 Batch (t): 0.898, 570.934/s LR: 0.000005 Logit Scale: 99.995 - V4 -2024-11-19,15:33:35 | INFO | Train Epoch: 0 [ 154112/2000000 (8%)] Loss: 2.2084 (3.352) Data (t): 0.001 Batch (t): 0.899, 571.294/s LR: 0.000005 Logit Scale: 99.994 - V4 -2024-11-19,15:35:06 | INFO | Train Epoch: 0 [ 205312/2000000 (10%)] Loss: 2.3320 (3.148) Data (t): 0.001 Batch (t): 0.906, 569.565/s LR: 0.000005 Logit Scale: 99.993 - V4 -2024-11-19,15:36:36 | INFO | Train Epoch: 0 [ 256512/2000000 (13%)] Loss: 2.2325 (2.996) Data (t): 0.001 Batch (t): 0.898, 570.133/s LR: 0.000005 Logit Scale: 99.991 - V4 -2024-11-19,15:38:06 | INFO | Train Epoch: 0 [ 307712/2000000 (15%)] Loss: 1.9296 (2.843) Data (t): 0.001 Batch (t): 0.897, 572.652/s LR: 0.000005 Logit Scale: 99.987 - V4 -2024-11-19,15:39:35 | INFO | Train Epoch: 0 [ 358912/2000000 (18%)] Loss: 2.1537 (2.757) Data (t): 0.001 Batch (t): 0.896, 571.429/s LR: 0.000005 Logit Scale: 99.984 - V4 -2024-11-19,15:41:05 | INFO | Train Epoch: 0 [ 410112/2000000 (21%)] Loss: 2.1788 (2.693) Data (t): 0.001 Batch (t): 0.898, 571.671/s LR: 0.000005 Logit Scale: 99.984 - V4 -2024-11-19,15:42:35 | INFO | Train Epoch: 0 [ 461312/2000000 (23%)] Loss: 2.0519 (2.629) Data (t): 0.001 Batch (t): 0.903, 572.804/s LR: 0.000005 Logit Scale: 99.982 - V4 -2024-11-19,15:44:05 | INFO | Train Epoch: 0 [ 512512/2000000 (26%)] Loss: 1.8651 (2.559) Data (t): 0.001 Batch (t): 0.897, 572.202/s LR: 0.000005 Logit Scale: 99.980 - V4 -2024-11-19,15:45:35 | INFO | Train Epoch: 0 [ 563712/2000000 (28%)] Loss: 2.0888 (2.520) Data (t): 0.001 Batch (t): 0.897, 571.065/s LR: 0.000005 Logit Scale: 99.977 - V4 -2024-11-19,15:47:05 | INFO | Train Epoch: 0 [ 614912/2000000 (31%)] Loss: 1.9534 (2.477) Data (t): 0.001 Batch (t): 0.898, 570.971/s LR: 0.000005 Logit Scale: 99.975 - V4 -2024-11-19,15:48:34 | INFO | Train Epoch: 0 [ 666112/2000000 (33%)] Loss: 1.7041 (2.421) Data (t): 0.001 Batch (t): 0.897, 571.105/s LR: 0.000005 Logit Scale: 99.975 - V4 -2024-11-19,15:50:05 | INFO | Train Epoch: 0 [ 717312/2000000 (36%)] Loss: 1.8796 (2.385) Data (t): 0.001 Batch (t): 0.908, 570.693/s LR: 0.000005 Logit Scale: 99.971 - V4 -2024-11-19,15:51:35 | INFO | Train Epoch: 0 [ 768512/2000000 (38%)] Loss: 1.8147 (2.350) Data (t): 0.001 Batch (t): 0.896, 572.011/s LR: 0.000005 Logit Scale: 99.971 - V4 -2024-11-19,15:53:04 | INFO | Train Epoch: 0 [ 819712/2000000 (41%)] Loss: 2.0721 (2.333) Data (t): 0.001 Batch (t): 0.896, 573.465/s LR: 0.000005 Logit Scale: 99.968 - V4 -2024-11-19,15:54:34 | INFO | Train Epoch: 0 [ 870912/2000000 (44%)] Loss: 1.9114 (2.310) Data (t): 0.001 Batch (t): 0.897, 571.653/s LR: 0.000005 Logit Scale: 99.966 - V4 -2024-11-19,15:56:04 | INFO | Train Epoch: 0 [ 922112/2000000 (46%)] Loss: 1.9547 (2.291) Data (t): 0.001 Batch (t): 0.897, 570.605/s LR: 0.000005 Logit Scale: 99.965 - V4 -2024-11-19,15:57:35 | INFO | Train Epoch: 0 [ 973312/2000000 (49%)] Loss: 1.8327 (2.268) Data (t): 0.001 Batch (t): 0.909, 569.498/s LR: 0.000005 Logit Scale: 99.964 - V4 -2024-11-19,15:59:04 | INFO | Train Epoch: 0 [1024512/2000000 (51%)] Loss: 1.9088 (2.251) Data (t): 0.001 Batch (t): 0.898, 567.527/s LR: 0.000005 Logit Scale: 99.964 - V4 -2024-11-19,16:00:34 | INFO | Train Epoch: 0 [1075712/2000000 (54%)] Loss: 1.9298 (2.236) Data (t): 0.001 Batch (t): 0.899, 570.316/s LR: 0.000005 Logit Scale: 99.963 - V4 -2024-11-19,16:02:04 | INFO | Train Epoch: 0 [1126912/2000000 (56%)] Loss: 1.7629 (2.216) Data (t): 0.001 Batch (t): 0.898, 570.652/s LR: 0.000005 Logit Scale: 99.963 - V4 -2024-11-19,16:03:34 | INFO | Train Epoch: 0 [1178112/2000000 (59%)] Loss: 1.8551 (2.201) Data (t): 0.001 Batch (t): 0.898, 570.355/s LR: 0.000005 Logit Scale: 99.965 - V4 -2024-11-19,16:05:05 | INFO | Train Epoch: 0 [1229312/2000000 (61%)] Loss: 1.7685 (2.184) Data (t): 0.001 Batch (t): 0.909, 570.915/s LR: 0.000005 Logit Scale: 99.964 - V4 -2024-11-19,16:06:34 | INFO | Train Epoch: 0 [1280512/2000000 (64%)] Loss: 1.8566 (2.171) Data (t): 0.001 Batch (t): 0.896, 572.136/s LR: 0.000004 Logit Scale: 99.965 - V4 -2024-11-19,16:08:04 | INFO | Train Epoch: 0 [1331712/2000000 (67%)] Loss: 1.9796 (2.164) Data (t): 0.001 Batch (t): 0.897, 568.955/s LR: 0.000004 Logit Scale: 99.964 - V4 -2024-11-19,16:09:34 | INFO | Train Epoch: 0 [1382912/2000000 (69%)] Loss: 1.8832 (2.154) Data (t): 0.001 Batch (t): 0.898, 570.155/s LR: 0.000004 Logit Scale: 99.965 - V4 -2024-11-19,16:11:04 | INFO | Train Epoch: 0 [1434112/2000000 (72%)] Loss: 1.8433 (2.143) Data (t): 0.001 Batch (t): 0.897, 570.615/s LR: 0.000004 Logit Scale: 99.963 - V4 -2024-11-19,16:12:33 | INFO | Train Epoch: 0 [1485312/2000000 (74%)] Loss: 1.8844 (2.135) Data (t): 0.001 Batch (t): 0.899, 567.456/s LR: 0.000004 Logit Scale: 99.964 - V4 -2024-11-19,16:14:04 | INFO | Train Epoch: 0 [1536512/2000000 (77%)] Loss: 1.9147 (2.127) Data (t): 0.001 Batch (t): 0.907, 569.984/s LR: 0.000004 Logit Scale: 99.966 - V4 -2024-11-19,16:15:34 | INFO | Train Epoch: 0 [1587712/2000000 (79%)] Loss: 1.7464 (2.116) Data (t): 0.001 Batch (t): 0.897, 570.759/s LR: 0.000004 Logit Scale: 99.967 - V4 -2024-11-19,16:17:04 | INFO | Train Epoch: 0 [1638912/2000000 (82%)] Loss: 1.8658 (2.108) Data (t): 0.001 Batch (t): 0.898, 569.301/s LR: 0.000004 Logit Scale: 99.968 - V4 -2024-11-19,16:18:33 | INFO | Train Epoch: 0 [1690112/2000000 (85%)] Loss: 1.8141 (2.099) Data (t): 0.001 Batch (t): 0.897, 573.014/s LR: 0.000004 Logit Scale: 99.968 - V4 -2024-11-19,16:20:03 | INFO | Train Epoch: 0 [1741312/2000000 (87%)] Loss: 1.7476 (2.089) Data (t): 0.001 Batch (t): 0.900, 568.969/s LR: 0.000004 Logit Scale: 99.969 - V4 -2024-11-19,16:21:34 | INFO | Train Epoch: 0 [1792512/2000000 (90%)] Loss: 1.7485 (2.080) Data (t): 0.001 Batch (t): 0.909, 569.211/s LR: 0.000004 Logit Scale: 99.969 - V4 -2024-11-19,16:23:04 | INFO | Train Epoch: 0 [1843712/2000000 (92%)] Loss: 1.8207 (2.073) Data (t): 0.001 Batch (t): 0.898, 568.663/s LR: 0.000004 Logit Scale: 99.971 - V4 -2024-11-19,16:24:34 | INFO | Train Epoch: 0 [1894912/2000000 (95%)] Loss: 1.7328 (2.064) Data (t): 0.001 Batch (t): 0.899, 572.522/s LR: 0.000004 Logit Scale: 99.973 - V4 -2024-11-19,16:26:04 | INFO | Train Epoch: 0 [1946112/2000000 (97%)] Loss: 1.5711 (2.051) Data (t): 0.001 Batch (t): 0.899, 570.532/s LR: 0.000004 Logit Scale: 99.975 - V4 -2024-11-19,16:27:34 | INFO | Train Epoch: 0 [1997312/2000000 (100%)] Loss: 1.7178 (2.043) Data (t): 0.001 Batch (t): 0.898, 571.033/s LR: 0.000004 Logit Scale: 99.977 - V4 -2024-11-19,16:27:38 | INFO | Train Epoch: 0 [1999872/2000000 (100%)] Loss: 1.6936 (2.034) Data (t): 0.005 Batch (t): 0.895, 571.122/s LR: 0.000004 Logit Scale: 99.977 - V4 -2024-11-19,16:27:43 | INFO | Start epoch 1 -2024-11-19,16:27:45 | INFO | Train Epoch: 1 [ 512/2000000 (0%)] Loss: 1.8669 (1.867) Data (t): 1.031 Batch (t): 1.915, 267.326/s LR: 0.000004 Logit Scale: 99.977 - V4 -2024-11-19,16:29:16 | INFO | Train Epoch: 1 [ 51712/2000000 (3%)] Loss: 1.6626 (1.765) Data (t): 0.001 Batch (t): 0.907, 570.171/s LR: 0.000004 Logit Scale: 99.980 - V4 -2024-11-19,16:30:46 | INFO | Train Epoch: 1 [ 102912/2000000 (5%)] Loss: 1.5746 (1.701) Data (t): 0.001 Batch (t): 0.898, 569.641/s LR: 0.000004 Logit Scale: 99.982 - V4 -2024-11-19,16:32:15 | INFO | Train Epoch: 1 [ 154112/2000000 (8%)] Loss: 1.8032 (1.727) Data (t): 0.001 Batch (t): 0.898, 568.967/s LR: 0.000004 Logit Scale: 99.984 - V4 -2024-11-19,16:33:45 | INFO | Train Epoch: 1 [ 205312/2000000 (10%)] Loss: 1.7087 (1.723) Data (t): 0.001 Batch (t): 0.898, 571.700/s LR: 0.000004 Logit Scale: 99.988 - V4 -2024-11-19,16:35:15 | INFO | Train Epoch: 1 [ 256512/2000000 (13%)] Loss: 1.6652 (1.714) Data (t): 0.001 Batch (t): 0.901, 569.765/s LR: 0.000003 Logit Scale: 99.991 - V4 -2024-11-19,16:36:46 | INFO | Train Epoch: 1 [ 307712/2000000 (15%)] Loss: 1.6190 (1.700) Data (t): 0.001 Batch (t): 0.905, 569.930/s LR: 0.000003 Logit Scale: 99.994 - V4 -2024-11-19,16:38:16 | INFO | Train Epoch: 1 [ 358912/2000000 (18%)] Loss: 1.8352 (1.717) Data (t): 0.001 Batch (t): 0.898, 568.760/s LR: 0.000003 Logit Scale: 99.996 - V4 -2024-11-19,16:39:45 | INFO | Train Epoch: 1 [ 410112/2000000 (21%)] Loss: 1.6636 (1.711) Data (t): 0.001 Batch (t): 0.898, 573.033/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-19,16:41:15 | INFO | Train Epoch: 1 [ 461312/2000000 (23%)] Loss: 1.6142 (1.701) Data (t): 0.001 Batch (t): 0.897, 571.555/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-19,16:42:45 | INFO | Train Epoch: 1 [ 512512/2000000 (26%)] Loss: 1.6758 (1.699) Data (t): 0.001 Batch (t): 0.898, 569.545/s LR: 0.000003 Logit Scale: 99.999 - V4 -2024-11-19,16:44:15 | INFO | Train Epoch: 1 [ 563712/2000000 (28%)] Loss: 1.7603 (1.704) Data (t): 0.001 Batch (t): 0.904, 568.421/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-19,16:45:45 | INFO | Train Epoch: 1 [ 614912/2000000 (31%)] Loss: 1.6037 (1.696) Data (t): 0.001 Batch (t): 0.898, 570.934/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-19,16:47:15 | INFO | Train Epoch: 1 [ 666112/2000000 (33%)] Loss: 1.7349 (1.699) Data (t): 0.001 Batch (t): 0.897, 569.741/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-19,16:48:44 | INFO | Train Epoch: 1 [ 717312/2000000 (36%)] Loss: 1.7195 (1.700) Data (t): 0.001 Batch (t): 0.897, 567.906/s LR: 0.000003 Logit Scale: 99.999 - V4 -2024-11-19,16:50:14 | INFO | Train Epoch: 1 [ 768512/2000000 (38%)] Loss: 1.7906 (1.706) Data (t): 0.001 Batch (t): 0.898, 570.482/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-19,16:51:44 | INFO | Train Epoch: 1 [ 819712/2000000 (41%)] Loss: 1.6468 (1.703) Data (t): 0.001 Batch (t): 0.898, 570.196/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-19,16:53:14 | INFO | Train Epoch: 1 [ 870912/2000000 (44%)] Loss: 1.8331 (1.710) Data (t): 0.001 Batch (t): 0.903, 571.648/s LR: 0.000003 Logit Scale: 99.999 - V4 -2024-11-19,16:54:44 | INFO | Train Epoch: 1 [ 922112/2000000 (46%)] Loss: 1.6685 (1.708) Data (t): 0.001 Batch (t): 0.896, 571.512/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-19,16:56:14 | INFO | Train Epoch: 1 [ 973312/2000000 (49%)] Loss: 1.6768 (1.706) Data (t): 0.001 Batch (t): 0.896, 573.670/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-19,16:57:43 | INFO | Train Epoch: 1 [1024512/2000000 (51%)] Loss: 1.7772 (1.710) Data (t): 0.001 Batch (t): 0.897, 570.219/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-19,16:59:13 | INFO | Train Epoch: 1 [1075712/2000000 (54%)] Loss: 1.4077 (1.696) Data (t): 0.001 Batch (t): 0.899, 570.241/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-19,17:00:44 | INFO | Train Epoch: 1 [1126912/2000000 (56%)] Loss: 1.8494 (1.702) Data (t): 0.001 Batch (t): 0.905, 568.207/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-19,17:02:14 | INFO | Train Epoch: 1 [1178112/2000000 (59%)] Loss: 1.8079 (1.707) Data (t): 0.001 Batch (t): 0.899, 569.389/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-19,17:03:43 | INFO | Train Epoch: 1 [1229312/2000000 (61%)] Loss: 1.5961 (1.702) Data (t): 0.001 Batch (t): 0.899, 568.032/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-19,17:05:13 | INFO | Train Epoch: 1 [1280512/2000000 (64%)] Loss: 1.5522 (1.697) Data (t): 0.001 Batch (t): 0.897, 570.287/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-19,17:06:43 | INFO | Train Epoch: 1 [1331712/2000000 (67%)] Loss: 1.5550 (1.691) Data (t): 0.001 Batch (t): 0.899, 571.472/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-19,17:08:14 | INFO | Train Epoch: 1 [1382912/2000000 (69%)] Loss: 1.7001 (1.692) Data (t): 0.001 Batch (t): 0.905, 570.941/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-19,17:09:43 | INFO | Train Epoch: 1 [1434112/2000000 (72%)] Loss: 1.7038 (1.692) Data (t): 0.001 Batch (t): 0.898, 573.978/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-19,17:11:13 | INFO | Train Epoch: 1 [1485312/2000000 (74%)] Loss: 1.6293 (1.690) Data (t): 0.001 Batch (t): 0.896, 573.044/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-19,17:12:43 | INFO | Train Epoch: 1 [1536512/2000000 (77%)] Loss: 1.5268 (1.685) Data (t): 0.001 Batch (t): 0.897, 572.830/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-19,17:14:12 | INFO | Train Epoch: 1 [1587712/2000000 (79%)] Loss: 1.5464 (1.680) Data (t): 0.001 Batch (t): 0.896, 568.953/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-19,17:15:43 | INFO | Train Epoch: 1 [1638912/2000000 (82%)] Loss: 1.5259 (1.676) Data (t): 0.001 Batch (t): 0.907, 571.760/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-19,17:17:13 | INFO | Train Epoch: 1 [1690112/2000000 (85%)] Loss: 1.4916 (1.670) Data (t): 0.001 Batch (t): 0.898, 571.271/s LR: 0.000002 Logit Scale: 99.999 - V4 -2024-11-19,17:18:42 | INFO | Train Epoch: 1 [1741312/2000000 (87%)] Loss: 1.8018 (1.674) Data (t): 0.001 Batch (t): 0.897, 569.211/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-19,17:20:12 | INFO | Train Epoch: 1 [1792512/2000000 (90%)] Loss: 1.7321 (1.676) Data (t): 0.001 Batch (t): 0.898, 572.494/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-19,17:21:42 | INFO | Train Epoch: 1 [1843712/2000000 (92%)] Loss: 1.7267 (1.677) Data (t): 0.001 Batch (t): 0.898, 571.106/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-19,17:23:13 | INFO | Train Epoch: 1 [1894912/2000000 (95%)] Loss: 1.5456 (1.674) Data (t): 0.001 Batch (t): 0.907, 569.895/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-19,17:24:42 | INFO | Train Epoch: 1 [1946112/2000000 (97%)] Loss: 1.6294 (1.673) Data (t): 0.001 Batch (t): 0.897, 571.159/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-19,17:26:12 | INFO | Train Epoch: 1 [1997312/2000000 (100%)] Loss: 1.6536 (1.672) Data (t): 0.001 Batch (t): 0.897, 570.040/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-19,17:26:17 | INFO | Train Epoch: 1 [1999872/2000000 (100%)] Loss: 1.5622 (1.669) Data (t): 0.005 Batch (t): 0.895, 569.903/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-19,17:26:21 | INFO | Start epoch 2 -2024-11-19,17:26:23 | INFO | Train Epoch: 2 [ 512/2000000 (0%)] Loss: 1.7242 (1.724) Data (t): 0.981 Batch (t): 1.872, 273.458/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-19,17:27:53 | INFO | Train Epoch: 2 [ 51712/2000000 (3%)] Loss: 1.7108 (1.717) Data (t): 0.001 Batch (t): 0.899, 571.474/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-19,17:29:23 | INFO | Train Epoch: 2 [ 102912/2000000 (5%)] Loss: 1.5005 (1.645) Data (t): 0.001 Batch (t): 0.900, 572.427/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-19,17:30:53 | INFO | Train Epoch: 2 [ 154112/2000000 (8%)] Loss: 1.5384 (1.618) Data (t): 0.001 Batch (t): 0.902, 569.623/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-19,17:32:23 | INFO | Train Epoch: 2 [ 205312/2000000 (10%)] Loss: 1.5692 (1.609) Data (t): 0.001 Batch (t): 0.900, 573.816/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-19,17:33:53 | INFO | Train Epoch: 2 [ 256512/2000000 (13%)] Loss: 1.6483 (1.615) Data (t): 0.001 Batch (t): 0.897, 572.838/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-19,17:35:23 | INFO | Train Epoch: 2 [ 307712/2000000 (15%)] Loss: 1.7056 (1.628) Data (t): 0.001 Batch (t): 0.895, 570.674/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-19,17:36:52 | INFO | Train Epoch: 2 [ 358912/2000000 (18%)] Loss: 1.5977 (1.624) Data (t): 0.001 Batch (t): 0.896, 570.512/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-19,17:38:22 | INFO | Train Epoch: 2 [ 410112/2000000 (21%)] Loss: 1.5250 (1.613) Data (t): 0.001 Batch (t): 0.898, 572.520/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-19,17:39:52 | INFO | Train Epoch: 2 [ 461312/2000000 (23%)] Loss: 1.5137 (1.603) Data (t): 0.001 Batch (t): 0.903, 571.206/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-19,17:41:22 | INFO | Train Epoch: 2 [ 512512/2000000 (26%)] Loss: 1.6001 (1.603) Data (t): 0.001 Batch (t): 0.897, 571.950/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-19,17:42:52 | INFO | Train Epoch: 2 [ 563712/2000000 (28%)] Loss: 1.6134 (1.604) Data (t): 0.001 Batch (t): 0.896, 570.163/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-19,17:44:21 | INFO | Train Epoch: 2 [ 614912/2000000 (31%)] Loss: 1.7793 (1.617) Data (t): 0.001 Batch (t): 0.896, 569.077/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-19,17:45:51 | INFO | Train Epoch: 2 [ 666112/2000000 (33%)] Loss: 1.6295 (1.618) Data (t): 0.001 Batch (t): 0.899, 571.097/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-19,17:47:21 | INFO | Train Epoch: 2 [ 717312/2000000 (36%)] Loss: 1.6220 (1.619) Data (t): 0.001 Batch (t): 0.904, 570.421/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-19,17:48:51 | INFO | Train Epoch: 2 [ 768512/2000000 (38%)] Loss: 1.6141 (1.618) Data (t): 0.001 Batch (t): 0.899, 568.045/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-19,17:50:21 | INFO | Train Epoch: 2 [ 819712/2000000 (41%)] Loss: 1.5424 (1.614) Data (t): 0.001 Batch (t): 0.900, 568.090/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-19,17:51:51 | INFO | Train Epoch: 2 [ 870912/2000000 (44%)] Loss: 1.6344 (1.615) Data (t): 0.001 Batch (t): 0.899, 570.721/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-19,17:53:21 | INFO | Train Epoch: 2 [ 922112/2000000 (46%)] Loss: 1.5585 (1.612) Data (t): 0.001 Batch (t): 0.901, 570.929/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-19,17:54:52 | INFO | Train Epoch: 2 [ 973312/2000000 (49%)] Loss: 1.6504 (1.614) Data (t): 0.001 Batch (t): 0.905, 569.907/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-19,17:56:22 | INFO | Train Epoch: 2 [1024512/2000000 (51%)] Loss: 1.6103 (1.614) Data (t): 0.001 Batch (t): 0.899, 568.902/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-19,17:57:52 | INFO | Train Epoch: 2 [1075712/2000000 (54%)] Loss: 1.6474 (1.615) Data (t): 0.001 Batch (t): 0.900, 570.905/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-19,17:59:22 | INFO | Train Epoch: 2 [1126912/2000000 (56%)] Loss: 1.4019 (1.606) Data (t): 0.001 Batch (t): 0.899, 569.141/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-19,18:00:52 | INFO | Train Epoch: 2 [1178112/2000000 (59%)] Loss: 1.6659 (1.608) Data (t): 0.001 Batch (t): 0.901, 570.899/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-19,18:02:22 | INFO | Train Epoch: 2 [1229312/2000000 (61%)] Loss: 1.6503 (1.610) Data (t): 0.001 Batch (t): 0.905, 569.803/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-19,18:03:52 | INFO | Train Epoch: 2 [1280512/2000000 (64%)] Loss: 1.5176 (1.607) Data (t): 0.001 Batch (t): 0.899, 572.951/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-19,18:05:22 | INFO | Train Epoch: 2 [1331712/2000000 (67%)] Loss: 1.7743 (1.613) Data (t): 0.001 Batch (t): 0.899, 571.605/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-19,18:06:52 | INFO | Train Epoch: 2 [1382912/2000000 (69%)] Loss: 1.5842 (1.612) Data (t): 0.001 Batch (t): 0.898, 570.921/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-19,18:08:22 | INFO | Train Epoch: 2 [1434112/2000000 (72%)] Loss: 1.7001 (1.615) Data (t): 0.001 Batch (t): 0.898, 568.411/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-19,18:09:52 | INFO | Train Epoch: 2 [1485312/2000000 (74%)] Loss: 1.6202 (1.615) Data (t): 0.001 Batch (t): 0.905, 568.251/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-19,18:11:22 | INFO | Train Epoch: 2 [1536512/2000000 (77%)] Loss: 1.5798 (1.614) Data (t): 0.001 Batch (t): 0.900, 572.671/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-19,18:12:52 | INFO | Train Epoch: 2 [1587712/2000000 (79%)] Loss: 1.5903 (1.613) Data (t): 0.001 Batch (t): 0.897, 570.863/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-19,18:14:22 | INFO | Train Epoch: 2 [1638912/2000000 (82%)] Loss: 1.4327 (1.608) Data (t): 0.001 Batch (t): 0.897, 571.413/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-19,18:15:51 | INFO | Train Epoch: 2 [1690112/2000000 (85%)] Loss: 1.5730 (1.607) Data (t): 0.001 Batch (t): 0.896, 569.513/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-19,18:17:21 | INFO | Train Epoch: 2 [1741312/2000000 (87%)] Loss: 1.6404 (1.608) Data (t): 0.001 Batch (t): 0.900, 567.369/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-19,18:18:52 | INFO | Train Epoch: 2 [1792512/2000000 (90%)] Loss: 1.6068 (1.608) Data (t): 0.001 Batch (t): 0.905, 571.641/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-19,18:20:22 | INFO | Train Epoch: 2 [1843712/2000000 (92%)] Loss: 1.7877 (1.612) Data (t): 0.001 Batch (t): 0.898, 570.876/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-19,18:21:51 | INFO | Train Epoch: 2 [1894912/2000000 (95%)] Loss: 1.5601 (1.611) Data (t): 0.001 Batch (t): 0.898, 567.361/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-19,18:23:21 | INFO | Train Epoch: 2 [1946112/2000000 (97%)] Loss: 1.5902 (1.611) Data (t): 0.001 Batch (t): 0.898, 572.244/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-19,18:24:51 | INFO | Train Epoch: 2 [1997312/2000000 (100%)] Loss: 1.6741 (1.612) Data (t): 0.001 Batch (t): 0.900, 569.954/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-19,18:24:56 | INFO | Train Epoch: 2 [1999872/2000000 (100%)] Loss: 1.6024 (1.612) Data (t): 0.005 Batch (t): 0.896, 569.465/s LR: 0.000000 Logit Scale: 100.000 - V4 diff --git a/data/trained_openclip/negative_logs/dvqa_qa_captions_new_sampled/2024_11_19-15_28_36-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/params.txt b/data/trained_openclip/negative_logs/dvqa_qa_captions_new_sampled/2024_11_19-15_28_36-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/params.txt deleted file mode 100644 index cb2cd2d9597d17444e59477cf97415d1dcf2e7ca..0000000000000000000000000000000000000000 --- a/data/trained_openclip/negative_logs/dvqa_qa_captions_new_sampled/2024_11_19-15_28_36-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/params.txt +++ /dev/null @@ -1,67 +0,0 @@ -batch_size: 64 -beta1: 0.9 -beta2: 0.98 -checkpoint_path: data/trained_openclip/negative_logs/dvqa_qa_captions_new_sampled/2024_11_19-15_28_36-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/checkpoints -copy_codebase: False -csv_caption_key: caption -csv_hard_captions_key: neg_caption -csv_img_key: img_path -csv_separator: , -dataset_resampled: False -dataset_type: csv -ddp_static_graph: False -debug: False -device: cuda:0 -dist_backend: nccl -dist_url: env:// -distributed: True -epochs: 3 -eps: 1e-06 -force_quick_gelu: True -gather_with_grad: False -grad_checkpointing: False -horovod: False -imagenet_v2: None -imagenet_val: None -local_loss: False -local_rank: 0 -lock_image: False -lock_image_freeze_bn_stats: False -lock_image_unlocked_groups: 0 -log_level: 20 -log_local: False -log_path: data/trained_openclip/negative_logs/dvqa_qa_captions_new_sampled/2024_11_19-15_28_36-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/out.log -logs: data/trained_openclip/negative_logs/dvqa_qa_captions_new_sampled -lr: 5e-06 -model: ViT-L-14-336 -name: 2024_11_19-15_28_36-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp -no_set_device_rank: False -norm_gradient_clip: None -precision: amp -pretrained: data/openclip-vit-14-336/openclip_model.pt -pretrained_image: False -rank: 0 -report_to: wandb -resume: None -save_frequency: 1 -save_most_recent: False -seed: 0 -skip_scheduler: 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a/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten/2024_11_26-13_27_22-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/out.log b/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten/2024_11_26-13_27_22-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/out.log deleted file mode 100644 index 750d7de95987308346e0ece406d3bed6e9a51d99..0000000000000000000000000000000000000000 --- a/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten/2024_11_26-13_27_22-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/out.log +++ /dev/null @@ -1,834 +0,0 @@ -2024-11-26,13:27:22 | INFO | Running in distributed mode with multiple processes. Device: cuda:0.Process (global: 0, local 0), total 8. -2024-11-26,13:27:22 | INFO | Loading ViT-L-14-336 model config. -2024-11-26,13:27:25 | INFO | Loading pretrained ViT-L-14-336 weights (data/openclip-vit-14-336/openclip_model.pt). -2024-11-26,13:27:32 | INFO | Model: -2024-11-26,13:27:32 | INFO | CLIP( - (visual): VisualTransformer( - (conv1): Conv2d(3, 1024, kernel_size=(14, 14), stride=(14, 14), bias=False) - (ln_pre): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) - (transformer): Transformer( - (resblocks): ModuleList( - (0-23): 24 x ResidualAttentionBlock( - (attn): MultiheadAttention( - (out_proj): NonDynamicallyQuantizableLinear(in_features=1024, out_features=1024, bias=True) - ) - (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) - (mlp): Sequential( - (c_fc): Linear(in_features=1024, out_features=4096, bias=True) - (gelu): QuickGELU() - (c_proj): Linear(in_features=4096, out_features=1024, bias=True) - ) - (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) - ) - ) - ) - (ln_post): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) - ) - (transformer): Transformer( - (resblocks): ModuleList( - (0-11): 12 x ResidualAttentionBlock( - (attn): MultiheadAttention( - (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) - ) - (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) - (mlp): Sequential( - (c_fc): Linear(in_features=768, out_features=3072, bias=True) - (gelu): QuickGELU() - (c_proj): Linear(in_features=3072, out_features=768, bias=True) - ) - (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) - ) - ) - ) - (token_embedding): Embedding(49408, 768) - (ln_final): LayerNorm((768,), eps=1e-05, elementwise_affine=True) -) -2024-11-26,13:27:32 | INFO | Params: -2024-11-26,13:27:32 | INFO | batch_size: 64 -2024-11-26,13:27:32 | INFO | beta1: 0.9 -2024-11-26,13:27:32 | INFO | beta2: 0.98 -2024-11-26,13:27:32 | INFO | checkpoint_path: data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten/2024_11_26-13_27_22-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/checkpoints -2024-11-26,13:27:32 | INFO | copy_codebase: False -2024-11-26,13:27:32 | INFO | csv_caption_key: caption -2024-11-26,13:27:32 | INFO | csv_hard_captions_key: neg_caption -2024-11-26,13:27:32 | INFO | csv_img_key: img_path -2024-11-26,13:27:32 | INFO | csv_separator: , -2024-11-26,13:27:32 | INFO | dataset_resampled: False -2024-11-26,13:27:32 | INFO | dataset_type: csv -2024-11-26,13:27:32 | INFO | ddp_static_graph: False -2024-11-26,13:27:32 | INFO | debug: False -2024-11-26,13:27:32 | INFO | device: cuda:0 -2024-11-26,13:27:32 | INFO | dist_backend: nccl -2024-11-26,13:27:32 | INFO | dist_url: env:// -2024-11-26,13:27:32 | INFO | distributed: True -2024-11-26,13:27:32 | INFO | epochs: 2 -2024-11-26,13:27:32 | INFO | eps: 1e-06 -2024-11-26,13:27:32 | INFO | force_quick_gelu: True -2024-11-26,13:27:32 | INFO | gather_with_grad: False -2024-11-26,13:27:32 | INFO | grad_checkpointing: False -2024-11-26,13:27:32 | INFO | horovod: False -2024-11-26,13:27:32 | INFO | imagenet_v2: None -2024-11-26,13:27:32 | INFO | imagenet_val: None -2024-11-26,13:27:32 | INFO | local_loss: False -2024-11-26,13:27:32 | INFO | local_rank: 0 -2024-11-26,13:27:32 | INFO | lock_image: False -2024-11-26,13:27:32 | INFO | lock_image_freeze_bn_stats: False -2024-11-26,13:27:32 | INFO | lock_image_unlocked_groups: 0 -2024-11-26,13:27:32 | INFO | log_level: 20 -2024-11-26,13:27:32 | INFO | log_local: False -2024-11-26,13:27:32 | INFO | log_path: data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten/2024_11_26-13_27_22-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/out.log -2024-11-26,13:27:32 | INFO | logs: data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten -2024-11-26,13:27:32 | INFO | lr: 1e-06 -2024-11-26,13:27:32 | INFO | model: ViT-L-14-336 -2024-11-26,13:27:32 | INFO | name: 2024_11_26-13_27_22-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp -2024-11-26,13:27:32 | INFO | no_set_device_rank: False -2024-11-26,13:27:32 | INFO | norm_gradient_clip: None -2024-11-26,13:27:32 | INFO | precision: amp -2024-11-26,13:27:32 | INFO | pretrained: data/openclip-vit-14-336/openclip_model.pt -2024-11-26,13:27:32 | INFO | pretrained_image: False -2024-11-26,13:27:32 | INFO | rank: 0 -2024-11-26,13:27:32 | INFO | report_to: wandb -2024-11-26,13:27:32 | INFO | resume: None -2024-11-26,13:27:32 | INFO | save_frequency: 1 -2024-11-26,13:27:32 | INFO | save_most_recent: False -2024-11-26,13:27:32 | INFO | seed: 0 -2024-11-26,13:27:32 | INFO | skip_scheduler: False -2024-11-26,13:27:32 | INFO | tensorboard: False -2024-11-26,13:27:32 | INFO | tensorboard_path: -2024-11-26,13:27:32 | INFO | torchscript: False -2024-11-26,13:27:32 | INFO | trace: False -2024-11-26,13:27:32 | INFO | train_data: csv_data/plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten.csv -2024-11-26,13:27:32 | INFO | train_num_samples: None -2024-11-26,13:27:32 | INFO | use_bn_sync: False -2024-11-26,13:27:32 | INFO | val_data: None -2024-11-26,13:27:32 | INFO | val_frequency: 1 -2024-11-26,13:27:32 | INFO | val_num_samples: None -2024-11-26,13:27:32 | INFO | wandb: True -2024-11-26,13:27:32 | INFO | wandb_notes: -2024-11-26,13:27:32 | INFO | wandb_project: neg-clip-plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten -2024-11-26,13:27:32 | INFO | warmup: 0 -2024-11-26,13:27:32 | INFO | wd: 0.1 -2024-11-26,13:27:32 | INFO | workers: 4 -2024-11-26,13:27:32 | INFO | world_size: 8 -2024-11-26,13:27:32 | INFO | zeroshot_frequency: 2 -2024-11-26,13:29:13 | INFO | Init a wandb project! -2024-11-26,13:29:20 | INFO | Start epoch 0 -2024-11-26,13:29:28 | INFO | Train Epoch: 0 [ 512/18327966 (0%)] Loss: 5.0808 (5.081) Data (t): 3.456 Batch (t): 7.927, 64.5895/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,13:30:59 | INFO | Train Epoch: 0 [ 51712/18327966 (0%)] Loss: 2.2232 (3.652) Data (t): 0.001 Batch (t): 0.913, 568.220/s LR: 0.000001 Logit Scale: 99.997 - V4 -2024-11-26,13:32:29 | INFO | Train Epoch: 0 [ 102912/18327966 (1%)] Loss: 1.7186 (3.008) Data (t): 0.001 Batch (t): 0.904, 567.185/s LR: 0.000001 Logit Scale: 99.997 - V4 -2024-11-26,13:34:01 | INFO | Train Epoch: 0 [ 154112/18327966 (1%)] Loss: 1.8533 (2.719) Data (t): 0.001 Batch (t): 0.915, 568.843/s LR: 0.000001 Logit Scale: 99.998 - V4 -2024-11-26,13:35:37 | INFO | Train Epoch: 0 [ 205312/18327966 (1%)] Loss: 1.5638 (2.488) Data (t): 0.001 Batch (t): 0.961, 568.688/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,13:37:07 | INFO | Train Epoch: 0 [ 256512/18327966 (1%)] Loss: 1.4146 (2.309) Data (t): 0.001 Batch (t): 0.902, 564.758/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,13:38:37 | INFO | Train Epoch: 0 [ 307712/18327966 (2%)] Loss: 1.2489 (2.158) Data (t): 0.001 Batch (t): 0.902, 570.425/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,13:40:08 | INFO | Train Epoch: 0 [ 358912/18327966 (2%)] Loss: 1.4936 (2.075) Data (t): 0.001 Batch (t): 0.904, 569.734/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,13:41:39 | INFO | Train Epoch: 0 [ 410112/18327966 (2%)] Loss: 1.2398 (1.982) Data (t): 0.001 Batch (t): 0.914, 565.858/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,13:43:16 | INFO | Train Epoch: 0 [ 461312/18327966 (3%)] Loss: 1.1007 (1.894) Data (t): 0.001 Batch (t): 0.967, 569.762/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,13:44:46 | INFO | Train Epoch: 0 [ 512512/18327966 (3%)] Loss: 1.1115 (1.823) Data (t): 0.001 Batch (t): 0.904, 565.018/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,13:46:17 | INFO | Train Epoch: 0 [ 563712/18327966 (3%)] Loss: 1.2578 (1.776) Data (t): 0.001 Batch (t): 0.903, 568.308/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,13:47:47 | INFO | Train Epoch: 0 [ 614912/18327966 (3%)] Loss: 1.3479 (1.743) Data (t): 0.001 Batch (t): 0.903, 569.526/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,13:49:17 | INFO | Train Epoch: 0 [ 666112/18327966 (4%)] Loss: 1.2537 (1.708) Data (t): 0.001 Batch (t): 0.901, 569.274/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,13:50:54 | INFO | Train Epoch: 0 [ 717312/18327966 (4%)] Loss: 1.1505 (1.671) Data (t): 0.001 Batch (t): 0.968, 570.095/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,13:52:24 | INFO | Train Epoch: 0 [ 768512/18327966 (4%)] Loss: 1.1517 (1.638) Data (t): 0.001 Batch (t): 0.903, 567.358/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,13:53:55 | INFO | Train Epoch: 0 [ 819712/18327966 (4%)] Loss: 1.3307 (1.620) Data (t): 0.001 Batch (t): 0.905, 568.658/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,13:55:25 | INFO | Train Epoch: 0 [ 870912/18327966 (5%)] Loss: 1.3169 (1.603) Data (t): 0.001 Batch (t): 0.903, 563.334/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,13:56:55 | INFO | Train Epoch: 0 [ 922112/18327966 (5%)] Loss: 1.1250 (1.578) Data (t): 0.001 Batch (t): 0.904, 568.191/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,13:58:33 | INFO | Train Epoch: 0 [ 973312/18327966 (5%)] Loss: 1.1192 (1.555) Data (t): 0.001 Batch (t): 0.979, 570.500/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:00:04 | INFO | Train Epoch: 0 [ 1024512/18327966 (6%)] Loss: 1.0946 (1.533) Data (t): 0.001 Batch (t): 0.903, 570.201/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:01:34 | INFO | Train Epoch: 0 [ 1075712/18327966 (6%)] Loss: 1.0231 (1.510) Data (t): 0.001 Batch (t): 0.904, 567.005/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:03:04 | INFO | Train Epoch: 0 [ 1126912/18327966 (6%)] Loss: 0.99849 (1.488) Data (t): 0.001 Batch (t): 0.904, 566.798/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:04:35 | INFO | Train Epoch: 0 [ 1178112/18327966 (6%)] Loss: 1.1386 (1.473) Data (t): 0.001 Batch (t): 0.904, 566.882/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:06:11 | INFO | Train Epoch: 0 [ 1229312/18327966 (7%)] Loss: 1.1964 (1.462) Data (t): 0.001 Batch (t): 0.959, 565.375/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:07:44 | INFO | Train Epoch: 0 [ 1280512/18327966 (7%)] Loss: 1.0062 (1.445) Data (t): 0.001 Batch (t): 0.935, 566.609/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:09:15 | INFO | Train Epoch: 0 [ 1331712/18327966 (7%)] Loss: 1.1137 (1.432) Data (t): 0.001 Batch (t): 0.904, 568.105/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:10:45 | INFO | Train Epoch: 0 [ 1382912/18327966 (8%)] Loss: 1.1807 (1.423) Data (t): 0.001 Batch (t): 0.904, 565.325/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:12:15 | INFO | Train Epoch: 0 [ 1434112/18327966 (8%)] Loss: 0.98085 (1.408) Data (t): 0.001 Batch (t): 0.904, 566.357/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:13:47 | INFO | Train Epoch: 0 [ 1485312/18327966 (8%)] Loss: 1.0586 (1.396) Data (t): 0.001 Batch (t): 0.915, 564.882/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:15:25 | INFO | Train Epoch: 0 [ 1536512/18327966 (8%)] Loss: 1.0545 (1.385) Data (t): 0.001 Batch (t): 0.980, 569.280/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:16:55 | INFO | Train Epoch: 0 [ 1587712/18327966 (9%)] Loss: 0.99654 (1.373) Data (t): 0.001 Batch (t): 0.903, 565.158/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:18:25 | INFO | Train Epoch: 0 [ 1638912/18327966 (9%)] Loss: 1.1521 (1.367) Data (t): 0.001 Batch (t): 0.903, 561.091/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:19:56 | INFO | Train Epoch: 0 [ 1690112/18327966 (9%)] Loss: 1.0141 (1.356) Data (t): 0.001 Batch (t): 0.904, 563.313/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:21:27 | INFO | Train Epoch: 0 [ 1741312/18327966 (10%)] Loss: 1.1415 (1.350) Data (t): 0.001 Batch (t): 0.913, 567.304/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:23:05 | INFO | Train Epoch: 0 [ 1792512/18327966 (10%)] Loss: 1.0203 (1.341) Data (t): 0.001 Batch (t): 0.978, 566.819/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:24:35 | INFO | Train Epoch: 0 [ 1843712/18327966 (10%)] Loss: 1.0723 (1.334) Data (t): 0.001 Batch (t): 0.902, 570.546/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:26:05 | INFO | Train Epoch: 0 [ 1894912/18327966 (10%)] Loss: 1.0193 (1.325) Data (t): 0.001 Batch (t): 0.902, 569.548/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:27:36 | INFO | Train Epoch: 0 [ 1946112/18327966 (11%)] Loss: 0.98830 (1.317) Data (t): 0.001 Batch (t): 0.903, 564.744/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:29:06 | INFO | Train Epoch: 0 [ 1997312/18327966 (11%)] Loss: 1.0640 (1.310) Data (t): 0.001 Batch (t): 0.902, 564.573/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:30:44 | INFO | Train Epoch: 0 [ 2048512/18327966 (11%)] Loss: 0.87952 (1.300) Data (t): 0.001 Batch (t): 0.979, 568.761/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:32:14 | INFO | Train Epoch: 0 [ 2099712/18327966 (11%)] Loss: 1.0411 (1.294) Data (t): 0.001 Batch (t): 0.903, 567.035/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:33:44 | INFO | Train Epoch: 0 [ 2150912/18327966 (12%)] Loss: 0.90820 (1.285) Data (t): 0.001 Batch (t): 0.903, 566.615/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:35:15 | INFO | Train Epoch: 0 [ 2202112/18327966 (12%)] Loss: 0.94015 (1.277) Data (t): 0.001 Batch (t): 0.902, 566.885/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:36:45 | INFO | Train Epoch: 0 [ 2253312/18327966 (12%)] Loss: 1.0241 (1.271) Data (t): 0.001 Batch (t): 0.904, 567.921/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:38:24 | INFO | Train Epoch: 0 [ 2304512/18327966 (13%)] Loss: 0.99887 (1.265) Data (t): 0.001 Batch (t): 0.989, 564.941/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:39:54 | INFO | Train Epoch: 0 [ 2355712/18327966 (13%)] Loss: 0.94278 (1.259) Data (t): 0.001 Batch (t): 0.902, 570.707/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:41:25 | INFO | Train Epoch: 0 [ 2406912/18327966 (13%)] Loss: 1.0042 (1.253) Data (t): 0.001 Batch (t): 0.903, 568.610/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:42:55 | INFO | Train Epoch: 0 [ 2458112/18327966 (13%)] Loss: 1.1593 (1.251) Data (t): 0.001 Batch (t): 0.902, 568.461/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:44:25 | INFO | Train Epoch: 0 [ 2509312/18327966 (14%)] Loss: 1.0120 (1.247) Data (t): 0.001 Batch (t): 0.903, 565.863/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:46:01 | INFO | Train Epoch: 0 [ 2560512/18327966 (14%)] Loss: 0.97194 (1.241) Data (t): 0.001 Batch (t): 0.957, 567.993/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:47:34 | INFO | Train Epoch: 0 [ 2611712/18327966 (14%)] Loss: 1.0380 (1.237) Data (t): 0.001 Batch (t): 0.935, 567.252/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:49:05 | INFO | Train Epoch: 0 [ 2662912/18327966 (15%)] Loss: 0.91660 (1.231) Data (t): 0.001 Batch (t): 0.903, 565.890/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:50:35 | INFO | Train Epoch: 0 [ 2714112/18327966 (15%)] Loss: 1.0549 (1.228) Data (t): 0.001 Batch (t): 0.903, 567.585/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:52:05 | INFO | Train Epoch: 0 [ 2765312/18327966 (15%)] Loss: 1.0486 (1.225) Data (t): 0.001 Batch (t): 0.903, 565.976/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:53:38 | INFO | Train Epoch: 0 [ 2816512/18327966 (15%)] Loss: 0.98456 (1.220) Data (t): 0.001 Batch (t): 0.925, 256.812/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:55:15 | INFO | Train Epoch: 0 [ 2867712/18327966 (16%)] Loss: 0.92401 (1.215) Data (t): 0.001 Batch (t): 0.969, 568.053/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:56:45 | INFO | Train Epoch: 0 [ 2918912/18327966 (16%)] Loss: 0.99992 (1.211) Data (t): 0.001 Batch (t): 0.904, 567.057/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:58:15 | INFO | Train Epoch: 0 [ 2970112/18327966 (16%)] Loss: 0.99712 (1.208) Data (t): 0.001 Batch (t): 0.902, 566.002/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:59:46 | INFO | Train Epoch: 0 [ 3021312/18327966 (16%)] Loss: 0.84657 (1.202) Data (t): 0.001 Batch (t): 0.903, 568.994/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:01:17 | INFO | Train Epoch: 0 [ 3072512/18327966 (17%)] Loss: 1.0454 (1.199) Data (t): 0.001 Batch (t): 0.914, 566.277/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:02:55 | INFO | Train Epoch: 0 [ 3123712/18327966 (17%)] Loss: 0.99233 (1.196) Data (t): 0.001 Batch (t): 0.979, 566.830/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:04:25 | INFO | Train Epoch: 0 [ 3174912/18327966 (17%)] Loss: 0.86858 (1.191) Data (t): 0.001 Batch (t): 0.903, 568.456/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:05:55 | INFO | Train Epoch: 0 [ 3226112/18327966 (18%)] Loss: 0.87988 (1.186) Data (t): 0.001 Batch (t): 0.903, 567.926/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:07:26 | INFO | Train Epoch: 0 [ 3277312/18327966 (18%)] Loss: 0.87524 (1.181) Data (t): 0.001 Batch (t): 0.904, 568.999/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:08:56 | INFO | Train Epoch: 0 [ 3328512/18327966 (18%)] Loss: 0.88903 (1.177) Data (t): 0.001 Batch (t): 0.904, 566.863/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:10:35 | INFO | Train Epoch: 0 [ 3379712/18327966 (18%)] Loss: 1.0589 (1.175) Data (t): 0.001 Batch (t): 0.990, 569.427/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:12:06 | INFO | Train Epoch: 0 [ 3430912/18327966 (19%)] Loss: 0.97689 (1.172) Data (t): 0.001 Batch (t): 0.904, 567.235/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:13:36 | INFO | Train Epoch: 0 [ 3482112/18327966 (19%)] Loss: 1.0643 (1.170) Data (t): 0.001 Batch (t): 0.904, 568.726/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:15:06 | INFO | Train Epoch: 0 [ 3533312/18327966 (19%)] Loss: 1.0316 (1.168) Data (t): 0.001 Batch (t): 0.904, 563.075/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:16:37 | INFO | Train Epoch: 0 [ 3584512/18327966 (20%)] Loss: 1.0142 (1.166) Data (t): 0.001 Batch (t): 0.903, 571.147/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:18:13 | INFO | Train Epoch: 0 [ 3635712/18327966 (20%)] Loss: 1.0353 (1.164) Data (t): 0.001 Batch (t): 0.959, 568.510/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:19:46 | INFO | Train Epoch: 0 [ 3686912/18327966 (20%)] Loss: 0.88616 (1.161) Data (t): 0.001 Batch (t): 0.937, 566.594/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:21:17 | INFO | Train Epoch: 0 [ 3738112/18327966 (20%)] Loss: 0.95161 (1.158) Data (t): 0.001 Batch (t): 0.904, 567.377/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:22:47 | INFO | Train Epoch: 0 [ 3789312/18327966 (21%)] Loss: 0.99048 (1.156) Data (t): 0.001 Batch (t): 0.905, 566.423/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:24:17 | INFO | Train Epoch: 0 [ 3840512/18327966 (21%)] Loss: 0.84364 (1.151) Data (t): 0.001 Batch (t): 0.903, 566.363/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:25:53 | INFO | Train Epoch: 0 [ 3891712/18327966 (21%)] Loss: 0.86231 (1.148) Data (t): 0.001 Batch (t): 0.959, 569.432/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:27:27 | INFO | Train Epoch: 0 [ 3942912/18327966 (22%)] Loss: 0.88501 (1.144) Data (t): 0.001 Batch (t): 0.936, 569.846/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:28:57 | INFO | Train Epoch: 0 [ 3994112/18327966 (22%)] Loss: 1.0180 (1.143) Data (t): 0.001 Batch (t): 0.904, 568.817/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:30:28 | INFO | Train Epoch: 0 [ 4045312/18327966 (22%)] Loss: 0.87960 (1.139) Data (t): 0.001 Batch (t): 0.903, 568.325/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:31:58 | INFO | Train Epoch: 0 [ 4096512/18327966 (22%)] Loss: 0.93766 (1.137) Data (t): 0.001 Batch (t): 0.903, 564.962/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:33:31 | INFO | Train Epoch: 0 [ 4147712/18327966 (23%)] Loss: 0.92262 (1.134) Data (t): 0.001 Batch (t): 0.926, 568.132/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:35:08 | INFO | Train Epoch: 0 [ 4198912/18327966 (23%)] Loss: 0.83956 (1.131) Data (t): 0.001 Batch (t): 0.970, 569.688/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:36:38 | INFO | Train Epoch: 0 [ 4250112/18327966 (23%)] Loss: 0.92005 (1.128) Data (t): 0.001 Batch (t): 0.902, 564.030/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:38:08 | INFO | Train Epoch: 0 [ 4301312/18327966 (23%)] Loss: 1.0140 (1.127) Data (t): 0.001 Batch (t): 0.903, 566.441/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:39:39 | INFO | Train Epoch: 0 [ 4352512/18327966 (24%)] Loss: 1.0250 (1.126) Data (t): 0.001 Batch (t): 0.904, 568.316/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:41:10 | INFO | Train Epoch: 0 [ 4403712/18327966 (24%)] Loss: 0.87302 (1.123) Data (t): 0.001 Batch (t): 0.915, 568.730/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:42:47 | INFO | Train Epoch: 0 [ 4454912/18327966 (24%)] Loss: 0.94140 (1.121) Data (t): 0.001 Batch (t): 0.970, 255.378/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:44:17 | INFO | Train Epoch: 0 [ 4506112/18327966 (25%)] Loss: 0.85177 (1.118) Data (t): 0.001 Batch (t): 0.903, 563.354/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:45:48 | INFO | Train Epoch: 0 [ 4557312/18327966 (25%)] Loss: 0.92441 (1.116) Data (t): 0.001 Batch (t): 0.903, 569.596/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:47:18 | INFO | Train Epoch: 0 [ 4608512/18327966 (25%)] Loss: 0.88697 (1.113) Data (t): 0.001 Batch (t): 0.902, 567.370/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:48:48 | INFO | Train Epoch: 0 [ 4659712/18327966 (25%)] Loss: 0.95644 (1.111) Data (t): 0.001 Batch (t): 0.903, 566.041/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:50:25 | INFO | Train Epoch: 0 [ 4710912/18327966 (26%)] Loss: 0.97518 (1.110) Data (t): 0.001 Batch (t): 0.970, 568.892/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:51:59 | INFO | Train Epoch: 0 [ 4762112/18327966 (26%)] Loss: 0.85625 (1.107) Data (t): 0.001 Batch (t): 0.936, 565.973/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:53:29 | INFO | Train Epoch: 0 [ 4813312/18327966 (26%)] Loss: 0.94091 (1.106) Data (t): 0.001 Batch (t): 0.904, 569.043/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:54:59 | INFO | Train Epoch: 0 [ 4864512/18327966 (27%)] Loss: 0.91859 (1.104) Data (t): 0.001 Batch (t): 0.902, 571.337/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:56:30 | INFO | Train Epoch: 0 [ 4915712/18327966 (27%)] Loss: 1.0132 (1.103) Data (t): 0.001 Batch (t): 0.903, 567.469/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:58:06 | INFO | Train Epoch: 0 [ 4966912/18327966 (27%)] Loss: 0.95845 (1.101) Data (t): 0.001 Batch (t): 0.959, 566.520/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:59:39 | INFO | Train Epoch: 0 [ 5018112/18327966 (27%)] Loss: 0.77138 (1.098) Data (t): 0.001 Batch (t): 0.936, 561.021/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:01:10 | INFO | Train Epoch: 0 [ 5069312/18327966 (28%)] Loss: 0.93271 (1.096) Data (t): 0.001 Batch (t): 0.904, 568.403/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:02:40 | INFO | Train Epoch: 0 [ 5120512/18327966 (28%)] Loss: 1.0950 (1.096) Data (t): 0.001 Batch (t): 0.903, 567.669/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:04:10 | INFO | Train Epoch: 0 [ 5171712/18327966 (28%)] Loss: 0.94643 (1.095) Data (t): 0.001 Batch (t): 0.904, 566.812/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:05:45 | INFO | Train Epoch: 0 [ 5222912/18327966 (28%)] Loss: 0.91479 (1.093) Data (t): 0.001 Batch (t): 0.948, 565.777/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:07:19 | INFO | Train Epoch: 0 [ 5274112/18327966 (29%)] Loss: 0.86651 (1.091) Data (t): 0.001 Batch (t): 0.937, 567.446/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:08:49 | INFO | Train Epoch: 0 [ 5325312/18327966 (29%)] Loss: 0.86163 (1.089) Data (t): 0.001 Batch (t): 0.904, 564.906/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:10:20 | INFO | Train Epoch: 0 [ 5376512/18327966 (29%)] Loss: 0.92687 (1.087) Data (t): 0.001 Batch (t): 0.904, 562.760/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:11:50 | INFO | Train Epoch: 0 [ 5427712/18327966 (30%)] Loss: 0.92163 (1.086) Data (t): 0.001 Batch (t): 0.903, 569.078/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:13:21 | INFO | Train Epoch: 0 [ 5478912/18327966 (30%)] Loss: 1.0442 (1.085) Data (t): 0.001 Batch (t): 0.914, 566.750/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:14:58 | INFO | Train Epoch: 0 [ 5530112/18327966 (30%)] Loss: 0.93207 (1.084) Data (t): 0.001 Batch (t): 0.970, 570.528/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:16:30 | INFO | Train Epoch: 0 [ 5581312/18327966 (30%)] Loss: 0.87174 (1.082) Data (t): 0.001 Batch (t): 0.914, 566.747/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:18:00 | INFO | Train Epoch: 0 [ 5632512/18327966 (31%)] Loss: 0.89400 (1.080) Data (t): 0.001 Batch (t): 0.904, 565.757/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:19:30 | INFO | Train Epoch: 0 [ 5683712/18327966 (31%)] Loss: 0.91185 (1.079) Data (t): 0.001 Batch (t): 0.903, 568.491/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:21:01 | INFO | Train Epoch: 0 [ 5734912/18327966 (31%)] Loss: 0.90193 (1.077) Data (t): 0.001 Batch (t): 0.903, 567.988/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:22:38 | INFO | Train Epoch: 0 [ 5786112/18327966 (32%)] Loss: 0.84007 (1.075) Data (t): 0.001 Batch (t): 0.969, 570.058/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:24:11 | INFO | Train Epoch: 0 [ 5837312/18327966 (32%)] Loss: 0.91355 (1.074) Data (t): 0.001 Batch (t): 0.938, 561.552/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:25:42 | INFO | Train Epoch: 0 [ 5888512/18327966 (32%)] Loss: 0.96585 (1.073) Data (t): 0.001 Batch (t): 0.904, 565.158/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:27:12 | INFO | Train Epoch: 0 [ 5939712/18327966 (32%)] Loss: 1.0160 (1.072) Data (t): 0.001 Batch (t): 0.904, 564.363/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:28:43 | INFO | Train Epoch: 0 [ 5990912/18327966 (33%)] Loss: 0.92139 (1.071) Data (t): 0.001 Batch (t): 0.905, 570.757/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:30:19 | INFO | Train Epoch: 0 [ 6042112/18327966 (33%)] Loss: 0.93039 (1.070) Data (t): 0.001 Batch (t): 0.958, 567.230/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:31:53 | INFO | Train Epoch: 0 [ 6093312/18327966 (33%)] Loss: 0.97993 (1.069) Data (t): 0.001 Batch (t): 0.946, 565.568/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:33:24 | INFO | Train Epoch: 0 [ 6144512/18327966 (34%)] Loss: 0.83864 (1.067) Data (t): 0.001 Batch (t): 0.904, 566.917/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:34:54 | INFO | Train Epoch: 0 [ 6195712/18327966 (34%)] Loss: 0.87298 (1.065) Data (t): 0.001 Batch (t): 0.903, 567.097/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:36:24 | INFO | Train Epoch: 0 [ 6246912/18327966 (34%)] Loss: 0.86738 (1.064) Data (t): 0.001 Batch (t): 0.903, 567.570/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:38:00 | INFO | Train Epoch: 0 [ 6298112/18327966 (34%)] Loss: 0.91830 (1.063) Data (t): 0.002 Batch (t): 0.962, 566.805/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:39:35 | INFO | Train Epoch: 0 [ 6349312/18327966 (35%)] Loss: 0.92631 (1.062) Data (t): 0.001 Batch (t): 0.943, 568.038/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:41:05 | INFO | Train Epoch: 0 [ 6400512/18327966 (35%)] Loss: 0.93841 (1.061) Data (t): 0.001 Batch (t): 0.902, 567.845/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:42:35 | INFO | Train Epoch: 0 [ 6451712/18327966 (35%)] Loss: 0.83046 (1.059) Data (t): 0.001 Batch (t): 0.902, 566.721/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:44:06 | INFO | Train Epoch: 0 [ 6502912/18327966 (35%)] Loss: 1.0524 (1.059) Data (t): 0.001 Batch (t): 0.904, 568.903/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:45:41 | INFO | Train Epoch: 0 [ 6554112/18327966 (36%)] Loss: 0.93586 (1.058) Data (t): 0.001 Batch (t): 0.959, 571.576/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:47:15 | INFO | Train Epoch: 0 [ 6605312/18327966 (36%)] Loss: 0.83526 (1.056) Data (t): 0.001 Batch (t): 0.931, 569.726/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:48:46 | INFO | Train Epoch: 0 [ 6656512/18327966 (36%)] Loss: 0.88137 (1.055) Data (t): 0.001 Batch (t): 0.913, 565.912/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:50:16 | INFO | Train Epoch: 0 [ 6707712/18327966 (37%)] Loss: 0.87643 (1.053) Data (t): 0.001 Batch (t): 0.903, 566.254/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:51:46 | INFO | Train Epoch: 0 [ 6758912/18327966 (37%)] Loss: 0.85963 (1.052) Data (t): 0.001 Batch (t): 0.903, 564.293/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:53:17 | INFO | Train Epoch: 0 [ 6810112/18327966 (37%)] Loss: 1.0279 (1.052) Data (t): 0.001 Batch (t): 0.902, 569.248/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:54:55 | INFO | Train Epoch: 0 [ 6861312/18327966 (37%)] Loss: 0.89707 (1.051) Data (t): 0.001 Batch (t): 0.985, 572.848/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:56:26 | INFO | Train Epoch: 0 [ 6912512/18327966 (38%)] Loss: 0.93583 (1.050) Data (t): 0.001 Batch (t): 0.914, 567.667/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:57:57 | INFO | Train Epoch: 0 [ 6963712/18327966 (38%)] Loss: 0.85162 (1.048) Data (t): 0.001 Batch (t): 0.903, 568.519/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:59:27 | INFO | Train Epoch: 0 [ 7014912/18327966 (38%)] Loss: 0.84659 (1.047) Data (t): 0.001 Batch (t): 0.903, 564.954/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:00:57 | INFO | Train Epoch: 0 [ 7066112/18327966 (39%)] Loss: 0.76957 (1.045) Data (t): 0.001 Batch (t): 0.903, 568.779/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:02:33 | INFO | Train Epoch: 0 [ 7117312/18327966 (39%)] Loss: 0.86513 (1.044) Data (t): 0.001 Batch (t): 0.958, 566.737/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:04:07 | INFO | Train Epoch: 0 [ 7168512/18327966 (39%)] Loss: 0.88662 (1.042) Data (t): 0.001 Batch (t): 0.942, 565.216/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:05:38 | INFO | Train Epoch: 0 [ 7219712/18327966 (39%)] Loss: 0.91699 (1.042) Data (t): 0.001 Batch (t): 0.903, 566.434/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:07:08 | INFO | Train Epoch: 0 [ 7270912/18327966 (40%)] Loss: 0.86768 (1.040) Data (t): 0.001 Batch (t): 0.902, 569.917/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:08:38 | INFO | Train Epoch: 0 [ 7322112/18327966 (40%)] Loss: 0.89716 (1.039) Data (t): 0.001 Batch (t): 0.901, 567.171/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:10:14 | INFO | Train Epoch: 0 [ 7373312/18327966 (40%)] Loss: 0.81936 (1.038) Data (t): 0.001 Batch (t): 0.958, 568.387/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:11:48 | INFO | Train Epoch: 0 [ 7424512/18327966 (41%)] Loss: 0.86621 (1.037) Data (t): 0.001 Batch (t): 0.943, 567.428/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:13:18 | INFO | Train Epoch: 0 [ 7475712/18327966 (41%)] Loss: 0.86980 (1.036) Data (t): 0.001 Batch (t): 0.903, 566.350/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:14:49 | INFO | Train Epoch: 0 [ 7526912/18327966 (41%)] Loss: 0.88914 (1.035) Data (t): 0.001 Batch (t): 0.904, 563.823/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:16:19 | INFO | Train Epoch: 0 [ 7578112/18327966 (41%)] Loss: 1.0627 (1.035) Data (t): 0.001 Batch (t): 0.903, 565.651/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:17:55 | INFO | Train Epoch: 0 [ 7629312/18327966 (42%)] Loss: 0.84401 (1.033) Data (t): 0.001 Batch (t): 0.958, 565.407/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:19:29 | INFO | Train Epoch: 0 [ 7680512/18327966 (42%)] Loss: 0.88287 (1.032) Data (t): 0.001 Batch (t): 0.943, 567.586/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:20:59 | INFO | Train Epoch: 0 [ 7731712/18327966 (42%)] Loss: 0.76748 (1.031) Data (t): 0.001 Batch (t): 0.903, 567.705/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:22:30 | INFO | Train Epoch: 0 [ 7782912/18327966 (42%)] Loss: 0.86989 (1.030) Data (t): 0.001 Batch (t): 0.904, 562.222/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:24:00 | INFO | Train Epoch: 0 [ 7834112/18327966 (43%)] Loss: 0.88488 (1.029) Data (t): 0.001 Batch (t): 0.904, 567.885/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:25:34 | INFO | Train Epoch: 0 [ 7885312/18327966 (43%)] Loss: 0.87705 (1.028) Data (t): 0.001 Batch (t): 0.936, 567.113/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:27:09 | INFO | Train Epoch: 0 [ 7936512/18327966 (43%)] Loss: 0.74207 (1.026) Data (t): 0.001 Batch (t): 0.955, 565.745/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:28:41 | INFO | Train Epoch: 0 [ 7987712/18327966 (44%)] Loss: 0.88271 (1.025) Data (t): 0.001 Batch (t): 0.915, 566.240/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:30:11 | INFO | Train Epoch: 0 [ 8038912/18327966 (44%)] Loss: 0.88517 (1.024) Data (t): 0.001 Batch (t): 0.902, 567.911/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:31:41 | INFO | Train Epoch: 0 [ 8090112/18327966 (44%)] Loss: 0.95329 (1.024) Data (t): 0.001 Batch (t): 0.902, 568.563/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:33:11 | INFO | Train Epoch: 0 [ 8141312/18327966 (44%)] Loss: 0.74121 (1.022) Data (t): 0.001 Batch (t): 0.902, 570.526/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:34:48 | INFO | Train Epoch: 0 [ 8192512/18327966 (45%)] Loss: 0.85868 (1.021) Data (t): 0.001 Batch (t): 0.971, 568.124/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:36:22 | INFO | Train Epoch: 0 [ 8243712/18327966 (45%)] Loss: 0.78430 (1.019) Data (t): 0.001 Batch (t): 0.931, 565.205/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:37:52 | INFO | Train Epoch: 0 [ 8294912/18327966 (45%)] Loss: 0.90808 (1.019) Data (t): 0.001 Batch (t): 0.902, 568.693/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:39:22 | INFO | Train Epoch: 0 [ 8346112/18327966 (46%)] Loss: 0.78761 (1.017) Data (t): 0.001 Batch (t): 0.902, 566.399/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:40:52 | INFO | Train Epoch: 0 [ 8397312/18327966 (46%)] Loss: 0.77318 (1.016) Data (t): 0.001 Batch (t): 0.902, 566.977/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:42:28 | INFO | Train Epoch: 0 [ 8448512/18327966 (46%)] Loss: 0.86287 (1.015) Data (t): 0.001 Batch (t): 0.958, 566.309/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:44:02 | INFO | Train Epoch: 0 [ 8499712/18327966 (46%)] Loss: 0.88992 (1.014) Data (t): 0.001 Batch (t): 0.944, 567.165/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:45:33 | INFO | Train Epoch: 0 [ 8550912/18327966 (47%)] Loss: 0.92053 (1.014) Data (t): 0.001 Batch (t): 0.903, 567.258/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:47:03 | INFO | Train Epoch: 0 [ 8602112/18327966 (47%)] Loss: 0.87176 (1.013) Data (t): 0.001 Batch (t): 0.902, 565.331/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:48:33 | INFO | Train Epoch: 0 [ 8653312/18327966 (47%)] Loss: 0.76126 (1.011) Data (t): 0.001 Batch (t): 0.902, 569.700/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:50:09 | INFO | Train Epoch: 0 [ 8704512/18327966 (47%)] Loss: 0.81537 (1.010) Data (t): 0.001 Batch (t): 0.958, 567.556/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:51:43 | INFO | Train Epoch: 0 [ 8755712/18327966 (48%)] Loss: 0.80173 (1.009) Data (t): 0.001 Batch (t): 0.942, 566.378/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:53:13 | INFO | Train Epoch: 0 [ 8806912/18327966 (48%)] Loss: 0.84050 (1.008) Data (t): 0.001 Batch (t): 0.902, 566.646/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:54:43 | INFO | Train Epoch: 0 [ 8858112/18327966 (48%)] Loss: 0.94577 (1.008) Data (t): 0.001 Batch (t): 0.902, 570.294/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:56:14 | INFO | Train Epoch: 0 [ 8909312/18327966 (49%)] Loss: 0.88619 (1.007) Data (t): 0.001 Batch (t): 0.902, 569.010/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:57:49 | INFO | Train Epoch: 0 [ 8960512/18327966 (49%)] Loss: 0.92311 (1.006) Data (t): 0.001 Batch (t): 0.958, 569.530/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:59:24 | INFO | Train Epoch: 0 [ 9011712/18327966 (49%)] Loss: 0.85377 (1.006) Data (t): 0.001 Batch (t): 0.941, 567.480/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:00:54 | INFO | Train Epoch: 0 [ 9062912/18327966 (49%)] Loss: 0.92239 (1.005) Data (t): 0.001 Batch (t): 0.903, 569.480/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:02:24 | INFO | Train Epoch: 0 [ 9114112/18327966 (50%)] Loss: 0.91270 (1.005) Data (t): 0.001 Batch (t): 0.903, 567.695/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:03:54 | INFO | Train Epoch: 0 [ 9165312/18327966 (50%)] Loss: 0.78770 (1.003) Data (t): 0.001 Batch (t): 0.903, 566.975/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:05:26 | INFO | Train Epoch: 0 [ 9216512/18327966 (50%)] Loss: 0.92648 (1.003) Data (t): 0.001 Batch (t): 0.914, 567.344/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:07:02 | INFO | Train Epoch: 0 [ 9267712/18327966 (51%)] Loss: 0.79220 (1.002) Data (t): 0.001 Batch (t): 0.967, 568.384/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:08:34 | INFO | Train Epoch: 0 [ 9318912/18327966 (51%)] Loss: 0.72938 (1.000) Data (t): 0.001 Batch (t): 0.914, 567.545/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:10:04 | INFO | Train Epoch: 0 [ 9370112/18327966 (51%)] Loss: 0.74346 (0.9989) Data (t): 0.001 Batch (t): 0.903, 568.246/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:11:34 | INFO | Train Epoch: 0 [ 9421312/18327966 (51%)] Loss: 0.92105 (0.9985) Data (t): 0.001 Batch (t): 0.902, 563.563/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:13:05 | INFO | Train Epoch: 0 [ 9472512/18327966 (52%)] Loss: 0.72038 (0.9970) Data (t): 0.001 Batch (t): 0.902, 569.312/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:14:39 | INFO | Train Epoch: 0 [ 9523712/18327966 (52%)] Loss: 0.77126 (0.9958) Data (t): 0.001 Batch (t): 0.948, 569.776/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:16:14 | INFO | Train Epoch: 0 [ 9574912/18327966 (52%)] Loss: 0.89952 (0.9953) Data (t): 0.001 Batch (t): 0.942, 564.899/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:17:44 | INFO | Train Epoch: 0 [ 9626112/18327966 (53%)] Loss: 0.85249 (0.9945) Data (t): 0.001 Batch (t): 0.903, 568.496/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:19:14 | INFO | Train Epoch: 0 [ 9677312/18327966 (53%)] Loss: 0.71092 (0.9931) Data (t): 0.001 Batch (t): 0.903, 569.648/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:20:44 | INFO | Train Epoch: 0 [ 9728512/18327966 (53%)] Loss: 0.84850 (0.9923) Data (t): 0.001 Batch (t): 0.902, 566.831/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:22:20 | INFO | Train Epoch: 0 [ 9779712/18327966 (53%)] Loss: 0.72777 (0.9909) Data (t): 0.001 Batch (t): 0.960, 564.610/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:23:55 | INFO | Train Epoch: 0 [ 9830912/18327966 (54%)] Loss: 0.65813 (0.9892) Data (t): 0.001 Batch (t): 0.943, 565.572/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:25:25 | INFO | Train Epoch: 0 [ 9882112/18327966 (54%)] Loss: 0.88460 (0.9887) Data (t): 0.001 Batch (t): 0.902, 569.596/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:26:55 | INFO | Train Epoch: 0 [ 9933312/18327966 (54%)] Loss: 0.85291 (0.9880) Data (t): 0.001 Batch (t): 0.902, 570.772/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:28:25 | INFO | Train Epoch: 0 [ 9984512/18327966 (54%)] Loss: 0.87019 (0.9874) Data (t): 0.001 Batch (t): 0.902, 567.469/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:30:01 | INFO | Train Epoch: 0 [10035712/18327966 (55%)] Loss: 0.84597 (0.9866) Data (t): 0.001 Batch (t): 0.960, 567.820/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:31:36 | INFO | Train Epoch: 0 [10086912/18327966 (55%)] Loss: 0.89252 (0.9862) Data (t): 0.001 Batch (t): 0.944, 565.617/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:33:06 | INFO | Train Epoch: 0 [10138112/18327966 (55%)] Loss: 0.74619 (0.9850) Data (t): 0.001 Batch (t): 0.904, 564.180/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:34:37 | INFO | Train Epoch: 0 [10189312/18327966 (56%)] Loss: 0.73683 (0.9837) Data (t): 0.001 Batch (t): 0.903, 568.860/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:36:07 | INFO | Train Epoch: 0 [10240512/18327966 (56%)] Loss: 0.71831 (0.9824) Data (t): 0.001 Batch (t): 0.903, 567.372/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:37:43 | INFO | Train Epoch: 0 [10291712/18327966 (56%)] Loss: 0.90991 (0.9820) Data (t): 0.001 Batch (t): 0.960, 567.286/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:39:17 | INFO | Train Epoch: 0 [10342912/18327966 (56%)] Loss: 0.78969 (0.9811) Data (t): 0.001 Batch (t): 0.943, 565.145/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:40:47 | INFO | Train Epoch: 0 [10394112/18327966 (57%)] Loss: 0.89781 (0.9807) Data (t): 0.001 Batch (t): 0.902, 567.001/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:42:18 | INFO | Train Epoch: 0 [10445312/18327966 (57%)] Loss: 0.75269 (0.9796) Data (t): 0.001 Batch (t): 0.902, 565.853/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:43:48 | INFO | Train Epoch: 0 [10496512/18327966 (57%)] Loss: 0.89454 (0.9792) Data (t): 0.001 Batch (t): 0.902, 563.747/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:45:19 | INFO | Train Epoch: 0 [10547712/18327966 (58%)] Loss: 0.72855 (0.9780) Data (t): 0.001 Batch (t): 0.915, 563.784/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:46:57 | INFO | Train Epoch: 0 [10598912/18327966 (58%)] Loss: 0.80680 (0.9771) Data (t): 0.001 Batch (t): 0.977, 568.851/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:48:29 | INFO | Train Epoch: 0 [10650112/18327966 (58%)] Loss: 0.85222 (0.9765) Data (t): 0.001 Batch (t): 0.914, 565.253/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:49:59 | INFO | Train Epoch: 0 [10701312/18327966 (58%)] Loss: 0.87534 (0.9760) Data (t): 0.001 Batch (t): 0.903, 565.515/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:51:29 | INFO | Train Epoch: 0 [10752512/18327966 (59%)] Loss: 0.95337 (0.9759) Data (t): 0.001 Batch (t): 0.904, 567.597/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:52:59 | INFO | Train Epoch: 0 [10803712/18327966 (59%)] Loss: 0.79367 (0.9751) Data (t): 0.001 Batch (t): 0.903, 567.524/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:54:35 | INFO | Train Epoch: 0 [10854912/18327966 (59%)] Loss: 0.72852 (0.9739) Data (t): 0.001 Batch (t): 0.960, 567.764/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:56:10 | INFO | Train Epoch: 0 [10906112/18327966 (60%)] Loss: 0.80770 (0.9731) Data (t): 0.001 Batch (t): 0.943, 570.406/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:57:40 | INFO | Train Epoch: 0 [10957312/18327966 (60%)] Loss: 0.72411 (0.9720) Data (t): 0.001 Batch (t): 0.902, 567.066/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:59:10 | INFO | Train Epoch: 0 [11008512/18327966 (60%)] Loss: 0.69769 (0.9707) Data (t): 0.001 Batch (t): 0.903, 567.809/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,19:00:41 | INFO | Train Epoch: 0 [11059712/18327966 (60%)] Loss: 0.79046 (0.9699) Data (t): 0.001 Batch (t): 0.903, 567.687/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,19:02:17 | INFO | Train Epoch: 0 [11110912/18327966 (61%)] Loss: 0.79688 (0.9691) Data (t): 0.001 Batch (t): 0.961, 562.826/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,19:03:51 | INFO | Train Epoch: 0 [11162112/18327966 (61%)] Loss: 0.82291 (0.9684) Data (t): 0.001 Batch (t): 0.943, 569.582/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,19:05:21 | INFO | Train Epoch: 0 [11213312/18327966 (61%)] Loss: 0.80061 (0.9677) Data (t): 0.001 Batch (t): 0.903, 561.619/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,19:06:52 | INFO | Train Epoch: 0 [11264512/18327966 (61%)] Loss: 0.83766 (0.9671) Data (t): 0.001 Batch (t): 0.903, 569.795/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,19:08:22 | INFO | Train Epoch: 0 [11315712/18327966 (62%)] Loss: 0.71977 (0.9660) Data (t): 0.001 Batch (t): 0.903, 567.022/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,19:09:58 | INFO | Train Epoch: 0 [11366912/18327966 (62%)] Loss: 0.84119 (0.9654) Data (t): 0.001 Batch (t): 0.960, 567.873/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,19:11:32 | INFO | Train Epoch: 0 [11418112/18327966 (62%)] Loss: 0.86319 (0.9649) Data (t): 0.001 Batch (t): 0.944, 567.461/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,19:13:03 | INFO | Train Epoch: 0 [11469312/18327966 (63%)] Loss: 0.73379 (0.9639) Data (t): 0.001 Batch (t): 0.902, 565.750/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,19:14:33 | INFO | Train Epoch: 0 [11520512/18327966 (63%)] Loss: 0.79282 (0.9632) Data (t): 0.001 Batch (t): 0.903, 566.530/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,19:16:03 | INFO | Train Epoch: 0 [11571712/18327966 (63%)] Loss: 0.90678 (0.9629) Data (t): 0.001 Batch (t): 0.903, 566.305/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,19:17:37 | INFO | Train Epoch: 0 [11622912/18327966 (63%)] Loss: 0.79066 (0.9622) Data (t): 0.001 Batch (t): 0.938, 567.797/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,19:19:12 | INFO | Train Epoch: 0 [11674112/18327966 (64%)] Loss: 0.79197 (0.9614) Data (t): 0.001 Batch (t): 0.954, 568.656/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,19:20:44 | INFO | Train Epoch: 0 [11725312/18327966 (64%)] Loss: 0.73296 (0.9604) Data (t): 0.001 Batch (t): 0.914, 566.887/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,19:22:14 | INFO | Train Epoch: 0 [11776512/18327966 (64%)] Loss: 0.88387 (0.9601) Data (t): 0.001 Batch (t): 0.904, 562.318/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,19:23:44 | INFO | Train Epoch: 0 [11827712/18327966 (65%)] Loss: 0.80772 (0.9594) Data (t): 0.001 Batch (t): 0.902, 568.208/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,19:25:17 | INFO | Train Epoch: 0 [11878912/18327966 (65%)] Loss: 0.79028 (0.9587) Data (t): 0.001 Batch (t): 0.926, 246.802/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,19:26:54 | INFO | Train Epoch: 0 [11930112/18327966 (65%)] Loss: 0.83320 (0.9582) Data (t): 0.001 Batch (t): 0.967, 566.299/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,19:28:25 | INFO | Train Epoch: 0 [11981312/18327966 (65%)] Loss: 0.74362 (0.9573) Data (t): 0.001 Batch (t): 0.914, 567.013/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,19:29:55 | INFO | Train Epoch: 0 [12032512/18327966 (66%)] Loss: 0.89189 (0.9570) Data (t): 0.001 Batch (t): 0.903, 568.727/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,19:31:26 | INFO | Train Epoch: 0 [12083712/18327966 (66%)] Loss: 0.73337 (0.9560) Data (t): 0.001 Batch (t): 0.902, 565.980/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,19:32:56 | INFO | Train Epoch: 0 [12134912/18327966 (66%)] Loss: 0.90378 (0.9558) Data (t): 0.001 Batch (t): 0.902, 569.428/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,19:34:32 | INFO | Train Epoch: 0 [12186112/18327966 (66%)] Loss: 0.66674 (0.9546) Data (t): 0.001 Batch (t): 0.961, 567.954/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,19:36:06 | INFO | Train Epoch: 0 [12237312/18327966 (67%)] Loss: 0.92684 (0.9545) Data (t): 0.001 Batch (t): 0.943, 570.650/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,19:37:36 | INFO | Train Epoch: 0 [12288512/18327966 (67%)] Loss: 0.67610 (0.9533) Data (t): 0.001 Batch (t): 0.902, 569.278/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,19:39:07 | INFO | Train Epoch: 0 [12339712/18327966 (67%)] Loss: 0.91888 (0.9532) Data (t): 0.001 Batch (t): 0.903, 569.109/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,19:40:37 | INFO | Train Epoch: 0 [12390912/18327966 (68%)] Loss: 0.88120 (0.9529) Data (t): 0.001 Batch (t): 0.902, 570.117/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,19:42:13 | INFO | Train Epoch: 0 [12442112/18327966 (68%)] Loss: 0.82568 (0.9524) Data (t): 0.001 Batch (t): 0.961, 565.862/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,19:43:47 | INFO | Train Epoch: 0 [12493312/18327966 (68%)] Loss: 0.86910 (0.9520) Data (t): 0.001 Batch (t): 0.944, 567.229/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,19:45:18 | INFO | Train Epoch: 0 [12544512/18327966 (68%)] Loss: 0.73905 (0.9512) Data (t): 0.001 Batch (t): 0.903, 568.021/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,19:46:48 | INFO | Train Epoch: 0 [12595712/18327966 (69%)] Loss: 0.74946 (0.9504) Data (t): 0.001 Batch (t): 0.902, 564.871/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,19:48:18 | INFO | Train Epoch: 0 [12646912/18327966 (69%)] Loss: 0.89531 (0.9501) Data (t): 0.001 Batch (t): 0.901, 568.482/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,19:49:54 | INFO | Train Epoch: 0 [12698112/18327966 (69%)] Loss: 0.87380 (0.9498) Data (t): 0.001 Batch (t): 0.960, 561.757/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,19:51:27 | INFO | Train Epoch: 0 [12749312/18327966 (70%)] Loss: 0.84628 (0.9494) Data (t): 0.001 Batch (t): 0.931, 567.939/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,19:52:58 | INFO | Train Epoch: 0 [12800512/18327966 (70%)] Loss: 0.77714 (0.9487) Data (t): 0.001 Batch (t): 0.915, 567.341/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,19:54:29 | INFO | Train Epoch: 0 [12851712/18327966 (70%)] Loss: 0.85504 (0.9484) Data (t): 0.001 Batch (t): 0.903, 567.386/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,19:55:59 | INFO | Train Epoch: 0 [12902912/18327966 (70%)] Loss: 0.90893 (0.9482) Data (t): 0.001 Batch (t): 0.902, 567.232/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,19:57:33 | INFO | Train Epoch: 0 [12954112/18327966 (71%)] Loss: 0.83518 (0.9478) Data (t): 0.001 Batch (t): 0.939, 569.132/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,19:59:08 | INFO | Train Epoch: 0 [13005312/18327966 (71%)] Loss: 0.77906 (0.9471) Data (t): 0.001 Batch (t): 0.955, 568.096/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,20:00:40 | INFO | Train Epoch: 0 [13056512/18327966 (71%)] Loss: 0.76038 (0.9464) Data (t): 0.001 Batch (t): 0.915, 568.692/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,20:02:10 | INFO | Train Epoch: 0 [13107712/18327966 (72%)] Loss: 0.71251 (0.9455) Data (t): 0.001 Batch (t): 0.903, 568.922/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,20:03:41 | INFO | Train Epoch: 0 [13158912/18327966 (72%)] Loss: 0.84123 (0.9450) Data (t): 0.001 Batch (t): 0.903, 566.158/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,20:05:12 | INFO | Train Epoch: 0 [13210112/18327966 (72%)] Loss: 0.81270 (0.9445) Data (t): 0.001 Batch (t): 0.914, 565.965/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,20:06:50 | INFO | Train Epoch: 0 [13261312/18327966 (72%)] Loss: 0.75450 (0.9438) Data (t): 0.001 Batch (t): 0.979, 567.978/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,20:08:21 | INFO | Train Epoch: 0 [13312512/18327966 (73%)] Loss: 0.78933 (0.9432) Data (t): 0.001 Batch (t): 0.914, 566.670/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,20:09:52 | INFO | Train Epoch: 0 [13363712/18327966 (73%)] Loss: 0.80547 (0.9427) Data (t): 0.001 Batch (t): 0.903, 567.744/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,20:11:22 | INFO | Train Epoch: 0 [13414912/18327966 (73%)] Loss: 0.77055 (0.9420) Data (t): 0.001 Batch (t): 0.903, 564.928/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,20:12:52 | INFO | Train Epoch: 0 [13466112/18327966 (73%)] Loss: 0.76903 (0.9414) Data (t): 0.001 Batch (t): 0.902, 567.645/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,20:14:28 | INFO | Train Epoch: 0 [13517312/18327966 (74%)] Loss: 0.80220 (0.9409) Data (t): 0.001 Batch (t): 0.962, 565.217/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,20:16:03 | INFO | Train Epoch: 0 [13568512/18327966 (74%)] Loss: 0.71437 (0.9400) Data (t): 0.001 Batch (t): 0.945, 566.802/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,20:17:33 | INFO | Train Epoch: 0 [13619712/18327966 (74%)] Loss: 0.73439 (0.9392) Data (t): 0.001 Batch (t): 0.902, 567.807/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,20:19:03 | INFO | Train Epoch: 0 [13670912/18327966 (75%)] Loss: 0.79933 (0.9387) Data (t): 0.001 Batch (t): 0.903, 570.223/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,20:20:33 | INFO | Train Epoch: 0 [13722112/18327966 (75%)] Loss: 0.88432 (0.9385) Data (t): 0.001 Batch (t): 0.903, 569.515/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,20:22:10 | INFO | Train Epoch: 0 [13773312/18327966 (75%)] Loss: 0.93140 (0.9385) Data (t): 0.001 Batch (t): 0.961, 567.180/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,20:23:44 | INFO | Train Epoch: 0 [13824512/18327966 (75%)] Loss: 0.78552 (0.9379) Data (t): 0.001 Batch (t): 0.943, 569.273/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,20:25:14 | INFO | Train Epoch: 0 [13875712/18327966 (76%)] Loss: 0.85537 (0.9376) Data (t): 0.001 Batch (t): 0.902, 567.481/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,20:26:44 | INFO | Train Epoch: 0 [13926912/18327966 (76%)] Loss: 0.81941 (0.9372) Data (t): 0.001 Batch (t): 0.902, 570.741/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,20:28:14 | INFO | Train Epoch: 0 [13978112/18327966 (76%)] Loss: 0.76645 (0.9366) Data (t): 0.001 Batch (t): 0.900, 568.508/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,20:29:49 | INFO | Train Epoch: 0 [14029312/18327966 (77%)] Loss: 0.83331 (0.9362) Data (t): 0.001 Batch (t): 0.949, 568.915/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,20:31:23 | INFO | Train Epoch: 0 [14080512/18327966 (77%)] Loss: 0.89048 (0.9360) Data (t): 0.001 Batch (t): 0.942, 568.436/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,20:32:55 | INFO | Train Epoch: 0 [14131712/18327966 (77%)] Loss: 0.78637 (0.9355) Data (t): 0.001 Batch (t): 0.913, 567.569/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,20:34:25 | INFO | Train Epoch: 0 [14182912/18327966 (77%)] Loss: 0.85848 (0.9352) Data (t): 0.001 Batch (t): 0.903, 564.654/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,20:35:55 | INFO | Train Epoch: 0 [14234112/18327966 (78%)] Loss: 0.79629 (0.9347) Data (t): 0.001 Batch (t): 0.902, 567.775/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,20:37:28 | INFO | Train Epoch: 0 [14285312/18327966 (78%)] Loss: 0.89196 (0.9345) Data (t): 0.001 Batch (t): 0.926, 568.596/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,20:39:05 | INFO | Train Epoch: 0 [14336512/18327966 (78%)] Loss: 0.79033 (0.9340) Data (t): 0.001 Batch (t): 0.968, 566.525/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,20:40:36 | INFO | Train Epoch: 0 [14387712/18327966 (79%)] Loss: 0.91626 (0.9340) Data (t): 0.001 Batch (t): 0.914, 568.134/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,20:42:06 | INFO | Train Epoch: 0 [14438912/18327966 (79%)] Loss: 0.79174 (0.9335) Data (t): 0.001 Batch (t): 0.904, 567.816/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,20:43:37 | INFO | Train Epoch: 0 [14490112/18327966 (79%)] Loss: 0.86670 (0.9332) Data (t): 0.001 Batch (t): 0.902, 569.007/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,20:45:08 | INFO | Train Epoch: 0 [14541312/18327966 (79%)] Loss: 0.86218 (0.9330) Data (t): 0.001 Batch (t): 0.914, 570.433/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,20:46:45 | INFO | Train Epoch: 0 [14592512/18327966 (80%)] Loss: 0.75260 (0.9324) Data (t): 0.001 Batch (t): 0.968, 189.034/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,20:48:17 | INFO | Train Epoch: 0 [14643712/18327966 (80%)] Loss: 0.78685 (0.9318) Data (t): 0.001 Batch (t): 0.926, 564.085/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,20:49:48 | INFO | Train Epoch: 0 [14694912/18327966 (80%)] Loss: 0.77095 (0.9313) Data (t): 0.001 Batch (t): 0.902, 568.478/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,20:51:18 | INFO | Train Epoch: 0 [14746112/18327966 (80%)] Loss: 0.87202 (0.9311) Data (t): 0.001 Batch (t): 0.902, 566.538/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,20:52:48 | INFO | Train Epoch: 0 [14797312/18327966 (81%)] Loss: 0.89607 (0.9310) Data (t): 0.001 Batch (t): 0.902, 567.370/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,20:54:24 | INFO | Train Epoch: 0 [14848512/18327966 (81%)] Loss: 0.72284 (0.9302) Data (t): 0.001 Batch (t): 0.962, 571.271/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,20:55:59 | INFO | Train Epoch: 0 [14899712/18327966 (81%)] Loss: 0.65991 (0.9293) Data (t): 0.001 Batch (t): 0.945, 568.694/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,20:57:29 | INFO | Train Epoch: 0 [14950912/18327966 (82%)] Loss: 0.76960 (0.9288) Data (t): 0.001 Batch (t): 0.906, 566.420/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,20:59:00 | INFO | Train Epoch: 0 [15002112/18327966 (82%)] Loss: 0.76523 (0.9282) Data (t): 0.001 Batch (t): 0.903, 568.782/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,21:00:30 | INFO | Train Epoch: 0 [15053312/18327966 (82%)] Loss: 0.83866 (0.9279) Data (t): 0.001 Batch (t): 0.902, 568.061/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,21:02:06 | INFO | Train Epoch: 0 [15104512/18327966 (82%)] Loss: 0.82765 (0.9276) Data (t): 0.001 Batch (t): 0.963, 567.675/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,21:03:41 | INFO | Train Epoch: 0 [15155712/18327966 (83%)] Loss: 0.82362 (0.9272) Data (t): 0.001 Batch (t): 0.944, 238.732/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,21:05:11 | INFO | Train Epoch: 0 [15206912/18327966 (83%)] Loss: 0.77927 (0.9267) Data (t): 0.001 Batch (t): 0.902, 567.092/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,21:06:41 | INFO | Train Epoch: 0 [15258112/18327966 (83%)] Loss: 0.80807 (0.9263) Data (t): 0.001 Batch (t): 0.902, 568.287/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,21:08:11 | INFO | Train Epoch: 0 [15309312/18327966 (84%)] Loss: 0.79011 (0.9259) Data (t): 0.001 Batch (t): 0.902, 568.844/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,21:09:46 | INFO | Train Epoch: 0 [15360512/18327966 (84%)] Loss: 0.76330 (0.9253) Data (t): 0.001 Batch (t): 0.950, 241.085/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,21:11:19 | INFO | Train Epoch: 0 [15411712/18327966 (84%)] Loss: 0.73970 (0.9247) Data (t): 0.001 Batch (t): 0.931, 560.916/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,21:12:51 | INFO | Train Epoch: 0 [15462912/18327966 (84%)] Loss: 0.75252 (0.9242) Data (t): 0.001 Batch (t): 0.913, 569.093/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,21:14:21 | INFO | Train Epoch: 0 [15514112/18327966 (85%)] Loss: 0.83398 (0.9239) Data (t): 0.001 Batch (t): 0.902, 569.788/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,21:15:51 | INFO | Train Epoch: 0 [15565312/18327966 (85%)] Loss: 0.78192 (0.9234) Data (t): 0.001 Batch (t): 0.901, 564.208/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,21:17:22 | INFO | Train Epoch: 0 [15616512/18327966 (85%)] Loss: 0.81921 (0.9231) Data (t): 0.001 Batch (t): 0.912, 569.754/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,21:18:58 | INFO | Train Epoch: 0 [15667712/18327966 (85%)] Loss: 0.82979 (0.9228) Data (t): 0.001 Batch (t): 0.955, 569.524/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,21:20:30 | INFO | Train Epoch: 0 [15718912/18327966 (86%)] Loss: 0.73451 (0.9221) Data (t): 0.001 Batch (t): 0.926, 563.938/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,21:22:01 | INFO | Train Epoch: 0 [15770112/18327966 (86%)] Loss: 0.72366 (0.9215) Data (t): 0.001 Batch (t): 0.902, 563.661/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,21:23:31 | INFO | Train Epoch: 0 [15821312/18327966 (86%)] Loss: 0.70898 (0.9208) Data (t): 0.001 Batch (t): 0.901, 569.218/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,21:25:02 | INFO | Train Epoch: 0 [15872512/18327966 (87%)] Loss: 0.73459 (0.9202) Data (t): 0.001 Batch (t): 0.914, 567.954/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,21:26:36 | INFO | Train Epoch: 0 [15923712/18327966 (87%)] Loss: 0.78713 (0.9198) Data (t): 0.001 Batch (t): 0.938, 567.040/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,21:28:10 | INFO | Train Epoch: 0 [15974912/18327966 (87%)] Loss: 0.78983 (0.9194) Data (t): 0.001 Batch (t): 0.943, 570.877/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,21:29:40 | INFO | Train Epoch: 0 [16026112/18327966 (87%)] Loss: 0.85531 (0.9192) Data (t): 0.001 Batch (t): 0.900, 569.058/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,21:31:10 | INFO | Train Epoch: 0 [16077312/18327966 (88%)] Loss: 0.78175 (0.9187) Data (t): 0.001 Batch (t): 0.900, 570.313/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,21:32:42 | INFO | Train Epoch: 0 [16128512/18327966 (88%)] Loss: 0.74827 (0.9182) Data (t): 0.001 Batch (t): 0.912, 568.491/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,21:34:17 | INFO | Train Epoch: 0 [16179712/18327966 (88%)] Loss: 0.84380 (0.9180) Data (t): 0.001 Batch (t): 0.948, 570.254/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,21:35:51 | INFO | Train Epoch: 0 [16230912/18327966 (89%)] Loss: 0.84465 (0.9177) Data (t): 0.001 Batch (t): 0.942, 571.486/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,21:37:21 | INFO | Train Epoch: 0 [16282112/18327966 (89%)] Loss: 0.92154 (0.9177) Data (t): 0.001 Batch (t): 0.899, 570.866/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,21:38:51 | INFO | Train Epoch: 0 [16333312/18327966 (89%)] Loss: 0.84152 (0.9175) Data (t): 0.001 Batch (t): 0.899, 569.700/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,21:40:21 | INFO | Train Epoch: 0 [16384512/18327966 (89%)] Loss: 0.80511 (0.9172) Data (t): 0.001 Batch (t): 0.900, 569.759/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,21:41:57 | INFO | Train Epoch: 0 [16435712/18327966 (90%)] Loss: 0.76104 (0.9167) Data (t): 0.001 Batch (t): 0.962, 565.650/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,21:43:30 | INFO | Train Epoch: 0 [16486912/18327966 (90%)] Loss: 0.72106 (0.9161) Data (t): 0.001 Batch (t): 0.931, 568.151/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,21:45:01 | INFO | Train Epoch: 0 [16538112/18327966 (90%)] Loss: 0.74944 (0.9155) Data (t): 0.001 Batch (t): 0.914, 568.116/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,21:46:31 | INFO | Train Epoch: 0 [16589312/18327966 (91%)] Loss: 0.81275 (0.9152) Data (t): 0.001 Batch (t): 0.901, 565.069/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,21:48:01 | INFO | Train Epoch: 0 [16640512/18327966 (91%)] Loss: 0.84105 (0.9150) Data (t): 0.001 Batch (t): 0.901, 568.632/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,21:49:36 | INFO | Train Epoch: 0 [16691712/18327966 (91%)] Loss: 0.74794 (0.9145) Data (t): 0.001 Batch (t): 0.951, 574.478/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,21:51:11 | INFO | Train Epoch: 0 [16742912/18327966 (91%)] Loss: 0.80016 (0.9141) Data (t): 0.001 Batch (t): 0.943, 569.480/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,21:52:42 | INFO | Train Epoch: 0 [16794112/18327966 (92%)] Loss: 0.66809 (0.9134) Data (t): 0.001 Batch (t): 0.913, 567.463/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,21:54:12 | INFO | Train Epoch: 0 [16845312/18327966 (92%)] Loss: 0.84950 (0.9132) Data (t): 0.001 Batch (t): 0.900, 566.636/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,21:55:42 | INFO | Train Epoch: 0 [16896512/18327966 (92%)] Loss: 0.81475 (0.9129) Data (t): 0.001 Batch (t): 0.900, 568.551/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,21:57:13 | INFO | Train Epoch: 0 [16947712/18327966 (92%)] Loss: 0.74465 (0.9124) Data (t): 0.001 Batch (t): 0.912, 569.391/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,21:58:48 | INFO | Train Epoch: 0 [16998912/18327966 (93%)] Loss: 0.85263 (0.9122) Data (t): 0.001 Batch (t): 0.949, 569.635/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,22:00:22 | INFO | Train Epoch: 0 [17050112/18327966 (93%)] Loss: 0.66517 (0.9115) Data (t): 0.001 Batch (t): 0.943, 566.314/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,22:01:53 | INFO | Train Epoch: 0 [17101312/18327966 (93%)] Loss: 0.77548 (0.9111) Data (t): 0.001 Batch (t): 0.901, 570.154/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,22:03:23 | INFO | Train Epoch: 0 [17152512/18327966 (94%)] Loss: 0.66952 (0.9104) Data (t): 0.001 Batch (t): 0.900, 568.740/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,22:04:54 | INFO | Train Epoch: 0 [17203712/18327966 (94%)] Loss: 0.68074 (0.9097) Data (t): 0.001 Batch (t): 0.913, 569.980/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,22:06:29 | INFO | Train Epoch: 0 [17254912/18327966 (94%)] Loss: 0.75552 (0.9092) Data (t): 0.001 Batch (t): 0.951, 567.762/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,22:08:04 | INFO | Train Epoch: 0 [17306112/18327966 (94%)] Loss: 0.70088 (0.9086) Data (t): 0.001 Batch (t): 0.945, 565.150/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,22:09:34 | INFO | Train Epoch: 0 [17357312/18327966 (95%)] Loss: 0.68397 (0.9079) Data (t): 0.001 Batch (t): 0.901, 568.271/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,22:11:04 | INFO | Train Epoch: 0 [17408512/18327966 (95%)] Loss: 0.88098 (0.9079) Data (t): 0.001 Batch (t): 0.901, 569.038/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,22:12:34 | INFO | Train Epoch: 0 [17459712/18327966 (95%)] Loss: 0.74427 (0.9074) Data (t): 0.001 Batch (t): 0.901, 569.951/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,22:14:10 | INFO | Train Epoch: 0 [17510912/18327966 (96%)] Loss: 0.82875 (0.9072) Data (t): 0.001 Batch (t): 0.963, 566.444/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,22:15:43 | INFO | Train Epoch: 0 [17562112/18327966 (96%)] Loss: 0.83079 (0.9069) Data (t): 0.001 Batch (t): 0.930, 570.321/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,22:17:15 | INFO | Train Epoch: 0 [17613312/18327966 (96%)] Loss: 0.86371 (0.9068) Data (t): 0.001 Batch (t): 0.914, 570.904/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,22:18:45 | INFO | Train Epoch: 0 [17664512/18327966 (96%)] Loss: 0.83160 (0.9066) Data (t): 0.001 Batch (t): 0.901, 569.475/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,22:20:15 | INFO | Train Epoch: 0 [17715712/18327966 (97%)] Loss: 0.81944 (0.9063) Data (t): 0.001 Batch (t): 0.902, 569.302/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,22:21:51 | INFO | Train Epoch: 0 [17766912/18327966 (97%)] Loss: 0.85785 (0.9062) Data (t): 0.001 Batch (t): 0.964, 571.390/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,22:23:24 | INFO | Train Epoch: 0 [17818112/18327966 (97%)] Loss: 0.79566 (0.9059) Data (t): 0.001 Batch (t): 0.932, 567.293/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,22:24:56 | INFO | Train Epoch: 0 [17869312/18327966 (97%)] Loss: 0.79737 (0.9056) Data (t): 0.001 Batch (t): 0.914, 568.698/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,22:26:26 | INFO | Train Epoch: 0 [17920512/18327966 (98%)] Loss: 0.86020 (0.9054) Data (t): 0.001 Batch (t): 0.901, 567.137/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,22:27:56 | INFO | Train Epoch: 0 [17971712/18327966 (98%)] Loss: 0.74384 (0.9050) Data (t): 0.001 Batch (t): 0.901, 568.779/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,22:29:29 | INFO | Train Epoch: 0 [18022912/18327966 (98%)] Loss: 0.78226 (0.9046) Data (t): 0.001 Batch (t): 0.925, 568.889/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,22:31:04 | INFO | Train Epoch: 0 [18074112/18327966 (99%)] Loss: 0.77372 (0.9043) Data (t): 0.001 Batch (t): 0.956, 570.353/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,22:32:37 | INFO | Train Epoch: 0 [18125312/18327966 (99%)] Loss: 0.77930 (0.9039) Data (t): 0.001 Batch (t): 0.926, 565.861/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,22:34:07 | INFO | Train Epoch: 0 [18176512/18327966 (99%)] Loss: 0.84773 (0.9038) Data (t): 0.001 Batch (t): 0.900, 569.392/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,22:35:37 | INFO | Train Epoch: 0 [18227712/18327966 (99%)] Loss: 0.81695 (0.9035) Data (t): 0.001 Batch (t): 0.901, 569.928/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,22:37:08 | INFO | Train Epoch: 0 [18278912/18327966 (100%)] Loss: 0.80295 (0.9032) Data (t): 0.001 Batch (t): 0.913, 568.408/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,22:38:39 | INFO | Train Epoch: 0 [18327552/18327966 (100%)] Loss: 0.80299 (0.9030) Data (t): 0.001 Batch (t): 0.954, 571.646/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,22:38:47 | INFO | Start epoch 1 -2024-11-26,22:38:52 | INFO | Train Epoch: 1 [ 512/18327966 (0%)] Loss: 0.88376 (0.8838) Data (t): 3.427 Batch (t): 4.360, 117.429/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:40:26 | INFO | Train Epoch: 1 [ 51712/18327966 (0%)] Loss: 0.78020 (0.8320) Data (t): 0.001 Batch (t): 0.946, 569.347/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:41:57 | INFO | Train Epoch: 1 [ 102912/18327966 (1%)] Loss: 0.67932 (0.7811) Data (t): 0.001 Batch (t): 0.902, 571.038/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:43:27 | INFO | Train Epoch: 1 [ 154112/18327966 (1%)] Loss: 0.79654 (0.7850) Data (t): 0.001 Batch (t): 0.901, 567.751/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:44:59 | INFO | Train Epoch: 1 [ 205312/18327966 (1%)] Loss: 0.77474 (0.7829) Data (t): 0.001 Batch (t): 0.928, 572.293/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:46:33 | INFO | Train Epoch: 1 [ 256512/18327966 (1%)] Loss: 0.77299 (0.7813) Data (t): 0.001 Batch (t): 0.940, 566.858/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:48:07 | INFO | Train Epoch: 1 [ 307712/18327966 (2%)] Loss: 0.71761 (0.7722) Data (t): 0.001 Batch (t): 0.940, 566.747/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:49:38 | INFO | Train Epoch: 1 [ 358912/18327966 (2%)] Loss: 0.79547 (0.7751) Data (t): 0.001 Batch (t): 0.902, 567.128/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:51:08 | INFO | Train Epoch: 1 [ 410112/18327966 (2%)] Loss: 0.72084 (0.7691) Data (t): 0.001 Batch (t): 0.902, 564.699/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:52:38 | INFO | Train Epoch: 1 [ 461312/18327966 (3%)] Loss: 0.89982 (0.7821) Data (t): 0.001 Batch (t): 0.902, 569.461/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:54:14 | INFO | Train Epoch: 1 [ 512512/18327966 (3%)] Loss: 0.81585 (0.7852) Data (t): 0.001 Batch (t): 0.957, 567.484/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:55:47 | INFO | Train Epoch: 1 [ 563712/18327966 (3%)] Loss: 0.85686 (0.7912) Data (t): 0.001 Batch (t): 0.929, 566.557/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:57:18 | INFO | Train Epoch: 1 [ 614912/18327966 (3%)] Loss: 0.71019 (0.7849) Data (t): 0.001 Batch (t): 0.912, 568.026/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:58:48 | INFO | Train Epoch: 1 [ 666112/18327966 (4%)] Loss: 0.77273 (0.7841) Data (t): 0.001 Batch (t): 0.902, 566.704/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:00:18 | INFO | Train Epoch: 1 [ 717312/18327966 (4%)] Loss: 0.81024 (0.7858) Data (t): 0.001 Batch (t): 0.902, 571.215/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:01:54 | INFO | Train Epoch: 1 [ 768512/18327966 (4%)] Loss: 0.78923 (0.7860) Data (t): 0.001 Batch (t): 0.957, 254.006/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:03:27 | INFO | Train Epoch: 1 [ 819712/18327966 (4%)] Loss: 0.75311 (0.7841) Data (t): 0.001 Batch (t): 0.928, 568.227/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:04:58 | INFO | Train Epoch: 1 [ 870912/18327966 (5%)] Loss: 0.76517 (0.7830) Data (t): 0.001 Batch (t): 0.912, 571.004/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:06:28 | INFO | Train Epoch: 1 [ 922112/18327966 (5%)] Loss: 0.72820 (0.7802) Data (t): 0.001 Batch (t): 0.902, 566.735/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:07:58 | INFO | Train Epoch: 1 [ 973312/18327966 (5%)] Loss: 0.88093 (0.7852) Data (t): 0.001 Batch (t): 0.902, 563.701/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:09:32 | INFO | Train Epoch: 1 [ 1024512/18327966 (6%)] Loss: 0.70848 (0.7815) Data (t): 0.001 Batch (t): 0.935, 568.195/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:11:04 | INFO | Train Epoch: 1 [ 1075712/18327966 (6%)] Loss: 0.77927 (0.7814) Data (t): 0.001 Batch (t): 0.924, 568.787/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:12:38 | INFO | Train Epoch: 1 [ 1126912/18327966 (6%)] Loss: 0.71050 (0.7783) Data (t): 0.001 Batch (t): 0.941, 570.749/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:14:09 | INFO | Train Epoch: 1 [ 1178112/18327966 (6%)] Loss: 0.73508 (0.7765) Data (t): 0.001 Batch (t): 0.902, 566.092/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:15:39 | INFO | Train Epoch: 1 [ 1229312/18327966 (7%)] Loss: 0.74167 (0.7752) Data (t): 0.001 Batch (t): 0.902, 568.851/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:17:11 | INFO | Train Epoch: 1 [ 1280512/18327966 (7%)] Loss: 0.73366 (0.7736) Data (t): 0.001 Batch (t): 0.923, 567.999/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:18:45 | INFO | Train Epoch: 1 [ 1331712/18327966 (7%)] Loss: 0.70668 (0.7711) Data (t): 0.001 Batch (t): 0.935, 565.117/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:20:19 | INFO | Train Epoch: 1 [ 1382912/18327966 (8%)] Loss: 0.66939 (0.7674) Data (t): 0.001 Batch (t): 0.940, 569.431/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:21:49 | INFO | Train Epoch: 1 [ 1434112/18327966 (8%)] Loss: 0.77839 (0.7678) Data (t): 0.001 Batch (t): 0.903, 567.414/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:23:19 | INFO | Train Epoch: 1 [ 1485312/18327966 (8%)] Loss: 0.85789 (0.7708) Data (t): 0.001 Batch (t): 0.903, 567.382/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:24:51 | INFO | Train Epoch: 1 [ 1536512/18327966 (8%)] Loss: 0.80748 (0.7720) Data (t): 0.001 Batch (t): 0.914, 569.558/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:26:25 | INFO | Train Epoch: 1 [ 1587712/18327966 (9%)] Loss: 0.73990 (0.7710) Data (t): 0.001 Batch (t): 0.945, 567.936/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:27:59 | INFO | Train Epoch: 1 [ 1638912/18327966 (9%)] Loss: 0.70814 (0.7691) Data (t): 0.001 Batch (t): 0.940, 254.740/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:29:29 | INFO | Train Epoch: 1 [ 1690112/18327966 (9%)] Loss: 0.76392 (0.7689) Data (t): 0.001 Batch (t): 0.902, 569.439/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:30:59 | INFO | Train Epoch: 1 [ 1741312/18327966 (10%)] Loss: 0.74530 (0.7683) Data (t): 0.001 Batch (t): 0.902, 568.466/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:32:30 | INFO | Train Epoch: 1 [ 1792512/18327966 (10%)] Loss: 0.72982 (0.7672) Data (t): 0.001 Batch (t): 0.901, 567.425/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:34:05 | INFO | Train Epoch: 1 [ 1843712/18327966 (10%)] Loss: 0.71337 (0.7657) Data (t): 0.001 Batch (t): 0.958, 562.569/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:35:38 | INFO | Train Epoch: 1 [ 1894912/18327966 (10%)] Loss: 0.69241 (0.7638) Data (t): 0.001 Batch (t): 0.931, 570.951/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:37:10 | INFO | Train Epoch: 1 [ 1946112/18327966 (11%)] Loss: 0.77919 (0.7642) Data (t): 0.001 Batch (t): 0.912, 566.865/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:38:40 | INFO | Train Epoch: 1 [ 1997312/18327966 (11%)] Loss: 0.69239 (0.7624) Data (t): 0.001 Batch (t): 0.902, 569.768/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:40:10 | INFO | Train Epoch: 1 [ 2048512/18327966 (11%)] Loss: 0.80300 (0.7634) Data (t): 0.001 Batch (t): 0.902, 569.101/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:41:45 | INFO | Train Epoch: 1 [ 2099712/18327966 (11%)] Loss: 0.81423 (0.7646) Data (t): 0.001 Batch (t): 0.946, 570.328/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:43:19 | INFO | Train Epoch: 1 [ 2150912/18327966 (12%)] Loss: 0.76174 (0.7646) Data (t): 0.001 Batch (t): 0.941, 569.686/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:44:50 | INFO | Train Epoch: 1 [ 2202112/18327966 (12%)] Loss: 0.75938 (0.7644) Data (t): 0.001 Batch (t): 0.913, 566.628/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:46:20 | INFO | Train Epoch: 1 [ 2253312/18327966 (12%)] Loss: 0.75450 (0.7642) Data (t): 0.001 Batch (t): 0.903, 566.296/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:47:51 | INFO | Train Epoch: 1 [ 2304512/18327966 (13%)] Loss: 0.63414 (0.7614) Data (t): 0.001 Batch (t): 0.903, 569.744/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:49:24 | INFO | Train Epoch: 1 [ 2355712/18327966 (13%)] Loss: 0.77242 (0.7616) Data (t): 0.001 Batch (t): 0.935, 568.872/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:50:57 | INFO | Train Epoch: 1 [ 2406912/18327966 (13%)] Loss: 0.77198 (0.7618) Data (t): 0.001 Batch (t): 0.924, 566.952/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:52:31 | INFO | Train Epoch: 1 [ 2458112/18327966 (13%)] Loss: 0.80952 (0.7628) Data (t): 0.001 Batch (t): 0.941, 566.763/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:54:01 | INFO | Train Epoch: 1 [ 2509312/18327966 (14%)] Loss: 0.80309 (0.7636) Data (t): 0.001 Batch (t): 0.903, 568.227/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:55:31 | INFO | Train Epoch: 1 [ 2560512/18327966 (14%)] Loss: 0.81650 (0.7647) Data (t): 0.001 Batch (t): 0.903, 570.054/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:57:03 | INFO | Train Epoch: 1 [ 2611712/18327966 (14%)] Loss: 0.81285 (0.7656) Data (t): 0.001 Batch (t): 0.914, 566.941/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:58:37 | INFO | Train Epoch: 1 [ 2662912/18327966 (15%)] Loss: 0.79790 (0.7662) Data (t): 0.001 Batch (t): 0.946, 569.635/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:00:11 | INFO | Train Epoch: 1 [ 2714112/18327966 (15%)] Loss: 0.72922 (0.7655) Data (t): 0.001 Batch (t): 0.940, 563.433/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:01:42 | INFO | Train Epoch: 1 [ 2765312/18327966 (15%)] Loss: 0.80018 (0.7661) Data (t): 0.001 Batch (t): 0.903, 566.272/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:03:12 | INFO | Train Epoch: 1 [ 2816512/18327966 (15%)] Loss: 0.76574 (0.7661) Data (t): 0.001 Batch (t): 0.903, 566.946/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:04:43 | INFO | Train Epoch: 1 [ 2867712/18327966 (16%)] Loss: 0.79745 (0.7667) Data (t): 0.001 Batch (t): 0.913, 569.505/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:06:18 | INFO | Train Epoch: 1 [ 2918912/18327966 (16%)] Loss: 0.74287 (0.7663) Data (t): 0.001 Batch (t): 0.947, 567.485/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:07:51 | INFO | Train Epoch: 1 [ 2970112/18327966 (16%)] Loss: 0.83661 (0.7675) Data (t): 0.001 Batch (t): 0.929, 567.974/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:09:22 | INFO | Train Epoch: 1 [ 3021312/18327966 (16%)] Loss: 0.72381 (0.7667) Data (t): 0.001 Batch (t): 0.913, 570.982/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:10:52 | INFO | Train Epoch: 1 [ 3072512/18327966 (17%)] Loss: 0.83130 (0.7678) Data (t): 0.001 Batch (t): 0.902, 567.232/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:12:23 | INFO | Train Epoch: 1 [ 3123712/18327966 (17%)] Loss: 0.75647 (0.7676) Data (t): 0.001 Batch (t): 0.902, 567.336/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:13:58 | INFO | Train Epoch: 1 [ 3174912/18327966 (17%)] Loss: 0.81896 (0.7684) Data (t): 0.001 Batch (t): 0.957, 570.140/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:15:31 | INFO | Train Epoch: 1 [ 3226112/18327966 (18%)] Loss: 0.83003 (0.7694) Data (t): 0.001 Batch (t): 0.930, 564.715/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:17:03 | INFO | Train Epoch: 1 [ 3277312/18327966 (18%)] Loss: 0.80598 (0.7699) Data (t): 0.001 Batch (t): 0.914, 568.292/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:18:33 | INFO | Train Epoch: 1 [ 3328512/18327966 (18%)] Loss: 0.80928 (0.7705) Data (t): 0.001 Batch (t): 0.902, 569.440/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:20:03 | INFO | Train Epoch: 1 [ 3379712/18327966 (18%)] Loss: 0.81508 (0.7712) Data (t): 0.001 Batch (t): 0.901, 567.700/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:21:36 | INFO | Train Epoch: 1 [ 3430912/18327966 (19%)] Loss: 0.91892 (0.7734) Data (t): 0.001 Batch (t): 0.935, 568.093/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:23:12 | INFO | Train Epoch: 1 [ 3482112/18327966 (19%)] Loss: 0.72814 (0.7727) Data (t): 0.001 Batch (t): 0.951, 569.569/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:24:43 | INFO | Train Epoch: 1 [ 3533312/18327966 (19%)] Loss: 0.75677 (0.7725) Data (t): 0.001 Batch (t): 0.913, 567.794/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:26:13 | INFO | Train Epoch: 1 [ 3584512/18327966 (20%)] Loss: 0.81840 (0.7731) Data (t): 0.001 Batch (t): 0.902, 566.351/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:27:43 | INFO | Train Epoch: 1 [ 3635712/18327966 (20%)] Loss: 0.62239 (0.7710) Data (t): 0.001 Batch (t): 0.902, 567.052/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:29:16 | INFO | Train Epoch: 1 [ 3686912/18327966 (20%)] Loss: 0.79254 (0.7713) Data (t): 0.001 Batch (t): 0.925, 254.521/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:30:49 | INFO | Train Epoch: 1 [ 3738112/18327966 (20%)] Loss: 0.78022 (0.7715) Data (t): 0.001 Batch (t): 0.936, 567.542/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:32:23 | INFO | Train Epoch: 1 [ 3789312/18327966 (21%)] Loss: 0.71234 (0.7707) Data (t): 0.001 Batch (t): 0.941, 567.065/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:33:54 | INFO | Train Epoch: 1 [ 3840512/18327966 (21%)] Loss: 0.82171 (0.7713) Data (t): 0.001 Batch (t): 0.902, 566.307/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:35:24 | INFO | Train Epoch: 1 [ 3891712/18327966 (21%)] Loss: 0.84940 (0.7724) Data (t): 0.001 Batch (t): 0.902, 567.137/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:36:55 | INFO | Train Epoch: 1 [ 3942912/18327966 (22%)] Loss: 0.75013 (0.7721) Data (t): 0.001 Batch (t): 0.913, 568.988/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:38:30 | INFO | Train Epoch: 1 [ 3994112/18327966 (22%)] Loss: 0.76843 (0.7720) Data (t): 0.001 Batch (t): 0.946, 569.440/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:40:04 | INFO | Train Epoch: 1 [ 4045312/18327966 (22%)] Loss: 0.74669 (0.7717) Data (t): 0.001 Batch (t): 0.940, 569.762/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:41:34 | INFO | Train Epoch: 1 [ 4096512/18327966 (22%)] Loss: 0.76305 (0.7716) Data (t): 0.001 Batch (t): 0.903, 568.321/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:43:04 | INFO | Train Epoch: 1 [ 4147712/18327966 (23%)] Loss: 0.79443 (0.7719) Data (t): 0.001 Batch (t): 0.903, 568.299/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:44:36 | INFO | Train Epoch: 1 [ 4198912/18327966 (23%)] Loss: 0.70574 (0.7711) Data (t): 0.001 Batch (t): 0.913, 568.842/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:46:09 | INFO | Train Epoch: 1 [ 4250112/18327966 (23%)] Loss: 0.73175 (0.7706) Data (t): 0.001 Batch (t): 0.935, 570.184/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:47:42 | INFO | Train Epoch: 1 [ 4301312/18327966 (23%)] Loss: 0.84528 (0.7715) Data (t): 0.001 Batch (t): 0.928, 569.369/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:49:13 | INFO | Train Epoch: 1 [ 4352512/18327966 (24%)] Loss: 0.71833 (0.7709) Data (t): 0.001 Batch (t): 0.912, 571.309/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:50:43 | INFO | Train Epoch: 1 [ 4403712/18327966 (24%)] Loss: 0.79363 (0.7711) Data (t): 0.001 Batch (t): 0.902, 568.283/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:52:14 | INFO | Train Epoch: 1 [ 4454912/18327966 (24%)] Loss: 0.63096 (0.7695) Data (t): 0.001 Batch (t): 0.902, 567.808/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:53:49 | INFO | Train Epoch: 1 [ 4506112/18327966 (25%)] Loss: 0.68798 (0.7686) Data (t): 0.001 Batch (t): 0.959, 569.694/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:55:22 | INFO | Train Epoch: 1 [ 4557312/18327966 (25%)] Loss: 0.74462 (0.7684) Data (t): 0.001 Batch (t): 0.930, 569.207/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:56:54 | INFO | Train Epoch: 1 [ 4608512/18327966 (25%)] Loss: 0.80367 (0.7688) Data (t): 0.001 Batch (t): 0.913, 568.375/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:58:24 | INFO | Train Epoch: 1 [ 4659712/18327966 (25%)] Loss: 0.70495 (0.7681) Data (t): 0.001 Batch (t): 0.903, 566.258/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:59:54 | INFO | Train Epoch: 1 [ 4710912/18327966 (26%)] Loss: 0.81975 (0.7686) Data (t): 0.001 Batch (t): 0.903, 572.083/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,01:01:27 | INFO | Train Epoch: 1 [ 4762112/18327966 (26%)] Loss: 0.78332 (0.7688) Data (t): 0.001 Batch (t): 0.924, 569.301/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,01:03:02 | INFO | Train Epoch: 1 [ 4813312/18327966 (26%)] Loss: 0.76812 (0.7688) Data (t): 0.001 Batch (t): 0.949, 569.821/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,01:04:34 | INFO | Train Epoch: 1 [ 4864512/18327966 (27%)] Loss: 0.88933 (0.7700) Data (t): 0.001 Batch (t): 0.924, 567.584/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,01:06:04 | INFO | Train Epoch: 1 [ 4915712/18327966 (27%)] Loss: 0.78188 (0.7701) Data (t): 0.001 Batch (t): 0.902, 562.978/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,01:07:34 | INFO | Train Epoch: 1 [ 4966912/18327966 (27%)] Loss: 0.73355 (0.7698) Data (t): 0.001 Batch (t): 0.902, 567.554/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,01:09:07 | INFO | Train Epoch: 1 [ 5018112/18327966 (27%)] Loss: 0.83679 (0.7704) Data (t): 0.001 Batch (t): 0.924, 568.252/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,01:10:40 | INFO | Train Epoch: 1 [ 5069312/18327966 (28%)] Loss: 0.73067 (0.7700) Data (t): 0.001 Batch (t): 0.935, 568.742/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,01:12:14 | INFO | Train Epoch: 1 [ 5120512/18327966 (28%)] Loss: 0.79785 (0.7703) Data (t): 0.001 Batch (t): 0.940, 567.226/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,01:13:44 | INFO | Train Epoch: 1 [ 5171712/18327966 (28%)] Loss: 0.80355 (0.7706) Data (t): 0.001 Batch (t): 0.901, 570.202/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,01:15:14 | INFO | Train Epoch: 1 [ 5222912/18327966 (28%)] Loss: 0.71711 (0.7701) Data (t): 0.001 Batch (t): 0.901, 568.601/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,01:16:46 | INFO | Train Epoch: 1 [ 5274112/18327966 (29%)] Loss: 0.79953 (0.7704) Data (t): 0.001 Batch (t): 0.912, 565.028/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,01:18:20 | INFO | Train Epoch: 1 [ 5325312/18327966 (29%)] Loss: 0.73882 (0.7701) Data (t): 0.001 Batch (t): 0.946, 571.830/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,01:19:53 | INFO | Train Epoch: 1 [ 5376512/18327966 (29%)] Loss: 0.75566 (0.7700) Data (t): 0.001 Batch (t): 0.930, 567.807/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,01:21:25 | INFO | Train Epoch: 1 [ 5427712/18327966 (30%)] Loss: 0.79569 (0.7702) Data (t): 0.001 Batch (t): 0.914, 566.772/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,01:22:55 | INFO | Train Epoch: 1 [ 5478912/18327966 (30%)] Loss: 0.84524 (0.7709) Data (t): 0.001 Batch (t): 0.904, 566.810/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,01:24:26 | INFO | Train Epoch: 1 [ 5530112/18327966 (30%)] Loss: 0.80292 (0.7712) Data (t): 0.001 Batch (t): 0.914, 571.848/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,01:26:01 | INFO | Train Epoch: 1 [ 5581312/18327966 (30%)] Loss: 0.78571 (0.7713) Data (t): 0.000 Batch (t): 0.946, 568.811/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,01:27:34 | INFO | Train Epoch: 1 [ 5632512/18327966 (31%)] Loss: 0.82602 (0.7718) Data (t): 0.001 Batch (t): 0.929, 565.346/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,01:29:05 | INFO | Train Epoch: 1 [ 5683712/18327966 (31%)] Loss: 0.84753 (0.7725) Data (t): 0.001 Batch (t): 0.913, 566.751/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,01:30:35 | INFO | Train Epoch: 1 [ 5734912/18327966 (31%)] Loss: 0.77056 (0.7725) Data (t): 0.001 Batch (t): 0.902, 568.039/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,01:32:06 | INFO | Train Epoch: 1 [ 5786112/18327966 (32%)] Loss: 0.64779 (0.7714) Data (t): 0.001 Batch (t): 0.902, 569.472/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,01:33:41 | INFO | Train Epoch: 1 [ 5837312/18327966 (32%)] Loss: 0.73644 (0.7711) Data (t): 0.001 Batch (t): 0.957, 569.859/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,01:35:14 | INFO | Train Epoch: 1 [ 5888512/18327966 (32%)] Loss: 0.78317 (0.7712) Data (t): 0.001 Batch (t): 0.928, 569.699/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,01:36:46 | INFO | Train Epoch: 1 [ 5939712/18327966 (32%)] Loss: 0.80037 (0.7714) Data (t): 0.001 Batch (t): 0.914, 566.675/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,01:38:16 | INFO | Train Epoch: 1 [ 5990912/18327966 (33%)] Loss: 0.76643 (0.7714) Data (t): 0.001 Batch (t): 0.903, 566.576/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,01:39:46 | INFO | Train Epoch: 1 [ 6042112/18327966 (33%)] Loss: 0.77077 (0.7714) Data (t): 0.001 Batch (t): 0.902, 570.037/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,01:41:18 | INFO | Train Epoch: 1 [ 6093312/18327966 (33%)] Loss: 0.70518 (0.7708) Data (t): 0.001 Batch (t): 0.923, 571.107/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,01:42:53 | INFO | Train Epoch: 1 [ 6144512/18327966 (34%)] Loss: 0.80253 (0.7711) Data (t): 0.001 Batch (t): 0.952, 567.312/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,01:44:26 | INFO | Train Epoch: 1 [ 6195712/18327966 (34%)] Loss: 0.71434 (0.7706) Data (t): 0.001 Batch (t): 0.924, 570.364/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,01:45:56 | INFO | Train Epoch: 1 [ 6246912/18327966 (34%)] Loss: 0.77912 (0.7707) Data (t): 0.000 Batch (t): 0.902, 565.041/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,01:47:26 | INFO | Train Epoch: 1 [ 6298112/18327966 (34%)] Loss: 0.72351 (0.7703) Data (t): 0.001 Batch (t): 0.902, 566.639/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,01:48:57 | INFO | Train Epoch: 1 [ 6349312/18327966 (35%)] Loss: 0.76347 (0.7703) Data (t): 0.001 Batch (t): 0.912, 568.563/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,01:50:32 | INFO | Train Epoch: 1 [ 6400512/18327966 (35%)] Loss: 0.86229 (0.7710) Data (t): 0.001 Batch (t): 0.946, 569.308/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,01:52:06 | INFO | Train Epoch: 1 [ 6451712/18327966 (35%)] Loss: 0.69808 (0.7704) Data (t): 0.001 Batch (t): 0.941, 567.463/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,01:53:36 | INFO | Train Epoch: 1 [ 6502912/18327966 (35%)] Loss: 0.80064 (0.7707) Data (t): 0.001 Batch (t): 0.901, 567.276/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,01:55:07 | INFO | Train Epoch: 1 [ 6554112/18327966 (36%)] Loss: 0.83196 (0.7711) Data (t): 0.001 Batch (t): 0.902, 567.739/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,01:56:38 | INFO | Train Epoch: 1 [ 6605312/18327966 (36%)] Loss: 0.69468 (0.7706) Data (t): 0.001 Batch (t): 0.913, 569.179/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,01:58:12 | INFO | Train Epoch: 1 [ 6656512/18327966 (36%)] Loss: 0.78858 (0.7707) Data (t): 0.001 Batch (t): 0.946, 570.571/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,01:59:45 | INFO | Train Epoch: 1 [ 6707712/18327966 (37%)] Loss: 0.74877 (0.7705) Data (t): 0.001 Batch (t): 0.930, 569.370/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,02:01:17 | INFO | Train Epoch: 1 [ 6758912/18327966 (37%)] Loss: 0.72607 (0.7702) Data (t): 0.001 Batch (t): 0.913, 568.853/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,02:02:47 | INFO | Train Epoch: 1 [ 6810112/18327966 (37%)] Loss: 0.78981 (0.7703) Data (t): 0.001 Batch (t): 0.901, 567.677/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,02:04:18 | INFO | Train Epoch: 1 [ 6861312/18327966 (37%)] Loss: 0.76062 (0.7703) Data (t): 0.001 Batch (t): 0.913, 569.083/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,02:05:52 | INFO | Train Epoch: 1 [ 6912512/18327966 (38%)] Loss: 0.78675 (0.7704) Data (t): 0.001 Batch (t): 0.935, 571.092/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,02:07:26 | INFO | Train Epoch: 1 [ 6963712/18327966 (38%)] Loss: 0.77061 (0.7704) Data (t): 0.001 Batch (t): 0.941, 568.949/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,02:08:57 | INFO | Train Epoch: 1 [ 7014912/18327966 (38%)] Loss: 0.72906 (0.7701) Data (t): 0.001 Batch (t): 0.913, 568.277/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,02:10:27 | INFO | Train Epoch: 1 [ 7066112/18327966 (39%)] Loss: 0.73280 (0.7698) Data (t): 0.001 Batch (t): 0.902, 567.592/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,02:11:57 | INFO | Train Epoch: 1 [ 7117312/18327966 (39%)] Loss: 0.76718 (0.7698) Data (t): 0.001 Batch (t): 0.902, 567.647/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,02:13:32 | INFO | Train Epoch: 1 [ 7168512/18327966 (39%)] Loss: 0.65601 (0.7690) Data (t): 0.001 Batch (t): 0.947, 571.287/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,02:15:05 | INFO | Train Epoch: 1 [ 7219712/18327966 (39%)] Loss: 0.71547 (0.7686) Data (t): 0.001 Batch (t): 0.929, 565.674/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,02:16:38 | INFO | Train Epoch: 1 [ 7270912/18327966 (40%)] Loss: 0.67298 (0.7679) Data (t): 0.001 Batch (t): 0.925, 571.142/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,02:18:08 | INFO | Train Epoch: 1 [ 7322112/18327966 (40%)] Loss: 0.74284 (0.7678) Data (t): 0.001 Batch (t): 0.902, 567.706/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,02:19:38 | INFO | Train Epoch: 1 [ 7373312/18327966 (40%)] Loss: 0.82418 (0.7682) Data (t): 0.001 Batch (t): 0.902, 567.354/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,02:21:10 | INFO | Train Epoch: 1 [ 7424512/18327966 (41%)] Loss: 0.68954 (0.7676) Data (t): 0.001 Batch (t): 0.924, 569.221/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,02:22:46 | INFO | Train Epoch: 1 [ 7475712/18327966 (41%)] Loss: 0.60823 (0.7665) Data (t): 0.001 Batch (t): 0.952, 567.964/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,02:24:18 | INFO | Train Epoch: 1 [ 7526912/18327966 (41%)] Loss: 0.76344 (0.7665) Data (t): 0.001 Batch (t): 0.925, 570.452/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,02:25:48 | INFO | Train Epoch: 1 [ 7578112/18327966 (41%)] Loss: 0.67961 (0.7659) Data (t): 0.001 Batch (t): 0.902, 566.780/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,02:27:18 | INFO | Train Epoch: 1 [ 7629312/18327966 (42%)] Loss: 0.77705 (0.7660) Data (t): 0.001 Batch (t): 0.903, 569.279/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,02:28:51 | INFO | Train Epoch: 1 [ 7680512/18327966 (42%)] Loss: 0.85686 (0.7666) Data (t): 0.001 Batch (t): 0.926, 567.771/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,02:30:25 | INFO | Train Epoch: 1 [ 7731712/18327966 (42%)] Loss: 0.69498 (0.7661) Data (t): 0.001 Batch (t): 0.937, 566.425/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,02:31:59 | INFO | Train Epoch: 1 [ 7782912/18327966 (42%)] Loss: 0.76478 (0.7661) Data (t): 0.001 Batch (t): 0.942, 572.146/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,02:33:29 | INFO | Train Epoch: 1 [ 7834112/18327966 (43%)] Loss: 0.78361 (0.7662) Data (t): 0.001 Batch (t): 0.901, 568.753/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,02:34:59 | INFO | Train Epoch: 1 [ 7885312/18327966 (43%)] Loss: 0.71920 (0.7659) Data (t): 0.001 Batch (t): 0.903, 565.226/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,02:36:31 | INFO | Train Epoch: 1 [ 7936512/18327966 (43%)] Loss: 0.75200 (0.7659) Data (t): 0.001 Batch (t): 0.914, 567.408/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,02:38:04 | INFO | Train Epoch: 1 [ 7987712/18327966 (44%)] Loss: 0.75940 (0.7658) Data (t): 0.001 Batch (t): 0.937, 568.840/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,02:39:40 | INFO | Train Epoch: 1 [ 8038912/18327966 (44%)] Loss: 0.74624 (0.7657) Data (t): 0.001 Batch (t): 0.952, 570.531/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,02:41:10 | INFO | Train Epoch: 1 [ 8090112/18327966 (44%)] Loss: 0.70243 (0.7653) Data (t): 0.001 Batch (t): 0.902, 565.691/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,02:42:40 | INFO | Train Epoch: 1 [ 8141312/18327966 (44%)] Loss: 0.76937 (0.7653) Data (t): 0.001 Batch (t): 0.902, 568.017/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,02:44:11 | INFO | Train Epoch: 1 [ 8192512/18327966 (45%)] Loss: 0.71988 (0.7650) Data (t): 0.001 Batch (t): 0.913, 568.528/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,02:45:45 | INFO | Train Epoch: 1 [ 8243712/18327966 (45%)] Loss: 0.70329 (0.7647) Data (t): 0.001 Batch (t): 0.936, 570.028/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,02:47:19 | INFO | Train Epoch: 1 [ 8294912/18327966 (45%)] Loss: 0.66753 (0.7641) Data (t): 0.001 Batch (t): 0.941, 567.922/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,02:48:50 | INFO | Train Epoch: 1 [ 8346112/18327966 (46%)] Loss: 0.72344 (0.7638) Data (t): 0.001 Batch (t): 0.913, 570.504/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,02:50:20 | INFO | Train Epoch: 1 [ 8397312/18327966 (46%)] Loss: 0.71605 (0.7635) Data (t): 0.001 Batch (t): 0.901, 567.661/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,02:51:52 | INFO | Train Epoch: 1 [ 8448512/18327966 (46%)] Loss: 0.87220 (0.7642) Data (t): 0.001 Batch (t): 0.912, 251.224/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,02:53:24 | INFO | Train Epoch: 1 [ 8499712/18327966 (46%)] Loss: 0.74941 (0.7641) Data (t): 0.001 Batch (t): 0.925, 565.451/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,02:54:59 | INFO | Train Epoch: 1 [ 8550912/18327966 (47%)] Loss: 0.62858 (0.7633) Data (t): 0.001 Batch (t): 0.952, 570.314/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,02:56:31 | INFO | Train Epoch: 1 [ 8602112/18327966 (47%)] Loss: 0.80467 (0.7635) Data (t): 0.001 Batch (t): 0.913, 567.706/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,02:58:01 | INFO | Train Epoch: 1 [ 8653312/18327966 (47%)] Loss: 0.68827 (0.7631) Data (t): 0.001 Batch (t): 0.902, 568.121/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,02:59:31 | INFO | Train Epoch: 1 [ 8704512/18327966 (47%)] Loss: 0.77834 (0.7632) Data (t): 0.001 Batch (t): 0.902, 570.300/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,03:01:03 | INFO | Train Epoch: 1 [ 8755712/18327966 (48%)] Loss: 0.68058 (0.7627) Data (t): 0.001 Batch (t): 0.923, 570.403/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,03:02:39 | INFO | Train Epoch: 1 [ 8806912/18327966 (48%)] Loss: 0.66385 (0.7621) Data (t): 0.001 Batch (t): 0.952, 568.989/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,03:04:11 | INFO | Train Epoch: 1 [ 8858112/18327966 (48%)] Loss: 0.75063 (0.7621) Data (t): 0.001 Batch (t): 0.923, 566.517/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,03:05:41 | INFO | Train Epoch: 1 [ 8909312/18327966 (49%)] Loss: 0.76259 (0.7621) Data (t): 0.001 Batch (t): 0.902, 569.060/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,03:07:11 | INFO | Train Epoch: 1 [ 8960512/18327966 (49%)] Loss: 0.78600 (0.7622) Data (t): 0.001 Batch (t): 0.902, 567.388/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,03:08:43 | INFO | Train Epoch: 1 [ 9011712/18327966 (49%)] Loss: 0.80652 (0.7624) Data (t): 0.001 Batch (t): 0.914, 567.192/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,03:10:16 | INFO | Train Epoch: 1 [ 9062912/18327966 (49%)] Loss: 0.64013 (0.7618) Data (t): 0.001 Batch (t): 0.936, 568.447/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,03:11:52 | INFO | Train Epoch: 1 [ 9114112/18327966 (50%)] Loss: 0.68076 (0.7613) Data (t): 0.001 Batch (t): 0.954, 566.614/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,03:13:22 | INFO | Train Epoch: 1 [ 9165312/18327966 (50%)] Loss: 0.83169 (0.7617) Data (t): 0.001 Batch (t): 0.902, 570.230/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,03:14:52 | INFO | Train Epoch: 1 [ 9216512/18327966 (50%)] Loss: 0.78256 (0.7618) Data (t): 0.001 Batch (t): 0.902, 566.963/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,03:16:23 | INFO | Train Epoch: 1 [ 9267712/18327966 (51%)] Loss: 0.83630 (0.7622) Data (t): 0.001 Batch (t): 0.914, 567.637/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,03:17:57 | INFO | Train Epoch: 1 [ 9318912/18327966 (51%)] Loss: 0.70710 (0.7619) Data (t): 0.001 Batch (t): 0.936, 566.612/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,03:19:31 | INFO | Train Epoch: 1 [ 9370112/18327966 (51%)] Loss: 0.71492 (0.7617) Data (t): 0.001 Batch (t): 0.941, 567.569/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,03:21:02 | INFO | Train Epoch: 1 [ 9421312/18327966 (51%)] Loss: 0.67919 (0.7612) Data (t): 0.001 Batch (t): 0.913, 567.920/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,03:22:33 | INFO | Train Epoch: 1 [ 9472512/18327966 (52%)] Loss: 0.74813 (0.7611) Data (t): 0.001 Batch (t): 0.902, 568.717/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,03:24:04 | INFO | Train Epoch: 1 [ 9523712/18327966 (52%)] Loss: 0.71301 (0.7609) Data (t): 0.001 Batch (t): 0.914, 566.615/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,03:25:38 | INFO | Train Epoch: 1 [ 9574912/18327966 (52%)] Loss: 0.74282 (0.7608) Data (t): 0.001 Batch (t): 0.936, 563.605/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,03:27:12 | INFO | Train Epoch: 1 [ 9626112/18327966 (53%)] Loss: 0.73602 (0.7607) Data (t): 0.001 Batch (t): 0.942, 567.552/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,03:28:43 | INFO | Train Epoch: 1 [ 9677312/18327966 (53%)] Loss: 0.79285 (0.7608) Data (t): 0.001 Batch (t): 0.914, 567.694/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,03:30:13 | INFO | Train Epoch: 1 [ 9728512/18327966 (53%)] Loss: 0.81968 (0.7611) Data (t): 0.001 Batch (t): 0.902, 566.198/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,03:31:44 | INFO | Train Epoch: 1 [ 9779712/18327966 (53%)] Loss: 0.89734 (0.7618) Data (t): 0.001 Batch (t): 0.902, 567.388/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,03:33:17 | INFO | Train Epoch: 1 [ 9830912/18327966 (54%)] Loss: 0.78148 (0.7619) Data (t): 0.001 Batch (t): 0.938, 567.746/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,03:34:52 | INFO | Train Epoch: 1 [ 9882112/18327966 (54%)] Loss: 0.74429 (0.7619) Data (t): 0.001 Batch (t): 0.942, 568.614/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,03:36:24 | INFO | Train Epoch: 1 [ 9933312/18327966 (54%)] Loss: 0.72204 (0.7617) Data (t): 0.001 Batch (t): 0.925, 567.745/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,03:37:54 | INFO | Train Epoch: 1 [ 9984512/18327966 (54%)] Loss: 0.79067 (0.7618) Data (t): 0.001 Batch (t): 0.902, 566.295/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,03:39:24 | INFO | Train Epoch: 1 [10035712/18327966 (55%)] Loss: 0.72603 (0.7616) Data (t): 0.001 Batch (t): 0.900, 567.323/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,03:40:57 | INFO | Train Epoch: 1 [10086912/18327966 (55%)] Loss: 0.66679 (0.7611) Data (t): 0.001 Batch (t): 0.925, 567.661/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,03:42:31 | INFO | Train Epoch: 1 [10138112/18327966 (55%)] Loss: 0.74965 (0.7611) Data (t): 0.001 Batch (t): 0.941, 568.938/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,03:44:04 | INFO | Train Epoch: 1 [10189312/18327966 (56%)] Loss: 0.83213 (0.7614) Data (t): 0.001 Batch (t): 0.936, 569.528/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,03:45:35 | INFO | Train Epoch: 1 [10240512/18327966 (56%)] Loss: 0.70084 (0.7611) Data (t): 0.001 Batch (t): 0.901, 569.764/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,03:47:05 | INFO | Train Epoch: 1 [10291712/18327966 (56%)] Loss: 0.74958 (0.7611) Data (t): 0.001 Batch (t): 0.901, 570.025/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,03:48:36 | INFO | Train Epoch: 1 [10342912/18327966 (56%)] Loss: 0.76385 (0.7611) Data (t): 0.001 Batch (t): 0.913, 564.247/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,03:50:10 | INFO | Train Epoch: 1 [10394112/18327966 (57%)] Loss: 0.74774 (0.7610) Data (t): 0.001 Batch (t): 0.936, 571.099/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,03:51:45 | INFO | Train Epoch: 1 [10445312/18327966 (57%)] Loss: 0.75862 (0.7610) Data (t): 0.001 Batch (t): 0.953, 567.411/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,03:53:15 | INFO | Train Epoch: 1 [10496512/18327966 (57%)] Loss: 0.75863 (0.7610) Data (t): 0.001 Batch (t): 0.903, 567.130/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,03:54:45 | INFO | Train Epoch: 1 [10547712/18327966 (58%)] Loss: 0.78722 (0.7611) Data (t): 0.001 Batch (t): 0.903, 568.021/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,03:56:17 | INFO | Train Epoch: 1 [10598912/18327966 (58%)] Loss: 0.68112 (0.7607) Data (t): 0.001 Batch (t): 0.914, 567.603/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,03:57:51 | INFO | Train Epoch: 1 [10650112/18327966 (58%)] Loss: 0.72074 (0.7606) Data (t): 0.001 Batch (t): 0.938, 565.243/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,03:59:25 | INFO | Train Epoch: 1 [10701312/18327966 (58%)] Loss: 0.75989 (0.7606) Data (t): 0.001 Batch (t): 0.943, 569.063/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,04:00:56 | INFO | Train Epoch: 1 [10752512/18327966 (59%)] Loss: 0.87568 (0.7611) Data (t): 0.001 Batch (t): 0.914, 567.488/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,04:02:27 | INFO | Train Epoch: 1 [10803712/18327966 (59%)] Loss: 0.81347 (0.7613) Data (t): 0.001 Batch (t): 0.902, 568.952/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,04:03:58 | INFO | Train Epoch: 1 [10854912/18327966 (59%)] Loss: 0.75481 (0.7613) Data (t): 0.001 Batch (t): 0.914, 567.182/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,04:05:32 | INFO | Train Epoch: 1 [10906112/18327966 (60%)] Loss: 0.68278 (0.7609) Data (t): 0.001 Batch (t): 0.937, 569.082/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,04:07:06 | INFO | Train Epoch: 1 [10957312/18327966 (60%)] Loss: 0.75836 (0.7609) Data (t): 0.001 Batch (t): 0.942, 569.038/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,04:08:37 | INFO | Train Epoch: 1 [11008512/18327966 (60%)] Loss: 0.71617 (0.7607) Data (t): 0.001 Batch (t): 0.913, 564.631/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,04:10:07 | INFO | Train Epoch: 1 [11059712/18327966 (60%)] Loss: 0.89163 (0.7613) Data (t): 0.001 Batch (t): 0.902, 567.936/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,04:11:38 | INFO | Train Epoch: 1 [11110912/18327966 (61%)] Loss: 0.77167 (0.7614) Data (t): 0.001 Batch (t): 0.903, 568.185/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,04:13:10 | INFO | Train Epoch: 1 [11162112/18327966 (61%)] Loss: 0.74306 (0.7613) Data (t): 0.001 Batch (t): 0.926, 565.854/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,04:14:45 | INFO | Train Epoch: 1 [11213312/18327966 (61%)] Loss: 0.84403 (0.7617) Data (t): 0.001 Batch (t): 0.942, 570.988/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,04:16:18 | INFO | Train Epoch: 1 [11264512/18327966 (61%)] Loss: 0.72781 (0.7615) Data (t): 0.001 Batch (t): 0.937, 568.772/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,04:17:48 | INFO | Train Epoch: 1 [11315712/18327966 (62%)] Loss: 0.68923 (0.7612) Data (t): 0.001 Batch (t): 0.902, 566.269/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,04:19:19 | INFO | Train Epoch: 1 [11366912/18327966 (62%)] Loss: 0.77030 (0.7612) Data (t): 0.001 Batch (t): 0.901, 564.799/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,04:20:51 | INFO | Train Epoch: 1 [11418112/18327966 (62%)] Loss: 0.87062 (0.7617) Data (t): 0.001 Batch (t): 0.926, 564.932/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,04:22:25 | INFO | Train Epoch: 1 [11469312/18327966 (63%)] Loss: 0.73021 (0.7616) Data (t): 0.001 Batch (t): 0.942, 569.616/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,04:23:59 | INFO | Train Epoch: 1 [11520512/18327966 (63%)] Loss: 0.74498 (0.7615) Data (t): 0.001 Batch (t): 0.937, 568.979/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,04:25:29 | INFO | Train Epoch: 1 [11571712/18327966 (63%)] Loss: 0.60758 (0.7608) Data (t): 0.001 Batch (t): 0.902, 568.521/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,04:26:59 | INFO | Train Epoch: 1 [11622912/18327966 (63%)] Loss: 0.74072 (0.7607) Data (t): 0.001 Batch (t): 0.902, 569.854/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,04:28:31 | INFO | Train Epoch: 1 [11674112/18327966 (64%)] Loss: 0.66307 (0.7603) Data (t): 0.001 Batch (t): 0.913, 568.622/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,04:30:04 | INFO | Train Epoch: 1 [11725312/18327966 (64%)] Loss: 0.87917 (0.7608) Data (t): 0.001 Batch (t): 0.937, 569.069/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,04:31:39 | INFO | Train Epoch: 1 [11776512/18327966 (64%)] Loss: 0.81668 (0.7611) Data (t): 0.001 Batch (t): 0.942, 562.908/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,04:33:09 | INFO | Train Epoch: 1 [11827712/18327966 (65%)] Loss: 0.68345 (0.7607) Data (t): 0.001 Batch (t): 0.902, 568.507/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,04:34:39 | INFO | Train Epoch: 1 [11878912/18327966 (65%)] Loss: 0.71532 (0.7605) Data (t): 0.001 Batch (t): 0.902, 569.138/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,04:36:11 | INFO | Train Epoch: 1 [11930112/18327966 (65%)] Loss: 0.71485 (0.7603) Data (t): 0.001 Batch (t): 0.914, 566.452/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,04:37:44 | INFO | Train Epoch: 1 [11981312/18327966 (65%)] Loss: 0.75500 (0.7603) Data (t): 0.001 Batch (t): 0.937, 572.098/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,04:39:17 | INFO | Train Epoch: 1 [12032512/18327966 (66%)] Loss: 0.72133 (0.7602) Data (t): 0.001 Batch (t): 0.930, 567.889/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,04:40:48 | INFO | Train Epoch: 1 [12083712/18327966 (66%)] Loss: 0.59928 (0.7595) Data (t): 0.001 Batch (t): 0.913, 569.364/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,04:42:19 | INFO | Train Epoch: 1 [12134912/18327966 (66%)] Loss: 0.82617 (0.7598) Data (t): 0.001 Batch (t): 0.902, 566.641/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,04:43:50 | INFO | Train Epoch: 1 [12186112/18327966 (66%)] Loss: 0.69662 (0.7595) Data (t): 0.001 Batch (t): 0.913, 570.837/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,04:45:23 | INFO | Train Epoch: 1 [12237312/18327966 (67%)] Loss: 0.77415 (0.7596) Data (t): 0.001 Batch (t): 0.926, 243.818/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,04:46:57 | INFO | Train Epoch: 1 [12288512/18327966 (67%)] Loss: 0.89576 (0.7601) Data (t): 0.001 Batch (t): 0.942, 570.586/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,04:48:29 | INFO | Train Epoch: 1 [12339712/18327966 (67%)] Loss: 0.66949 (0.7597) Data (t): 0.001 Batch (t): 0.924, 567.843/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,04:49:59 | INFO | Train Epoch: 1 [12390912/18327966 (68%)] Loss: 0.73130 (0.7596) Data (t): 0.001 Batch (t): 0.901, 570.282/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,04:51:29 | INFO | Train Epoch: 1 [12442112/18327966 (68%)] Loss: 0.72415 (0.7595) Data (t): 0.001 Batch (t): 0.902, 567.164/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,04:53:02 | INFO | Train Epoch: 1 [12493312/18327966 (68%)] Loss: 0.76717 (0.7595) Data (t): 0.001 Batch (t): 0.925, 567.566/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,04:54:36 | INFO | Train Epoch: 1 [12544512/18327966 (68%)] Loss: 0.75341 (0.7595) Data (t): 0.001 Batch (t): 0.942, 569.784/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,04:56:10 | INFO | Train Epoch: 1 [12595712/18327966 (69%)] Loss: 0.74721 (0.7594) Data (t): 0.001 Batch (t): 0.937, 568.153/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,04:57:40 | INFO | Train Epoch: 1 [12646912/18327966 (69%)] Loss: 0.68873 (0.7592) Data (t): 0.001 Batch (t): 0.901, 567.679/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,04:59:10 | INFO | Train Epoch: 1 [12698112/18327966 (69%)] Loss: 0.63007 (0.7586) Data (t): 0.001 Batch (t): 0.903, 566.364/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,05:00:43 | INFO | Train Epoch: 1 [12749312/18327966 (70%)] Loss: 0.73757 (0.7586) Data (t): 0.001 Batch (t): 0.926, 570.339/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,05:02:17 | INFO | Train Epoch: 1 [12800512/18327966 (70%)] Loss: 0.83712 (0.7589) Data (t): 0.001 Batch (t): 0.942, 570.178/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,05:03:51 | INFO | Train Epoch: 1 [12851712/18327966 (70%)] Loss: 0.68433 (0.7586) Data (t): 0.001 Batch (t): 0.937, 568.152/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,05:05:21 | INFO | Train Epoch: 1 [12902912/18327966 (70%)] Loss: 0.82440 (0.7588) Data (t): 0.001 Batch (t): 0.902, 569.708/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,05:06:51 | INFO | Train Epoch: 1 [12954112/18327966 (71%)] Loss: 0.72462 (0.7587) Data (t): 0.001 Batch (t): 0.901, 569.295/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,05:08:22 | INFO | Train Epoch: 1 [13005312/18327966 (71%)] Loss: 0.76812 (0.7587) Data (t): 0.001 Batch (t): 0.913, 571.909/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,05:09:56 | INFO | Train Epoch: 1 [13056512/18327966 (71%)] Loss: 0.77240 (0.7588) Data (t): 0.001 Batch (t): 0.938, 567.924/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,05:11:32 | INFO | Train Epoch: 1 [13107712/18327966 (72%)] Loss: 0.69051 (0.7585) Data (t): 0.001 Batch (t): 0.955, 568.983/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,05:13:02 | INFO | Train Epoch: 1 [13158912/18327966 (72%)] Loss: 0.82155 (0.7588) Data (t): 0.001 Batch (t): 0.902, 566.698/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,05:14:32 | INFO | Train Epoch: 1 [13210112/18327966 (72%)] Loss: 0.74879 (0.7587) Data (t): 0.001 Batch (t): 0.903, 566.765/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,05:16:03 | INFO | Train Epoch: 1 [13261312/18327966 (72%)] Loss: 0.82458 (0.7590) Data (t): 0.001 Batch (t): 0.914, 565.333/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,05:17:37 | INFO | Train Epoch: 1 [13312512/18327966 (73%)] Loss: 0.79865 (0.7591) Data (t): 0.001 Batch (t): 0.936, 570.563/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,05:19:10 | INFO | Train Epoch: 1 [13363712/18327966 (73%)] Loss: 0.68723 (0.7589) Data (t): 0.001 Batch (t): 0.930, 566.111/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,05:20:43 | INFO | Train Epoch: 1 [13414912/18327966 (73%)] Loss: 0.69645 (0.7586) Data (t): 0.001 Batch (t): 0.925, 569.407/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,05:22:13 | INFO | Train Epoch: 1 [13466112/18327966 (73%)] Loss: 0.80339 (0.7588) Data (t): 0.001 Batch (t): 0.902, 568.822/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,05:23:44 | INFO | Train Epoch: 1 [13517312/18327966 (74%)] Loss: 0.72736 (0.7587) Data (t): 0.001 Batch (t): 0.914, 567.121/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,05:25:16 | INFO | Train Epoch: 1 [13568512/18327966 (74%)] Loss: 0.60992 (0.7581) Data (t): 0.001 Batch (t): 0.914, 567.588/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,05:26:50 | INFO | Train Epoch: 1 [13619712/18327966 (74%)] Loss: 0.75497 (0.7581) Data (t): 0.001 Batch (t): 0.943, 569.626/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,05:28:24 | INFO | Train Epoch: 1 [13670912/18327966 (75%)] Loss: 0.74217 (0.7580) Data (t): 0.001 Batch (t): 0.938, 567.202/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,05:29:54 | INFO | Train Epoch: 1 [13722112/18327966 (75%)] Loss: 0.70147 (0.7578) Data (t): 0.001 Batch (t): 0.901, 569.144/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,05:31:24 | INFO | Train Epoch: 1 [13773312/18327966 (75%)] Loss: 0.66704 (0.7575) Data (t): 0.001 Batch (t): 0.901, 569.159/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,05:32:56 | INFO | Train Epoch: 1 [13824512/18327966 (75%)] Loss: 0.62583 (0.7570) Data (t): 0.001 Batch (t): 0.925, 569.842/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,05:34:31 | INFO | Train Epoch: 1 [13875712/18327966 (76%)] Loss: 0.75134 (0.7570) Data (t): 0.001 Batch (t): 0.942, 569.089/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,05:36:04 | INFO | Train Epoch: 1 [13926912/18327966 (76%)] Loss: 0.77888 (0.7571) Data (t): 0.001 Batch (t): 0.938, 567.688/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,05:37:35 | INFO | Train Epoch: 1 [13978112/18327966 (76%)] Loss: 0.76955 (0.7571) Data (t): 0.001 Batch (t): 0.901, 567.454/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,05:39:05 | INFO | Train Epoch: 1 [14029312/18327966 (77%)] Loss: 0.81499 (0.7573) Data (t): 0.001 Batch (t): 0.902, 566.473/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,05:40:37 | INFO | Train Epoch: 1 [14080512/18327966 (77%)] Loss: 0.69881 (0.7571) Data (t): 0.001 Batch (t): 0.926, 568.894/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,05:42:12 | INFO | Train Epoch: 1 [14131712/18327966 (77%)] Loss: 0.75641 (0.7571) Data (t): 0.001 Batch (t): 0.943, 562.463/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,05:43:45 | INFO | Train Epoch: 1 [14182912/18327966 (77%)] Loss: 0.78746 (0.7572) Data (t): 0.001 Batch (t): 0.937, 565.978/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,05:45:16 | INFO | Train Epoch: 1 [14234112/18327966 (78%)] Loss: 0.72964 (0.7571) Data (t): 0.001 Batch (t): 0.902, 565.826/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,05:46:46 | INFO | Train Epoch: 1 [14285312/18327966 (78%)] Loss: 0.79143 (0.7572) Data (t): 0.001 Batch (t): 0.902, 566.125/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,05:48:17 | INFO | Train Epoch: 1 [14336512/18327966 (78%)] Loss: 0.76339 (0.7573) Data (t): 0.001 Batch (t): 0.915, 570.737/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,05:49:51 | INFO | Train Epoch: 1 [14387712/18327966 (79%)] Loss: 0.77140 (0.7573) Data (t): 0.001 Batch (t): 0.938, 564.575/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,05:51:26 | INFO | Train Epoch: 1 [14438912/18327966 (79%)] Loss: 0.75257 (0.7573) Data (t): 0.001 Batch (t): 0.944, 566.579/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,05:52:57 | INFO | Train Epoch: 1 [14490112/18327966 (79%)] Loss: 0.81327 (0.7575) Data (t): 0.001 Batch (t): 0.914, 568.358/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,05:54:27 | INFO | Train Epoch: 1 [14541312/18327966 (79%)] Loss: 0.80360 (0.7577) Data (t): 0.001 Batch (t): 0.902, 569.352/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,05:55:59 | INFO | Train Epoch: 1 [14592512/18327966 (80%)] Loss: 0.68668 (0.7574) Data (t): 0.001 Batch (t): 0.915, 569.579/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,05:57:33 | INFO | Train Epoch: 1 [14643712/18327966 (80%)] Loss: 0.73141 (0.7573) Data (t): 0.001 Batch (t): 0.939, 569.281/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,05:59:06 | INFO | Train Epoch: 1 [14694912/18327966 (80%)] Loss: 0.77205 (0.7574) Data (t): 0.001 Batch (t): 0.932, 568.143/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,06:00:38 | INFO | Train Epoch: 1 [14746112/18327966 (80%)] Loss: 0.83920 (0.7577) Data (t): 0.001 Batch (t): 0.924, 567.139/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,06:02:08 | INFO | Train Epoch: 1 [14797312/18327966 (81%)] Loss: 0.66985 (0.7574) Data (t): 0.001 Batch (t): 0.901, 570.776/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,06:03:40 | INFO | Train Epoch: 1 [14848512/18327966 (81%)] Loss: 0.68765 (0.7571) Data (t): 0.001 Batch (t): 0.915, 568.660/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,06:05:14 | INFO | Train Epoch: 1 [14899712/18327966 (81%)] Loss: 0.76835 (0.7571) Data (t): 0.001 Batch (t): 0.939, 570.662/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,06:06:46 | INFO | Train Epoch: 1 [14950912/18327966 (82%)] Loss: 0.72780 (0.7570) Data (t): 0.001 Batch (t): 0.919, 568.991/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,06:08:19 | INFO | Train Epoch: 1 [15002112/18327966 (82%)] Loss: 0.76540 (0.7571) Data (t): 0.001 Batch (t): 0.937, 570.145/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,06:09:49 | INFO | Train Epoch: 1 [15053312/18327966 (82%)] Loss: 0.68585 (0.7568) Data (t): 0.001 Batch (t): 0.900, 566.749/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,06:11:19 | INFO | Train Epoch: 1 [15104512/18327966 (82%)] Loss: 0.71854 (0.7567) Data (t): 0.001 Batch (t): 0.901, 565.299/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,06:12:52 | INFO | Train Epoch: 1 [15155712/18327966 (83%)] Loss: 0.75614 (0.7567) Data (t): 0.001 Batch (t): 0.926, 568.214/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,06:14:25 | INFO | Train Epoch: 1 [15206912/18327966 (83%)] Loss: 0.70232 (0.7565) Data (t): 0.001 Batch (t): 0.931, 567.102/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,06:15:59 | INFO | Train Epoch: 1 [15258112/18327966 (83%)] Loss: 0.66387 (0.7562) Data (t): 0.001 Batch (t): 0.939, 566.625/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,06:17:29 | INFO | Train Epoch: 1 [15309312/18327966 (84%)] Loss: 0.77989 (0.7563) Data (t): 0.001 Batch (t): 0.901, 566.780/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,06:18:59 | INFO | Train Epoch: 1 [15360512/18327966 (84%)] Loss: 0.70945 (0.7561) Data (t): 0.001 Batch (t): 0.902, 568.661/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,06:20:32 | INFO | Train Epoch: 1 [15411712/18327966 (84%)] Loss: 0.65070 (0.7558) Data (t): 0.001 Batch (t): 0.926, 569.077/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,06:22:04 | INFO | Train Epoch: 1 [15462912/18327966 (84%)] Loss: 0.73557 (0.7557) Data (t): 0.001 Batch (t): 0.924, 566.849/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,06:23:39 | INFO | Train Epoch: 1 [15514112/18327966 (85%)] Loss: 0.76619 (0.7558) Data (t): 0.001 Batch (t): 0.944, 569.297/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,06:25:10 | INFO | Train Epoch: 1 [15565312/18327966 (85%)] Loss: 0.67741 (0.7555) Data (t): 0.001 Batch (t): 0.913, 570.333/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,06:26:40 | INFO | Train Epoch: 1 [15616512/18327966 (85%)] Loss: 0.84818 (0.7558) Data (t): 0.001 Batch (t): 0.902, 564.863/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,06:28:11 | INFO | Train Epoch: 1 [15667712/18327966 (85%)] Loss: 0.75300 (0.7558) Data (t): 0.001 Batch (t): 0.913, 567.784/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,06:29:45 | INFO | Train Epoch: 1 [15718912/18327966 (86%)] Loss: 0.62735 (0.7554) Data (t): 0.001 Batch (t): 0.939, 566.388/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,06:31:18 | INFO | Train Epoch: 1 [15770112/18327966 (86%)] Loss: 0.77559 (0.7554) Data (t): 0.001 Batch (t): 0.932, 566.971/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,06:32:51 | INFO | Train Epoch: 1 [15821312/18327966 (86%)] Loss: 0.67542 (0.7552) Data (t): 0.001 Batch (t): 0.927, 563.918/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,06:34:21 | INFO | Train Epoch: 1 [15872512/18327966 (87%)] Loss: 0.69945 (0.7550) Data (t): 0.001 Batch (t): 0.901, 568.595/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,06:35:53 | INFO | Train Epoch: 1 [15923712/18327966 (87%)] Loss: 0.78780 (0.7551) Data (t): 0.001 Batch (t): 0.915, 568.543/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,06:37:27 | INFO | Train Epoch: 1 [15974912/18327966 (87%)] Loss: 0.65570 (0.7548) Data (t): 0.001 Batch (t): 0.940, 568.127/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,06:38:59 | INFO | Train Epoch: 1 [16026112/18327966 (87%)] Loss: 0.72851 (0.7547) Data (t): 0.001 Batch (t): 0.920, 567.238/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,06:40:33 | INFO | Train Epoch: 1 [16077312/18327966 (88%)] Loss: 0.81932 (0.7549) Data (t): 0.001 Batch (t): 0.940, 568.083/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,06:42:03 | INFO | Train Epoch: 1 [16128512/18327966 (88%)] Loss: 0.67098 (0.7546) Data (t): 0.001 Batch (t): 0.902, 565.535/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,06:43:34 | INFO | Train Epoch: 1 [16179712/18327966 (88%)] Loss: 0.71197 (0.7545) Data (t): 0.001 Batch (t): 0.915, 564.257/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,06:45:07 | INFO | Train Epoch: 1 [16230912/18327966 (89%)] Loss: 0.75481 (0.7545) Data (t): 0.001 Batch (t): 0.927, 571.133/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,06:46:40 | INFO | Train Epoch: 1 [16282112/18327966 (89%)] Loss: 0.75944 (0.7545) Data (t): 0.001 Batch (t): 0.932, 568.436/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,06:48:14 | INFO | Train Epoch: 1 [16333312/18327966 (89%)] Loss: 0.72424 (0.7544) Data (t): 0.001 Batch (t): 0.939, 566.883/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,06:49:44 | INFO | Train Epoch: 1 [16384512/18327966 (89%)] Loss: 0.63445 (0.7541) Data (t): 0.001 Batch (t): 0.902, 567.562/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,06:51:15 | INFO | Train Epoch: 1 [16435712/18327966 (90%)] Loss: 0.65052 (0.7537) Data (t): 0.001 Batch (t): 0.903, 566.725/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,06:52:48 | INFO | Train Epoch: 1 [16486912/18327966 (90%)] Loss: 0.68391 (0.7535) Data (t): 0.001 Batch (t): 0.928, 568.541/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,06:54:22 | INFO | Train Epoch: 1 [16538112/18327966 (90%)] Loss: 0.81143 (0.7537) Data (t): 0.001 Batch (t): 0.945, 570.086/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,06:55:55 | INFO | Train Epoch: 1 [16589312/18327966 (91%)] Loss: 0.75244 (0.7537) Data (t): 0.001 Batch (t): 0.928, 567.876/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,06:57:26 | INFO | Train Epoch: 1 [16640512/18327966 (91%)] Loss: 0.57410 (0.7531) Data (t): 0.001 Batch (t): 0.915, 568.119/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,06:58:57 | INFO | Train Epoch: 1 [16691712/18327966 (91%)] Loss: 0.74232 (0.7531) Data (t): 0.001 Batch (t): 0.903, 566.731/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,07:00:28 | INFO | Train Epoch: 1 [16742912/18327966 (91%)] Loss: 0.72581 (0.7530) Data (t): 0.001 Batch (t): 0.915, 569.359/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,07:02:04 | INFO | Train Epoch: 1 [16794112/18327966 (92%)] Loss: 0.74310 (0.7530) Data (t): 0.001 Batch (t): 0.957, 567.854/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,07:03:36 | INFO | Train Epoch: 1 [16845312/18327966 (92%)] Loss: 0.75829 (0.7530) Data (t): 0.001 Batch (t): 0.927, 566.685/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,07:05:08 | INFO | Train Epoch: 1 [16896512/18327966 (92%)] Loss: 0.79918 (0.7532) Data (t): 0.001 Batch (t): 0.914, 570.638/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,07:06:38 | INFO | Train Epoch: 1 [16947712/18327966 (92%)] Loss: 0.65581 (0.7529) Data (t): 0.001 Batch (t): 0.902, 567.353/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,07:08:09 | INFO | Train Epoch: 1 [16998912/18327966 (93%)] Loss: 0.82718 (0.7531) Data (t): 0.001 Batch (t): 0.914, 570.501/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,07:09:43 | INFO | Train Epoch: 1 [17050112/18327966 (93%)] Loss: 0.68882 (0.7529) Data (t): 0.001 Batch (t): 0.940, 569.203/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,07:11:15 | INFO | Train Epoch: 1 [17101312/18327966 (93%)] Loss: 0.75296 (0.7529) Data (t): 0.001 Batch (t): 0.919, 569.272/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,07:12:48 | INFO | Train Epoch: 1 [17152512/18327966 (94%)] Loss: 0.71368 (0.7528) Data (t): 0.001 Batch (t): 0.928, 568.571/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,07:14:18 | INFO | Train Epoch: 1 [17203712/18327966 (94%)] Loss: 0.79011 (0.7529) Data (t): 0.001 Batch (t): 0.902, 566.101/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,07:15:50 | INFO | Train Epoch: 1 [17254912/18327966 (94%)] Loss: 0.82392 (0.7531) Data (t): 0.001 Batch (t): 0.916, 564.375/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,07:17:24 | INFO | Train Epoch: 1 [17306112/18327966 (94%)] Loss: 0.81760 (0.7533) Data (t): 0.001 Batch (t): 0.940, 567.852/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,07:18:56 | INFO | Train Epoch: 1 [17357312/18327966 (95%)] Loss: 0.77263 (0.7533) Data (t): 0.001 Batch (t): 0.919, 570.007/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,07:20:30 | INFO | Train Epoch: 1 [17408512/18327966 (95%)] Loss: 0.71448 (0.7532) Data (t): 0.001 Batch (t): 0.939, 569.079/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,07:22:00 | INFO | Train Epoch: 1 [17459712/18327966 (95%)] Loss: 0.71695 (0.7531) Data (t): 0.001 Batch (t): 0.901, 569.330/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,07:23:30 | INFO | Train Epoch: 1 [17510912/18327966 (96%)] Loss: 0.76651 (0.7532) Data (t): 0.001 Batch (t): 0.902, 567.149/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,07:25:03 | INFO | Train Epoch: 1 [17562112/18327966 (96%)] Loss: 0.72139 (0.7531) Data (t): 0.001 Batch (t): 0.927, 564.611/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,07:26:37 | INFO | Train Epoch: 1 [17613312/18327966 (96%)] Loss: 0.74589 (0.7530) Data (t): 0.001 Batch (t): 0.945, 568.469/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,07:28:10 | INFO | Train Epoch: 1 [17664512/18327966 (96%)] Loss: 0.77453 (0.7531) Data (t): 0.001 Batch (t): 0.928, 570.431/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,07:29:42 | INFO | Train Epoch: 1 [17715712/18327966 (97%)] Loss: 0.67934 (0.7529) Data (t): 0.001 Batch (t): 0.915, 564.874/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,07:31:12 | INFO | Train Epoch: 1 [17766912/18327966 (97%)] Loss: 0.72088 (0.7528) Data (t): 0.001 Batch (t): 0.902, 567.405/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,07:32:45 | INFO | Train Epoch: 1 [17818112/18327966 (97%)] Loss: 0.67494 (0.7526) Data (t): 0.001 Batch (t): 0.928, 566.747/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,07:34:19 | INFO | Train Epoch: 1 [17869312/18327966 (97%)] Loss: 0.73337 (0.7525) Data (t): 0.001 Batch (t): 0.944, 567.866/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,07:35:52 | INFO | Train Epoch: 1 [17920512/18327966 (98%)] Loss: 0.89323 (0.7529) Data (t): 0.001 Batch (t): 0.927, 565.935/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,07:37:23 | INFO | Train Epoch: 1 [17971712/18327966 (98%)] Loss: 0.72743 (0.7529) Data (t): 0.001 Batch (t): 0.915, 566.324/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,07:38:54 | INFO | Train Epoch: 1 [18022912/18327966 (98%)] Loss: 0.79178 (0.7530) Data (t): 0.001 Batch (t): 0.902, 567.173/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,07:40:26 | INFO | Train Epoch: 1 [18074112/18327966 (99%)] Loss: 0.68804 (0.7528) Data (t): 0.001 Batch (t): 0.928, 568.509/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,07:42:01 | INFO | Train Epoch: 1 [18125312/18327966 (99%)] Loss: 0.84892 (0.7531) Data (t): 0.001 Batch (t): 0.946, 566.846/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,07:43:34 | INFO | Train Epoch: 1 [18176512/18327966 (99%)] Loss: 0.85788 (0.7533) Data (t): 0.001 Batch (t): 0.928, 566.209/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,07:45:05 | INFO | Train Epoch: 1 [18227712/18327966 (99%)] Loss: 0.69887 (0.7532) Data (t): 0.001 Batch (t): 0.915, 568.044/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,07:46:35 | INFO | Train Epoch: 1 [18278912/18327966 (100%)] Loss: 0.66495 (0.7530) Data (t): 0.001 Batch (t): 0.902, 567.200/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,07:48:02 | INFO | Train Epoch: 1 [18327552/18327966 (100%)] Loss: 0.74101 (0.7529) Data (t): 0.002 Batch (t): 0.917, 569.527/s LR: 0.000000 Logit Scale: 100.000 - V4 diff --git a/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten/2024_11_26-13_27_22-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/params.txt b/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten/2024_11_26-13_27_22-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/params.txt deleted file mode 100644 index fbb5968970066992a9944c274d935d9cfae14260..0000000000000000000000000000000000000000 --- a/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten/2024_11_26-13_27_22-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/params.txt +++ /dev/null @@ -1,67 +0,0 @@ -batch_size: 64 -beta1: 0.9 -beta2: 0.98 -checkpoint_path: data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten/2024_11_26-13_27_22-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/checkpoints -copy_codebase: False -csv_caption_key: caption -csv_hard_captions_key: neg_caption -csv_img_key: img_path -csv_separator: , -dataset_resampled: False -dataset_type: csv -ddp_static_graph: False -debug: False -device: cuda:0 -dist_backend: nccl -dist_url: env:// -distributed: True -epochs: 2 -eps: 1e-06 -force_quick_gelu: True -gather_with_grad: False -grad_checkpointing: False -horovod: False -imagenet_v2: None -imagenet_val: None -local_loss: False -local_rank: 0 -lock_image: False -lock_image_freeze_bn_stats: False -lock_image_unlocked_groups: 0 -log_level: 20 -log_local: False -log_path: data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten/2024_11_26-13_27_22-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/out.log -logs: data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten -lr: 1e-06 -model: ViT-L-14-336 -name: 2024_11_26-13_27_22-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp -no_set_device_rank: False -norm_gradient_clip: None -precision: amp -pretrained: data/openclip-vit-14-336/openclip_model.pt -pretrained_image: False -rank: 0 -report_to: wandb -resume: None -save_frequency: 1 -save_most_recent: False -seed: 0 -skip_scheduler: False -tensorboard: False -tensorboard_path: -torchscript: False -trace: False -train_data: csv_data/plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten.csv -train_num_samples: None -use_bn_sync: False -val_data: None -val_frequency: 1 -val_num_samples: None -wandb: True -wandb_notes: -wandb_project: neg-clip-plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten -warmup: 0 -wd: 0.1 -workers: 4 -world_size: 8 -zeroshot_frequency: 2 diff --git a/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten/2024_11_27-07_49_03-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/checkpoints/epoch_1.pt b/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten/2024_11_27-07_49_03-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/checkpoints/epoch_1.pt deleted file mode 100644 index d2e686285c97a4f14cb4c0d423218060ad61fb1b..0000000000000000000000000000000000000000 --- a/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten/2024_11_27-07_49_03-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/checkpoints/epoch_1.pt +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:662e77e3bbe378fa991a97377a403068680d5765c797bdd76cdbdd5fc4d9e6a4 -size 5135890710 diff --git a/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten/2024_11_27-07_49_03-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/checkpoints/epoch_2.pt b/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten/2024_11_27-07_49_03-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/checkpoints/epoch_2.pt deleted file mode 100644 index 3555cb3e7db5fc12416d5d2928e1a78ec7ae9f65..0000000000000000000000000000000000000000 --- a/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten/2024_11_27-07_49_03-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/checkpoints/epoch_2.pt +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:b51a1cb7bb6a4315e6e5e82d620ab710c239d4a0625d00fae0f0155a669d2591 -size 5135890710 diff --git a/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten/2024_11_27-07_49_03-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/out.log b/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten/2024_11_27-07_49_03-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/out.log deleted file mode 100644 index 9fc3b13bbc4f83d34023277ddf084dc9784320ac..0000000000000000000000000000000000000000 --- a/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten/2024_11_27-07_49_03-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/out.log +++ /dev/null @@ -1,834 +0,0 @@ -2024-11-27,07:49:04 | INFO | Running in distributed mode with multiple processes. Device: cuda:0.Process (global: 0, local 0), total 8. -2024-11-27,07:49:04 | INFO | Loading ViT-L-14-336 model config. -2024-11-27,07:49:07 | INFO | Loading pretrained ViT-L-14-336 weights (data/openclip-vit-14-336/openclip_model.pt). -2024-11-27,07:49:16 | INFO | Model: -2024-11-27,07:49:16 | INFO | CLIP( - (visual): VisualTransformer( - (conv1): Conv2d(3, 1024, kernel_size=(14, 14), stride=(14, 14), bias=False) - (ln_pre): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) - (transformer): Transformer( - (resblocks): ModuleList( - (0-23): 24 x ResidualAttentionBlock( - (attn): MultiheadAttention( - (out_proj): NonDynamicallyQuantizableLinear(in_features=1024, out_features=1024, bias=True) - ) - (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) - (mlp): Sequential( - (c_fc): Linear(in_features=1024, out_features=4096, bias=True) - (gelu): QuickGELU() - (c_proj): Linear(in_features=4096, out_features=1024, bias=True) - ) - (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) - ) - ) - ) - (ln_post): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) - ) - (transformer): Transformer( - (resblocks): ModuleList( - (0-11): 12 x ResidualAttentionBlock( - (attn): MultiheadAttention( - (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) - ) - (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) - (mlp): Sequential( - (c_fc): Linear(in_features=768, out_features=3072, bias=True) - (gelu): QuickGELU() - (c_proj): Linear(in_features=3072, out_features=768, bias=True) - ) - (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) - ) - ) - ) - (token_embedding): Embedding(49408, 768) - (ln_final): LayerNorm((768,), eps=1e-05, elementwise_affine=True) -) -2024-11-27,07:49:16 | INFO | Params: -2024-11-27,07:49:16 | INFO | batch_size: 64 -2024-11-27,07:49:16 | INFO | beta1: 0.9 -2024-11-27,07:49:16 | INFO | beta2: 0.98 -2024-11-27,07:49:16 | INFO | checkpoint_path: data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten/2024_11_27-07_49_03-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/checkpoints -2024-11-27,07:49:16 | INFO | copy_codebase: False -2024-11-27,07:49:16 | INFO | csv_caption_key: caption -2024-11-27,07:49:16 | INFO | csv_hard_captions_key: neg_caption -2024-11-27,07:49:16 | INFO | csv_img_key: img_path -2024-11-27,07:49:16 | INFO | csv_separator: , -2024-11-27,07:49:16 | INFO | dataset_resampled: False -2024-11-27,07:49:16 | INFO | dataset_type: csv -2024-11-27,07:49:16 | INFO | ddp_static_graph: False -2024-11-27,07:49:16 | INFO | debug: False -2024-11-27,07:49:16 | INFO | device: cuda:0 -2024-11-27,07:49:16 | INFO | dist_backend: nccl -2024-11-27,07:49:16 | INFO | dist_url: env:// -2024-11-27,07:49:16 | INFO | distributed: True -2024-11-27,07:49:16 | INFO | epochs: 2 -2024-11-27,07:49:16 | INFO | eps: 1e-06 -2024-11-27,07:49:16 | INFO | force_quick_gelu: True -2024-11-27,07:49:16 | INFO | gather_with_grad: False -2024-11-27,07:49:16 | INFO | grad_checkpointing: False -2024-11-27,07:49:16 | INFO | horovod: False -2024-11-27,07:49:16 | INFO | imagenet_v2: None -2024-11-27,07:49:16 | INFO | imagenet_val: None -2024-11-27,07:49:16 | INFO | local_loss: False -2024-11-27,07:49:16 | INFO | local_rank: 0 -2024-11-27,07:49:16 | INFO | lock_image: False -2024-11-27,07:49:16 | INFO | lock_image_freeze_bn_stats: False -2024-11-27,07:49:16 | INFO | lock_image_unlocked_groups: 0 -2024-11-27,07:49:16 | INFO | log_level: 20 -2024-11-27,07:49:16 | INFO | log_local: False -2024-11-27,07:49:16 | INFO | log_path: data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten/2024_11_27-07_49_03-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/out.log -2024-11-27,07:49:16 | INFO | logs: data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten -2024-11-27,07:49:16 | INFO | lr: 5e-06 -2024-11-27,07:49:16 | INFO | model: ViT-L-14-336 -2024-11-27,07:49:16 | INFO | name: 2024_11_27-07_49_03-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp -2024-11-27,07:49:16 | INFO | no_set_device_rank: False -2024-11-27,07:49:16 | INFO | norm_gradient_clip: None -2024-11-27,07:49:16 | INFO | precision: amp -2024-11-27,07:49:16 | INFO | pretrained: data/openclip-vit-14-336/openclip_model.pt -2024-11-27,07:49:16 | INFO | pretrained_image: False -2024-11-27,07:49:16 | INFO | rank: 0 -2024-11-27,07:49:16 | INFO | report_to: wandb -2024-11-27,07:49:16 | INFO | resume: None -2024-11-27,07:49:16 | INFO | save_frequency: 1 -2024-11-27,07:49:16 | INFO | save_most_recent: False -2024-11-27,07:49:16 | INFO | seed: 0 -2024-11-27,07:49:16 | INFO | skip_scheduler: False -2024-11-27,07:49:16 | INFO | tensorboard: False -2024-11-27,07:49:16 | INFO | tensorboard_path: -2024-11-27,07:49:16 | INFO | torchscript: False -2024-11-27,07:49:16 | INFO | trace: False -2024-11-27,07:49:16 | INFO | train_data: csv_data/plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten.csv -2024-11-27,07:49:16 | INFO | train_num_samples: None -2024-11-27,07:49:16 | INFO | use_bn_sync: False -2024-11-27,07:49:16 | INFO | val_data: None -2024-11-27,07:49:16 | INFO | val_frequency: 1 -2024-11-27,07:49:16 | INFO | val_num_samples: None -2024-11-27,07:49:16 | INFO | wandb: True -2024-11-27,07:49:16 | INFO | wandb_notes: -2024-11-27,07:49:16 | INFO | wandb_project: neg-clip-plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten -2024-11-27,07:49:16 | INFO | warmup: 0 -2024-11-27,07:49:16 | INFO | wd: 0.1 -2024-11-27,07:49:16 | INFO | workers: 4 -2024-11-27,07:49:16 | INFO | world_size: 8 -2024-11-27,07:49:16 | INFO | zeroshot_frequency: 2 -2024-11-27,07:50:53 | INFO | Init a wandb project! -2024-11-27,07:51:04 | INFO | Start epoch 0 -2024-11-27,07:51:12 | INFO | Train Epoch: 0 [ 512/18327966 (0%)] Loss: 5.0808 (5.081) Data (t): 3.763 Batch (t): 8.178, 62.6062/s LR: 0.000005 Logit Scale: 100.000 - V4 -2024-11-27,07:52:45 | INFO | Train Epoch: 0 [ 51712/18327966 (0%)] Loss: 1.5936 (3.337) Data (t): 0.001 Batch (t): 0.926, 565.699/s LR: 0.000005 Logit Scale: 99.995 - V4 -2024-11-27,07:54:16 | INFO | Train Epoch: 0 [ 102912/18327966 (1%)] Loss: 1.2115 (2.629) Data (t): 0.001 Batch (t): 0.907, 564.364/s LR: 0.000005 Logit Scale: 99.996 - V4 -2024-11-27,07:55:48 | INFO | Train Epoch: 0 [ 154112/18327966 (1%)] Loss: 1.4464 (2.333) Data (t): 0.001 Batch (t): 0.919, 564.516/s LR: 0.000005 Logit Scale: 99.998 - V4 -2024-11-27,07:57:25 | INFO | Train Epoch: 0 [ 205312/18327966 (1%)] Loss: 1.2292 (2.112) Data (t): 0.001 Batch (t): 0.976, 567.491/s LR: 0.000005 Logit Scale: 99.998 - V4 -2024-11-27,07:58:56 | INFO | Train Epoch: 0 [ 256512/18327966 (1%)] Loss: 1.1142 (1.946) Data (t): 0.001 Batch (t): 0.907, 566.459/s LR: 0.000005 Logit Scale: 99.996 - V4 -2024-11-27,08:00:27 | INFO | Train Epoch: 0 [ 307712/18327966 (2%)] Loss: 0.93215 (1.801) Data (t): 0.001 Batch (t): 0.909, 565.421/s LR: 0.000005 Logit Scale: 99.994 - V4 -2024-11-27,08:01:58 | INFO | Train Epoch: 0 [ 358912/18327966 (2%)] Loss: 1.2424 (1.731) Data (t): 0.001 Batch (t): 0.908, 563.746/s LR: 0.000005 Logit Scale: 99.994 - V4 -2024-11-27,08:03:29 | INFO | Train Epoch: 0 [ 410112/18327966 (2%)] Loss: 1.0103 (1.651) Data (t): 0.001 Batch (t): 0.915, 569.688/s LR: 0.000005 Logit Scale: 99.993 - V4 -2024-11-27,08:05:05 | INFO | Train Epoch: 0 [ 461312/18327966 (3%)] Loss: 0.90902 (1.577) Data (t): 0.001 Batch (t): 0.962, 566.629/s LR: 0.000005 Logit Scale: 99.993 - V4 -2024-11-27,08:06:36 | INFO | Train Epoch: 0 [ 512512/18327966 (3%)] Loss: 0.92851 (1.518) Data (t): 0.001 Batch (t): 0.906, 565.293/s LR: 0.000005 Logit Scale: 99.988 - V4 -2024-11-27,08:08:06 | INFO | Train Epoch: 0 [ 563712/18327966 (3%)] Loss: 1.0660 (1.480) Data (t): 0.001 Batch (t): 0.904, 567.575/s LR: 0.000005 Logit Scale: 99.987 - V4 -2024-11-27,08:09:37 | INFO | Train Epoch: 0 [ 614912/18327966 (3%)] Loss: 1.1572 (1.455) Data (t): 0.001 Batch (t): 0.904, 569.009/s LR: 0.000005 Logit Scale: 99.985 - V4 -2024-11-27,08:11:07 | INFO | Train Epoch: 0 [ 666112/18327966 (4%)] Loss: 1.0617 (1.427) Data (t): 0.001 Batch (t): 0.906, 564.543/s LR: 0.000005 Logit Scale: 99.984 - V4 -2024-11-27,08:12:47 | INFO | Train Epoch: 0 [ 717312/18327966 (4%)] Loss: 0.96715 (1.397) Data (t): 0.001 Batch (t): 1.000, 566.352/s LR: 0.000005 Logit Scale: 99.980 - V4 -2024-11-27,08:14:18 | INFO | Train Epoch: 0 [ 768512/18327966 (4%)] Loss: 0.97674 (1.370) Data (t): 0.001 Batch (t): 0.905, 566.985/s LR: 0.000005 Logit Scale: 99.980 - V4 -2024-11-27,08:15:48 | INFO | Train Epoch: 0 [ 819712/18327966 (4%)] Loss: 1.1087 (1.355) Data (t): 0.001 Batch (t): 0.906, 565.858/s LR: 0.000005 Logit Scale: 99.976 - V4 -2024-11-27,08:17:19 | INFO | Train Epoch: 0 [ 870912/18327966 (5%)] Loss: 1.1626 (1.344) Data (t): 0.001 Batch (t): 0.906, 566.668/s LR: 0.000005 Logit Scale: 99.974 - V4 -2024-11-27,08:18:49 | INFO | Train Epoch: 0 [ 922112/18327966 (5%)] Loss: 0.93911 (1.323) Data (t): 0.001 Batch (t): 0.905, 565.994/s LR: 0.000005 Logit Scale: 99.971 - V4 -2024-11-27,08:20:30 | INFO | Train Epoch: 0 [ 973312/18327966 (5%)] Loss: 0.92884 (1.303) Data (t): 0.001 Batch (t): 1.011, 568.167/s LR: 0.000005 Logit Scale: 99.969 - V4 -2024-11-27,08:22:01 | INFO | Train Epoch: 0 [ 1024512/18327966 (6%)] Loss: 0.94030 (1.286) Data (t): 0.001 Batch (t): 0.905, 568.660/s LR: 0.000005 Logit Scale: 99.967 - V4 -2024-11-27,08:23:31 | INFO | Train Epoch: 0 [ 1075712/18327966 (6%)] Loss: 0.87443 (1.267) Data (t): 0.001 Batch (t): 0.904, 568.278/s LR: 0.000005 Logit Scale: 99.966 - V4 -2024-11-27,08:25:02 | INFO | Train Epoch: 0 [ 1126912/18327966 (6%)] Loss: 0.86196 (1.250) Data (t): 0.001 Batch (t): 0.904, 564.836/s LR: 0.000005 Logit Scale: 99.961 - V4 -2024-11-27,08:26:32 | INFO | Train Epoch: 0 [ 1178112/18327966 (6%)] Loss: 0.97415 (1.238) Data (t): 0.001 Batch (t): 0.904, 567.853/s LR: 0.000005 Logit Scale: 99.961 - V4 -2024-11-27,08:28:08 | INFO | Train Epoch: 0 [ 1229312/18327966 (7%)] Loss: 1.0539 (1.231) Data (t): 0.001 Batch (t): 0.962, 251.209/s LR: 0.000005 Logit Scale: 99.961 - V4 -2024-11-27,08:29:42 | INFO | Train Epoch: 0 [ 1280512/18327966 (7%)] Loss: 0.91266 (1.219) Data (t): 0.001 Batch (t): 0.943, 562.502/s LR: 0.000005 Logit Scale: 99.960 - V4 -2024-11-27,08:31:13 | INFO | Train Epoch: 0 [ 1331712/18327966 (7%)] Loss: 0.99883 (1.210) Data (t): 0.001 Batch (t): 0.904, 567.762/s LR: 0.000005 Logit Scale: 99.958 - V4 -2024-11-27,08:32:43 | INFO | Train Epoch: 0 [ 1382912/18327966 (8%)] Loss: 1.0189 (1.204) Data (t): 0.001 Batch (t): 0.904, 572.102/s LR: 0.000005 Logit Scale: 99.956 - V4 -2024-11-27,08:34:14 | INFO | Train Epoch: 0 [ 1434112/18327966 (8%)] Loss: 0.89763 (1.193) Data (t): 0.000 Batch (t): 0.903, 570.611/s LR: 0.000005 Logit Scale: 99.956 - V4 -2024-11-27,08:35:45 | INFO | Train Epoch: 0 [ 1485312/18327966 (8%)] Loss: 0.92555 (1.184) Data (t): 0.001 Batch (t): 0.914, 566.652/s LR: 0.000005 Logit Scale: 99.954 - V4 -2024-11-27,08:37:23 | INFO | Train Epoch: 0 [ 1536512/18327966 (8%)] Loss: 0.91615 (1.176) Data (t): 0.001 Batch (t): 0.977, 566.576/s LR: 0.000005 Logit Scale: 99.951 - V4 -2024-11-27,08:38:53 | INFO | Train Epoch: 0 [ 1587712/18327966 (9%)] Loss: 0.89370 (1.167) Data (t): 0.001 Batch (t): 0.903, 565.478/s LR: 0.000005 Logit Scale: 99.948 - V4 -2024-11-27,08:40:23 | INFO | Train Epoch: 0 [ 1638912/18327966 (9%)] Loss: 1.0490 (1.163) Data (t): 0.001 Batch (t): 0.903, 564.599/s LR: 0.000005 Logit Scale: 99.945 - V4 -2024-11-27,08:41:54 | INFO | Train Epoch: 0 [ 1690112/18327966 (9%)] Loss: 0.88695 (1.155) Data (t): 0.001 Batch (t): 0.903, 566.990/s LR: 0.000005 Logit Scale: 99.947 - V4 -2024-11-27,08:43:25 | INFO | Train Epoch: 0 [ 1741312/18327966 (10%)] Loss: 1.0308 (1.151) Data (t): 0.001 Batch (t): 0.914, 572.173/s LR: 0.000005 Logit Scale: 99.945 - V4 -2024-11-27,08:45:04 | INFO | Train Epoch: 0 [ 1792512/18327966 (10%)] Loss: 0.90808 (1.145) Data (t): 0.001 Batch (t): 0.985, 563.442/s LR: 0.000005 Logit Scale: 99.943 - V4 -2024-11-27,08:46:34 | INFO | Train Epoch: 0 [ 1843712/18327966 (10%)] Loss: 0.93911 (1.139) Data (t): 0.001 Batch (t): 0.904, 565.638/s LR: 0.000005 Logit Scale: 99.944 - V4 -2024-11-27,08:48:04 | INFO | Train Epoch: 0 [ 1894912/18327966 (10%)] Loss: 0.90408 (1.133) Data (t): 0.001 Batch (t): 0.904, 567.138/s LR: 0.000005 Logit Scale: 99.941 - V4 -2024-11-27,08:49:35 | INFO | Train Epoch: 0 [ 1946112/18327966 (11%)] Loss: 0.86446 (1.126) Data (t): 0.001 Batch (t): 0.904, 570.062/s LR: 0.000005 Logit Scale: 99.938 - V4 -2024-11-27,08:51:05 | INFO | Train Epoch: 0 [ 1997312/18327966 (11%)] Loss: 0.90096 (1.120) Data (t): 0.001 Batch (t): 0.903, 568.038/s LR: 0.000005 Logit Scale: 99.936 - V4 -2024-11-27,08:52:45 | INFO | Train Epoch: 0 [ 2048512/18327966 (11%)] Loss: 0.77306 (1.112) Data (t): 0.001 Batch (t): 0.998, 563.958/s LR: 0.000005 Logit Scale: 99.934 - V4 -2024-11-27,08:54:15 | INFO | Train Epoch: 0 [ 2099712/18327966 (11%)] Loss: 0.89992 (1.107) Data (t): 0.001 Batch (t): 0.904, 567.883/s LR: 0.000005 Logit Scale: 99.932 - V4 -2024-11-27,08:55:46 | INFO | Train Epoch: 0 [ 2150912/18327966 (12%)] Loss: 0.80776 (1.100) Data (t): 0.001 Batch (t): 0.903, 567.126/s LR: 0.000005 Logit Scale: 99.931 - V4 -2024-11-27,08:57:16 | INFO | Train Epoch: 0 [ 2202112/18327966 (12%)] Loss: 0.81093 (1.093) Data (t): 0.001 Batch (t): 0.903, 565.368/s LR: 0.000005 Logit Scale: 99.931 - V4 -2024-11-27,08:58:46 | INFO | Train Epoch: 0 [ 2253312/18327966 (12%)] Loss: 0.90606 (1.089) Data (t): 0.001 Batch (t): 0.905, 569.426/s LR: 0.000005 Logit Scale: 99.929 - V4 -2024-11-27,09:00:23 | INFO | Train Epoch: 0 [ 2304512/18327966 (13%)] Loss: 0.86596 (1.084) Data (t): 0.001 Batch (t): 0.971, 570.863/s LR: 0.000005 Logit Scale: 99.926 - V4 -2024-11-27,09:01:56 | INFO | Train Epoch: 0 [ 2355712/18327966 (13%)] Loss: 0.83033 (1.079) Data (t): 0.001 Batch (t): 0.930, 568.967/s LR: 0.000005 Logit Scale: 99.926 - V4 -2024-11-27,09:03:27 | INFO | Train Epoch: 0 [ 2406912/18327966 (13%)] Loss: 0.87583 (1.075) Data (t): 0.001 Batch (t): 0.902, 571.280/s LR: 0.000005 Logit Scale: 99.925 - V4 -2024-11-27,09:04:57 | INFO | Train Epoch: 0 [ 2458112/18327966 (13%)] Loss: 0.99384 (1.073) Data (t): 0.001 Batch (t): 0.902, 569.333/s LR: 0.000005 Logit Scale: 99.923 - V4 -2024-11-27,09:06:27 | INFO | Train Epoch: 0 [ 2509312/18327966 (14%)] Loss: 0.90764 (1.070) Data (t): 0.001 Batch (t): 0.902, 567.796/s LR: 0.000005 Logit Scale: 99.922 - V4 -2024-11-27,09:08:00 | INFO | Train Epoch: 0 [ 2560512/18327966 (14%)] Loss: 0.84941 (1.065) Data (t): 0.001 Batch (t): 0.931, 567.097/s LR: 0.000005 Logit Scale: 99.922 - V4 -2024-11-27,09:09:38 | INFO | Train Epoch: 0 [ 2611712/18327966 (14%)] Loss: 0.92484 (1.063) Data (t): 0.001 Batch (t): 0.981, 566.273/s LR: 0.000005 Logit Scale: 99.921 - V4 -2024-11-27,09:11:09 | INFO | Train Epoch: 0 [ 2662912/18327966 (15%)] Loss: 0.77367 (1.057) Data (t): 0.001 Batch (t): 0.903, 564.145/s LR: 0.000005 Logit Scale: 99.919 - V4 -2024-11-27,09:12:39 | INFO | Train Epoch: 0 [ 2714112/18327966 (15%)] Loss: 0.89276 (1.054) Data (t): 0.001 Batch (t): 0.904, 565.722/s LR: 0.000005 Logit Scale: 99.920 - V4 -2024-11-27,09:14:09 | INFO | Train Epoch: 0 [ 2765312/18327966 (15%)] Loss: 0.94589 (1.052) Data (t): 0.001 Batch (t): 0.903, 566.957/s LR: 0.000005 Logit Scale: 99.920 - V4 -2024-11-27,09:15:41 | INFO | Train Epoch: 0 [ 2816512/18327966 (15%)] Loss: 0.86785 (1.049) Data (t): 0.001 Batch (t): 0.913, 566.482/s LR: 0.000005 Logit Scale: 99.920 - V4 -2024-11-27,09:17:21 | INFO | Train Epoch: 0 [ 2867712/18327966 (16%)] Loss: 0.78401 (1.044) Data (t): 0.001 Batch (t): 0.999, 568.705/s LR: 0.000005 Logit Scale: 99.915 - V4 -2024-11-27,09:18:51 | INFO | Train Epoch: 0 [ 2918912/18327966 (16%)] Loss: 0.88704 (1.042) Data (t): 0.001 Batch (t): 0.902, 567.132/s LR: 0.000005 Logit Scale: 99.914 - V4 -2024-11-27,09:20:21 | INFO | Train Epoch: 0 [ 2970112/18327966 (16%)] Loss: 0.86143 (1.039) Data (t): 0.001 Batch (t): 0.904, 566.671/s LR: 0.000005 Logit Scale: 99.911 - V4 -2024-11-27,09:21:52 | INFO | Train Epoch: 0 [ 3021312/18327966 (16%)] Loss: 0.74849 (1.034) Data (t): 0.001 Batch (t): 0.903, 562.385/s LR: 0.000005 Logit Scale: 99.913 - V4 -2024-11-27,09:23:23 | INFO | Train Epoch: 0 [ 3072512/18327966 (17%)] Loss: 0.91687 (1.032) Data (t): 0.001 Batch (t): 0.914, 566.834/s LR: 0.000005 Logit Scale: 99.912 - V4 -2024-11-27,09:25:03 | INFO | Train Epoch: 0 [ 3123712/18327966 (17%)] Loss: 0.88092 (1.029) Data (t): 0.001 Batch (t): 0.999, 569.361/s LR: 0.000005 Logit Scale: 99.906 - V4 -2024-11-27,09:26:33 | INFO | Train Epoch: 0 [ 3174912/18327966 (17%)] Loss: 0.76266 (1.025) Data (t): 0.001 Batch (t): 0.903, 566.619/s LR: 0.000005 Logit Scale: 99.907 - V4 -2024-11-27,09:28:03 | INFO | Train Epoch: 0 [ 3226112/18327966 (18%)] Loss: 0.79375 (1.022) Data (t): 0.001 Batch (t): 0.903, 567.760/s LR: 0.000005 Logit Scale: 99.905 - V4 -2024-11-27,09:29:34 | INFO | Train Epoch: 0 [ 3277312/18327966 (18%)] Loss: 0.77217 (1.018) Data (t): 0.001 Batch (t): 0.903, 564.393/s LR: 0.000005 Logit Scale: 99.906 - V4 -2024-11-27,09:31:04 | INFO | Train Epoch: 0 [ 3328512/18327966 (18%)] Loss: 0.79114 (1.014) Data (t): 0.001 Batch (t): 0.903, 561.658/s LR: 0.000005 Logit Scale: 99.903 - V4 -2024-11-27,09:32:43 | INFO | Train Epoch: 0 [ 3379712/18327966 (18%)] Loss: 0.95056 (1.013) Data (t): 0.001 Batch (t): 0.993, 569.476/s LR: 0.000005 Logit Scale: 99.901 - V4 -2024-11-27,09:34:15 | INFO | Train Epoch: 0 [ 3430912/18327966 (19%)] Loss: 0.85950 (1.011) Data (t): 0.001 Batch (t): 0.920, 564.007/s LR: 0.000005 Logit Scale: 99.899 - V4 -2024-11-27,09:35:46 | INFO | Train Epoch: 0 [ 3482112/18327966 (19%)] Loss: 0.93205 (1.010) Data (t): 0.001 Batch (t): 0.905, 566.409/s LR: 0.000005 Logit Scale: 99.901 - V4 -2024-11-27,09:37:16 | INFO | Train Epoch: 0 [ 3533312/18327966 (19%)] Loss: 0.91971 (1.009) Data (t): 0.001 Batch (t): 0.904, 567.327/s LR: 0.000005 Logit Scale: 99.903 - V4 -2024-11-27,09:38:46 | INFO | Train Epoch: 0 [ 3584512/18327966 (20%)] Loss: 0.89871 (1.007) Data (t): 0.001 Batch (t): 0.904, 566.697/s LR: 0.000005 Logit Scale: 99.902 - V4 -2024-11-27,09:40:23 | INFO | Train Epoch: 0 [ 3635712/18327966 (20%)] Loss: 0.93705 (1.006) Data (t): 0.001 Batch (t): 0.961, 565.979/s LR: 0.000005 Logit Scale: 99.901 - V4 -2024-11-27,09:41:58 | INFO | Train Epoch: 0 [ 3686912/18327966 (20%)] Loss: 0.80864 (1.003) Data (t): 0.001 Batch (t): 0.955, 565.113/s LR: 0.000005 Logit Scale: 99.901 - V4 -2024-11-27,09:43:28 | INFO | Train Epoch: 0 [ 3738112/18327966 (20%)] Loss: 0.83220 (1.001) Data (t): 0.001 Batch (t): 0.905, 567.473/s LR: 0.000005 Logit Scale: 99.902 - V4 -2024-11-27,09:44:59 | INFO | Train Epoch: 0 [ 3789312/18327966 (21%)] Loss: 0.88427 (0.9995) Data (t): 0.001 Batch (t): 0.905, 567.145/s LR: 0.000005 Logit Scale: 99.900 - V4 -2024-11-27,09:46:29 | INFO | Train Epoch: 0 [ 3840512/18327966 (21%)] Loss: 0.75432 (0.9963) Data (t): 0.001 Batch (t): 0.904, 564.047/s LR: 0.000005 Logit Scale: 99.900 - V4 -2024-11-27,09:48:01 | INFO | Train Epoch: 0 [ 3891712/18327966 (21%)] Loss: 0.76405 (0.9933) Data (t): 0.001 Batch (t): 0.915, 563.975/s LR: 0.000005 Logit Scale: 99.900 - V4 -2024-11-27,09:49:41 | INFO | Train Epoch: 0 [ 3942912/18327966 (22%)] Loss: 0.74427 (0.9901) Data (t): 0.001 Batch (t): 1.001, 561.510/s LR: 0.000005 Logit Scale: 99.900 - V4 -2024-11-27,09:51:11 | INFO | Train Epoch: 0 [ 3994112/18327966 (22%)] Loss: 0.92315 (0.9892) Data (t): 0.001 Batch (t): 0.904, 566.596/s LR: 0.000005 Logit Scale: 99.897 - V4 -2024-11-27,09:52:42 | INFO | Train Epoch: 0 [ 4045312/18327966 (22%)] Loss: 0.76895 (0.9865) Data (t): 0.001 Batch (t): 0.904, 567.133/s LR: 0.000005 Logit Scale: 99.900 - V4 -2024-11-27,09:54:12 | INFO | Train Epoch: 0 [ 4096512/18327966 (22%)] Loss: 0.81706 (0.9844) Data (t): 0.001 Batch (t): 0.904, 568.512/s LR: 0.000005 Logit Scale: 99.900 - V4 -2024-11-27,09:55:44 | INFO | Train Epoch: 0 [ 4147712/18327966 (23%)] Loss: 0.81044 (0.9823) Data (t): 0.001 Batch (t): 0.915, 566.626/s LR: 0.000005 Logit Scale: 99.896 - V4 -2024-11-27,09:57:24 | INFO | Train Epoch: 0 [ 4198912/18327966 (23%)] Loss: 0.77906 (0.9798) Data (t): 0.001 Batch (t): 1.000, 566.023/s LR: 0.000005 Logit Scale: 99.895 - V4 -2024-11-27,09:58:54 | INFO | Train Epoch: 0 [ 4250112/18327966 (23%)] Loss: 0.81740 (0.9779) Data (t): 0.001 Batch (t): 0.905, 565.837/s LR: 0.000005 Logit Scale: 99.893 - V4 -2024-11-27,10:00:25 | INFO | Train Epoch: 0 [ 4301312/18327966 (23%)] Loss: 0.90447 (0.9770) Data (t): 0.001 Batch (t): 0.904, 566.693/s LR: 0.000005 Logit Scale: 99.894 - V4 -2024-11-27,10:01:55 | INFO | Train Epoch: 0 [ 4352512/18327966 (24%)] Loss: 0.94165 (0.9766) Data (t): 0.001 Batch (t): 0.904, 566.569/s LR: 0.000005 Logit Scale: 99.892 - V4 -2024-11-27,10:03:26 | INFO | Train Epoch: 0 [ 4403712/18327966 (24%)] Loss: 0.78024 (0.9744) Data (t): 0.001 Batch (t): 0.915, 571.552/s LR: 0.000005 Logit Scale: 99.891 - V4 -2024-11-27,10:05:06 | INFO | Train Epoch: 0 [ 4454912/18327966 (24%)] Loss: 0.82409 (0.9727) Data (t): 0.001 Batch (t): 0.999, 567.381/s LR: 0.000005 Logit Scale: 99.892 - V4 -2024-11-27,10:06:37 | INFO | Train Epoch: 0 [ 4506112/18327966 (25%)] Loss: 0.71558 (0.9698) Data (t): 0.001 Batch (t): 0.904, 567.360/s LR: 0.000005 Logit Scale: 99.891 - V4 -2024-11-27,10:08:07 | INFO | Train Epoch: 0 [ 4557312/18327966 (25%)] Loss: 0.81472 (0.9680) Data (t): 0.001 Batch (t): 0.905, 567.713/s LR: 0.000005 Logit Scale: 99.890 - V4 -2024-11-27,10:09:38 | INFO | Train Epoch: 0 [ 4608512/18327966 (25%)] Loss: 0.78029 (0.9660) Data (t): 0.001 Batch (t): 0.904, 563.790/s LR: 0.000005 Logit Scale: 99.891 - V4 -2024-11-27,10:11:08 | INFO | Train Epoch: 0 [ 4659712/18327966 (25%)] Loss: 0.82747 (0.9645) Data (t): 0.001 Batch (t): 0.904, 567.254/s LR: 0.000005 Logit Scale: 99.891 - V4 -2024-11-27,10:12:46 | INFO | Train Epoch: 0 [ 4710912/18327966 (26%)] Loss: 0.88366 (0.9636) Data (t): 0.001 Batch (t): 0.982, 566.917/s LR: 0.000005 Logit Scale: 99.892 - V4 -2024-11-27,10:14:19 | INFO | Train Epoch: 0 [ 4762112/18327966 (26%)] Loss: 0.74305 (0.9613) Data (t): 0.001 Batch (t): 0.932, 565.134/s LR: 0.000005 Logit Scale: 99.894 - V4 -2024-11-27,10:15:50 | INFO | Train Epoch: 0 [ 4813312/18327966 (26%)] Loss: 0.85874 (0.9602) Data (t): 0.001 Batch (t): 0.905, 567.484/s LR: 0.000005 Logit Scale: 99.893 - V4 -2024-11-27,10:17:20 | INFO | Train Epoch: 0 [ 4864512/18327966 (27%)] Loss: 0.80961 (0.9586) Data (t): 0.001 Batch (t): 0.904, 567.315/s LR: 0.000005 Logit Scale: 99.892 - V4 -2024-11-27,10:18:51 | INFO | Train Epoch: 0 [ 4915712/18327966 (27%)] Loss: 0.91040 (0.9581) Data (t): 0.001 Batch (t): 0.903, 566.843/s LR: 0.000005 Logit Scale: 99.895 - V4 -2024-11-27,10:20:24 | INFO | Train Epoch: 0 [ 4966912/18327966 (27%)] Loss: 0.85396 (0.9571) Data (t): 0.001 Batch (t): 0.931, 565.902/s LR: 0.000005 Logit Scale: 99.893 - V4 -2024-11-27,10:22:02 | INFO | Train Epoch: 0 [ 5018112/18327966 (27%)] Loss: 0.69485 (0.9544) Data (t): 0.001 Batch (t): 0.982, 568.714/s LR: 0.000005 Logit Scale: 99.895 - V4 -2024-11-27,10:23:32 | INFO | Train Epoch: 0 [ 5069312/18327966 (28%)] Loss: 0.84338 (0.9533) Data (t): 0.001 Batch (t): 0.904, 568.152/s LR: 0.000005 Logit Scale: 99.891 - V4 -2024-11-27,10:25:03 | INFO | Train Epoch: 0 [ 5120512/18327966 (28%)] Loss: 0.98483 (0.9536) Data (t): 0.001 Batch (t): 0.903, 567.539/s LR: 0.000005 Logit Scale: 99.894 - V4 -2024-11-27,10:26:33 | INFO | Train Epoch: 0 [ 5171712/18327966 (28%)] Loss: 0.86339 (0.9527) Data (t): 0.001 Batch (t): 0.904, 567.014/s LR: 0.000005 Logit Scale: 99.888 - V4 -2024-11-27,10:28:05 | INFO | Train Epoch: 0 [ 5222912/18327966 (28%)] Loss: 0.78369 (0.9511) Data (t): 0.001 Batch (t): 0.915, 568.792/s LR: 0.000005 Logit Scale: 99.888 - V4 -2024-11-27,10:29:43 | INFO | Train Epoch: 0 [ 5274112/18327966 (29%)] Loss: 0.77727 (0.9494) Data (t): 0.001 Batch (t): 0.989, 566.340/s LR: 0.000005 Logit Scale: 99.890 - V4 -2024-11-27,10:31:14 | INFO | Train Epoch: 0 [ 5325312/18327966 (29%)] Loss: 0.79618 (0.9480) Data (t): 0.001 Batch (t): 0.904, 569.212/s LR: 0.000005 Logit Scale: 99.888 - V4 -2024-11-27,10:32:44 | INFO | Train Epoch: 0 [ 5376512/18327966 (29%)] Loss: 0.81905 (0.9467) Data (t): 0.001 Batch (t): 0.904, 565.292/s LR: 0.000005 Logit Scale: 99.891 - V4 -2024-11-27,10:34:15 | INFO | Train Epoch: 0 [ 5427712/18327966 (30%)] Loss: 0.82385 (0.9456) Data (t): 0.001 Batch (t): 0.903, 568.997/s LR: 0.000005 Logit Scale: 99.890 - V4 -2024-11-27,10:35:46 | INFO | Train Epoch: 0 [ 5478912/18327966 (30%)] Loss: 0.94797 (0.9456) Data (t): 0.001 Batch (t): 0.915, 565.329/s LR: 0.000005 Logit Scale: 99.892 - V4 -2024-11-27,10:37:26 | INFO | Train Epoch: 0 [ 5530112/18327966 (30%)] Loss: 0.88187 (0.9450) Data (t): 0.001 Batch (t): 1.001, 565.730/s LR: 0.000005 Logit Scale: 99.891 - V4 -2024-11-27,10:38:57 | INFO | Train Epoch: 0 [ 5581312/18327966 (30%)] Loss: 0.75060 (0.9433) Data (t): 0.001 Batch (t): 0.904, 567.441/s LR: 0.000005 Logit Scale: 99.893 - V4 -2024-11-27,10:40:27 | INFO | Train Epoch: 0 [ 5632512/18327966 (31%)] Loss: 0.80397 (0.9420) Data (t): 0.001 Batch (t): 0.904, 569.851/s LR: 0.000005 Logit Scale: 99.894 - V4 -2024-11-27,10:41:57 | INFO | Train Epoch: 0 [ 5683712/18327966 (31%)] Loss: 0.79344 (0.9407) Data (t): 0.001 Batch (t): 0.904, 568.568/s LR: 0.000005 Logit Scale: 99.894 - V4 -2024-11-27,10:43:28 | INFO | Train Epoch: 0 [ 5734912/18327966 (31%)] Loss: 0.78566 (0.9393) Data (t): 0.001 Batch (t): 0.904, 566.554/s LR: 0.000005 Logit Scale: 99.897 - V4 -2024-11-27,10:45:09 | INFO | Train Epoch: 0 [ 5786112/18327966 (32%)] Loss: 0.74732 (0.9376) Data (t): 0.001 Batch (t): 1.010, 566.473/s LR: 0.000005 Logit Scale: 99.900 - V4 -2024-11-27,10:46:39 | INFO | Train Epoch: 0 [ 5837312/18327966 (32%)] Loss: 0.82612 (0.9366) Data (t): 0.001 Batch (t): 0.905, 568.922/s LR: 0.000005 Logit Scale: 99.901 - V4 -2024-11-27,10:48:10 | INFO | Train Epoch: 0 [ 5888512/18327966 (32%)] Loss: 0.87734 (0.9361) Data (t): 0.001 Batch (t): 0.905, 564.929/s LR: 0.000005 Logit Scale: 99.902 - V4 -2024-11-27,10:49:40 | INFO | Train Epoch: 0 [ 5939712/18327966 (32%)] Loss: 0.91505 (0.9360) Data (t): 0.001 Batch (t): 0.904, 567.306/s LR: 0.000005 Logit Scale: 99.903 - V4 -2024-11-27,10:51:10 | INFO | Train Epoch: 0 [ 5990912/18327966 (33%)] Loss: 0.85991 (0.9353) Data (t): 0.001 Batch (t): 0.904, 567.763/s LR: 0.000005 Logit Scale: 99.899 - V4 -2024-11-27,10:52:45 | INFO | Train Epoch: 0 [ 6042112/18327966 (33%)] Loss: 0.81894 (0.9343) Data (t): 0.001 Batch (t): 0.943, 567.066/s LR: 0.000005 Logit Scale: 99.902 - V4 -2024-11-27,10:54:22 | INFO | Train Epoch: 0 [ 6093312/18327966 (33%)] Loss: 0.91266 (0.9342) Data (t): 0.001 Batch (t): 0.972, 569.110/s LR: 0.000005 Logit Scale: 99.902 - V4 -2024-11-27,10:55:53 | INFO | Train Epoch: 0 [ 6144512/18327966 (34%)] Loss: 0.73773 (0.9325) Data (t): 0.001 Batch (t): 0.905, 566.839/s LR: 0.000005 Logit Scale: 99.905 - V4 -2024-11-27,10:57:23 | INFO | Train Epoch: 0 [ 6195712/18327966 (34%)] Loss: 0.79278 (0.9314) Data (t): 0.001 Batch (t): 0.904, 564.174/s LR: 0.000005 Logit Scale: 99.907 - V4 -2024-11-27,10:58:53 | INFO | Train Epoch: 0 [ 6246912/18327966 (34%)] Loss: 0.76678 (0.9300) Data (t): 0.001 Batch (t): 0.903, 569.528/s LR: 0.000005 Logit Scale: 99.907 - V4 -2024-11-27,11:00:25 | INFO | Train Epoch: 0 [ 6298112/18327966 (34%)] Loss: 0.79482 (0.9290) Data (t): 0.001 Batch (t): 0.919, 567.659/s LR: 0.000005 Logit Scale: 99.909 - V4 -2024-11-27,11:02:05 | INFO | Train Epoch: 0 [ 6349312/18327966 (35%)] Loss: 0.81985 (0.9281) Data (t): 0.001 Batch (t): 1.000, 569.379/s LR: 0.000005 Logit Scale: 99.911 - V4 -2024-11-27,11:03:36 | INFO | Train Epoch: 0 [ 6400512/18327966 (35%)] Loss: 0.82912 (0.9273) Data (t): 0.001 Batch (t): 0.904, 566.305/s LR: 0.000005 Logit Scale: 99.912 - V4 -2024-11-27,11:05:06 | INFO | Train Epoch: 0 [ 6451712/18327966 (35%)] Loss: 0.72413 (0.9257) Data (t): 0.001 Batch (t): 0.903, 566.849/s LR: 0.000005 Logit Scale: 99.910 - V4 -2024-11-27,11:06:36 | INFO | Train Epoch: 0 [ 6502912/18327966 (35%)] Loss: 0.97082 (0.9261) Data (t): 0.001 Batch (t): 0.903, 568.995/s LR: 0.000005 Logit Scale: 99.911 - V4 -2024-11-27,11:08:08 | INFO | Train Epoch: 0 [ 6554112/18327966 (36%)] Loss: 0.84517 (0.9254) Data (t): 0.001 Batch (t): 0.914, 568.943/s LR: 0.000005 Logit Scale: 99.913 - V4 -2024-11-27,11:09:47 | INFO | Train Epoch: 0 [ 6605312/18327966 (36%)] Loss: 0.75112 (0.9241) Data (t): 0.001 Batch (t): 0.998, 569.106/s LR: 0.000005 Logit Scale: 99.916 - V4 -2024-11-27,11:11:18 | INFO | Train Epoch: 0 [ 6656512/18327966 (36%)] Loss: 0.77257 (0.9229) Data (t): 0.001 Batch (t): 0.903, 567.630/s LR: 0.000005 Logit Scale: 99.914 - V4 -2024-11-27,11:12:48 | INFO | Train Epoch: 0 [ 6707712/18327966 (37%)] Loss: 0.77504 (0.9218) Data (t): 0.001 Batch (t): 0.903, 565.977/s LR: 0.000005 Logit Scale: 99.915 - V4 -2024-11-27,11:14:18 | INFO | Train Epoch: 0 [ 6758912/18327966 (37%)] Loss: 0.78359 (0.9208) Data (t): 0.001 Batch (t): 0.903, 570.493/s LR: 0.000005 Logit Scale: 99.917 - V4 -2024-11-27,11:15:50 | INFO | Train Epoch: 0 [ 6810112/18327966 (37%)] Loss: 0.92655 (0.9208) Data (t): 0.001 Batch (t): 0.913, 570.036/s LR: 0.000005 Logit Scale: 99.917 - V4 -2024-11-27,11:17:30 | INFO | Train Epoch: 0 [ 6861312/18327966 (37%)] Loss: 0.82313 (0.9201) Data (t): 0.001 Batch (t): 0.999, 564.372/s LR: 0.000005 Logit Scale: 99.920 - V4 -2024-11-27,11:19:00 | INFO | Train Epoch: 0 [ 6912512/18327966 (38%)] Loss: 0.85077 (0.9196) Data (t): 0.001 Batch (t): 0.903, 568.290/s LR: 0.000005 Logit Scale: 99.921 - V4 -2024-11-27,11:20:30 | INFO | Train Epoch: 0 [ 6963712/18327966 (38%)] Loss: 0.76293 (0.9184) Data (t): 0.001 Batch (t): 0.903, 567.490/s LR: 0.000005 Logit Scale: 99.920 - V4 -2024-11-27,11:22:01 | INFO | Train Epoch: 0 [ 7014912/18327966 (38%)] Loss: 0.74503 (0.9172) Data (t): 0.001 Batch (t): 0.904, 568.003/s LR: 0.000005 Logit Scale: 99.922 - V4 -2024-11-27,11:23:31 | INFO | Train Epoch: 0 [ 7066112/18327966 (39%)] Loss: 0.70990 (0.9157) Data (t): 0.001 Batch (t): 0.906, 566.643/s LR: 0.000005 Logit Scale: 99.923 - V4 -2024-11-27,11:25:11 | INFO | Train Epoch: 0 [ 7117312/18327966 (39%)] Loss: 0.75398 (0.9145) Data (t): 0.001 Batch (t): 0.994, 568.975/s LR: 0.000005 Logit Scale: 99.926 - V4 -2024-11-27,11:26:43 | INFO | Train Epoch: 0 [ 7168512/18327966 (39%)] Loss: 0.80365 (0.9137) Data (t): 0.001 Batch (t): 0.921, 564.225/s LR: 0.000005 Logit Scale: 99.924 - V4 -2024-11-27,11:28:13 | INFO | Train Epoch: 0 [ 7219712/18327966 (39%)] Loss: 0.82130 (0.9131) Data (t): 0.001 Batch (t): 0.905, 568.299/s LR: 0.000005 Logit Scale: 99.925 - V4 -2024-11-27,11:29:44 | INFO | Train Epoch: 0 [ 7270912/18327966 (40%)] Loss: 0.78246 (0.9122) Data (t): 0.001 Batch (t): 0.904, 565.395/s LR: 0.000005 Logit Scale: 99.929 - V4 -2024-11-27,11:31:14 | INFO | Train Epoch: 0 [ 7322112/18327966 (40%)] Loss: 0.79176 (0.9113) Data (t): 0.001 Batch (t): 0.904, 567.461/s LR: 0.000005 Logit Scale: 99.932 - V4 -2024-11-27,11:32:47 | INFO | Train Epoch: 0 [ 7373312/18327966 (40%)] Loss: 0.73724 (0.9101) Data (t): 0.001 Batch (t): 0.931, 567.729/s LR: 0.000005 Logit Scale: 99.930 - V4 -2024-11-27,11:34:25 | INFO | Train Epoch: 0 [ 7424512/18327966 (41%)] Loss: 0.81516 (0.9095) Data (t): 0.001 Batch (t): 0.983, 561.152/s LR: 0.000005 Logit Scale: 99.934 - V4 -2024-11-27,11:35:56 | INFO | Train Epoch: 0 [ 7475712/18327966 (41%)] Loss: 0.79436 (0.9087) Data (t): 0.001 Batch (t): 0.904, 565.412/s LR: 0.000005 Logit Scale: 99.933 - V4 -2024-11-27,11:37:26 | INFO | Train Epoch: 0 [ 7526912/18327966 (41%)] Loss: 0.77874 (0.9078) Data (t): 0.001 Batch (t): 0.902, 565.980/s LR: 0.000004 Logit Scale: 99.934 - V4 -2024-11-27,11:38:56 | INFO | Train Epoch: 0 [ 7578112/18327966 (41%)] Loss: 0.93847 (0.9080) Data (t): 0.001 Batch (t): 0.903, 567.733/s LR: 0.000004 Logit Scale: 99.938 - V4 -2024-11-27,11:40:28 | INFO | Train Epoch: 0 [ 7629312/18327966 (42%)] Loss: 0.74290 (0.9069) Data (t): 0.001 Batch (t): 0.915, 567.667/s LR: 0.000004 Logit Scale: 99.942 - V4 -2024-11-27,11:42:08 | INFO | Train Epoch: 0 [ 7680512/18327966 (42%)] Loss: 0.79449 (0.9062) Data (t): 0.001 Batch (t): 1.001, 564.374/s LR: 0.000004 Logit Scale: 99.941 - V4 -2024-11-27,11:43:38 | INFO | Train Epoch: 0 [ 7731712/18327966 (42%)] Loss: 0.68963 (0.9048) Data (t): 0.001 Batch (t): 0.903, 568.852/s LR: 0.000004 Logit Scale: 99.945 - V4 -2024-11-27,11:45:09 | INFO | Train Epoch: 0 [ 7782912/18327966 (42%)] Loss: 0.78446 (0.9040) Data (t): 0.001 Batch (t): 0.904, 567.379/s LR: 0.000004 Logit Scale: 99.946 - V4 -2024-11-27,11:46:39 | INFO | Train Epoch: 0 [ 7834112/18327966 (43%)] Loss: 0.76714 (0.9031) Data (t): 0.001 Batch (t): 0.903, 565.691/s LR: 0.000004 Logit Scale: 99.948 - V4 -2024-11-27,11:48:10 | INFO | Train Epoch: 0 [ 7885312/18327966 (43%)] Loss: 0.78418 (0.9023) Data (t): 0.001 Batch (t): 0.914, 571.130/s LR: 0.000004 Logit Scale: 99.947 - V4 -2024-11-27,11:49:49 | INFO | Train Epoch: 0 [ 7936512/18327966 (43%)] Loss: 0.66396 (0.9008) Data (t): 0.001 Batch (t): 0.990, 568.469/s LR: 0.000004 Logit Scale: 99.950 - V4 -2024-11-27,11:51:20 | INFO | Train Epoch: 0 [ 7987712/18327966 (44%)] Loss: 0.81278 (0.9002) Data (t): 0.001 Batch (t): 0.904, 565.351/s LR: 0.000004 Logit Scale: 99.954 - V4 -2024-11-27,11:52:50 | INFO | Train Epoch: 0 [ 8038912/18327966 (44%)] Loss: 0.81442 (0.8997) Data (t): 0.001 Batch (t): 0.904, 567.579/s LR: 0.000004 Logit Scale: 99.955 - V4 -2024-11-27,11:54:20 | INFO | Train Epoch: 0 [ 8090112/18327966 (44%)] Loss: 0.88071 (0.8996) Data (t): 0.001 Batch (t): 0.904, 565.248/s LR: 0.000004 Logit Scale: 99.956 - V4 -2024-11-27,11:55:52 | INFO | Train Epoch: 0 [ 8141312/18327966 (44%)] Loss: 0.67033 (0.8981) Data (t): 0.001 Batch (t): 0.914, 567.262/s LR: 0.000004 Logit Scale: 99.955 - V4 -2024-11-27,11:57:30 | INFO | Train Epoch: 0 [ 8192512/18327966 (45%)] Loss: 0.73876 (0.8972) Data (t): 0.001 Batch (t): 0.978, 568.870/s LR: 0.000004 Logit Scale: 99.960 - V4 -2024-11-27,11:59:00 | INFO | Train Epoch: 0 [ 8243712/18327966 (45%)] Loss: 0.72523 (0.8961) Data (t): 0.001 Batch (t): 0.902, 563.804/s LR: 0.000004 Logit Scale: 99.962 - V4 -2024-11-27,12:00:30 | INFO | Train Epoch: 0 [ 8294912/18327966 (45%)] Loss: 0.79919 (0.8955) Data (t): 0.001 Batch (t): 0.904, 566.677/s LR: 0.000004 Logit Scale: 99.964 - V4 -2024-11-27,12:02:01 | INFO | Train Epoch: 0 [ 8346112/18327966 (46%)] Loss: 0.70107 (0.8943) Data (t): 0.001 Batch (t): 0.904, 569.342/s LR: 0.000004 Logit Scale: 99.964 - V4 -2024-11-27,12:03:31 | INFO | Train Epoch: 0 [ 8397312/18327966 (46%)] Loss: 0.68866 (0.8931) Data (t): 0.001 Batch (t): 0.903, 567.071/s LR: 0.000004 Logit Scale: 99.970 - V4 -2024-11-27,12:05:05 | INFO | Train Epoch: 0 [ 8448512/18327966 (46%)] Loss: 0.76166 (0.8923) Data (t): 0.001 Batch (t): 0.943, 568.459/s LR: 0.000004 Logit Scale: 99.970 - V4 -2024-11-27,12:06:43 | INFO | Train Epoch: 0 [ 8499712/18327966 (46%)] Loss: 0.78632 (0.8916) Data (t): 0.001 Batch (t): 0.973, 566.524/s LR: 0.000004 Logit Scale: 99.972 - V4 -2024-11-27,12:08:13 | INFO | Train Epoch: 0 [ 8550912/18327966 (47%)] Loss: 0.78354 (0.8910) Data (t): 0.001 Batch (t): 0.906, 563.088/s LR: 0.000004 Logit Scale: 99.974 - V4 -2024-11-27,12:09:44 | INFO | Train Epoch: 0 [ 8602112/18327966 (47%)] Loss: 0.76658 (0.8903) Data (t): 0.001 Batch (t): 0.905, 566.951/s LR: 0.000004 Logit Scale: 99.977 - V4 -2024-11-27,12:11:14 | INFO | Train Epoch: 0 [ 8653312/18327966 (47%)] Loss: 0.67208 (0.8890) Data (t): 0.001 Batch (t): 0.903, 568.796/s LR: 0.000004 Logit Scale: 99.981 - V4 -2024-11-27,12:12:47 | INFO | Train Epoch: 0 [ 8704512/18327966 (47%)] Loss: 0.69694 (0.8879) Data (t): 0.001 Batch (t): 0.933, 568.868/s LR: 0.000004 Logit Scale: 99.984 - V4 -2024-11-27,12:14:26 | INFO | Train Epoch: 0 [ 8755712/18327966 (48%)] Loss: 0.71831 (0.8869) Data (t): 0.001 Batch (t): 0.985, 568.612/s LR: 0.000004 Logit Scale: 99.987 - V4 -2024-11-27,12:15:56 | INFO | Train Epoch: 0 [ 8806912/18327966 (48%)] Loss: 0.73277 (0.8860) Data (t): 0.001 Batch (t): 0.905, 566.318/s LR: 0.000004 Logit Scale: 99.992 - V4 -2024-11-27,12:17:27 | INFO | Train Epoch: 0 [ 8858112/18327966 (48%)] Loss: 0.87546 (0.8859) Data (t): 0.001 Batch (t): 0.905, 566.121/s LR: 0.000004 Logit Scale: 99.995 - V4 -2024-11-27,12:18:57 | INFO | Train Epoch: 0 [ 8909312/18327966 (49%)] Loss: 0.80799 (0.8855) Data (t): 0.001 Batch (t): 0.904, 565.463/s LR: 0.000004 Logit Scale: 99.997 - V4 -2024-11-27,12:20:29 | INFO | Train Epoch: 0 [ 8960512/18327966 (49%)] Loss: 0.83759 (0.8852) Data (t): 0.001 Batch (t): 0.914, 568.135/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,12:22:09 | INFO | Train Epoch: 0 [ 9011712/18327966 (49%)] Loss: 0.77201 (0.8846) Data (t): 0.001 Batch (t): 1.000, 567.066/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,12:23:39 | INFO | Train Epoch: 0 [ 9062912/18327966 (49%)] Loss: 0.83448 (0.8843) Data (t): 0.001 Batch (t): 0.903, 566.117/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,12:25:09 | INFO | Train Epoch: 0 [ 9114112/18327966 (50%)] Loss: 0.83867 (0.8840) Data (t): 0.001 Batch (t): 0.903, 565.763/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,12:26:39 | INFO | Train Epoch: 0 [ 9165312/18327966 (50%)] Loss: 0.70765 (0.8830) Data (t): 0.001 Batch (t): 0.902, 565.476/s LR: 0.000004 Logit Scale: 99.999 - V4 -2024-11-27,12:28:11 | INFO | Train Epoch: 0 [ 9216512/18327966 (50%)] Loss: 0.85929 (0.8829) Data (t): 0.001 Batch (t): 0.914, 566.412/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,12:29:51 | INFO | Train Epoch: 0 [ 9267712/18327966 (51%)] Loss: 0.71828 (0.8820) Data (t): 0.001 Batch (t): 1.001, 571.240/s LR: 0.000004 Logit Scale: 99.999 - V4 -2024-11-27,12:31:21 | INFO | Train Epoch: 0 [ 9318912/18327966 (51%)] Loss: 0.67066 (0.8809) Data (t): 0.001 Batch (t): 0.904, 566.727/s LR: 0.000004 Logit Scale: 99.998 - V4 -2024-11-27,12:32:52 | INFO | Train Epoch: 0 [ 9370112/18327966 (51%)] Loss: 0.64525 (0.8796) Data (t): 0.001 Batch (t): 0.905, 563.880/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,12:34:22 | INFO | Train Epoch: 0 [ 9421312/18327966 (51%)] Loss: 0.81450 (0.8792) Data (t): 0.001 Batch (t): 0.904, 567.481/s LR: 0.000004 Logit Scale: 99.999 - V4 -2024-11-27,12:35:54 | INFO | Train Epoch: 0 [ 9472512/18327966 (52%)] Loss: 0.65807 (0.8780) Data (t): 0.001 Batch (t): 0.915, 568.004/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,12:37:34 | INFO | Train Epoch: 0 [ 9523712/18327966 (52%)] Loss: 0.70551 (0.8771) Data (t): 0.001 Batch (t): 1.002, 567.672/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,12:39:04 | INFO | Train Epoch: 0 [ 9574912/18327966 (52%)] Loss: 0.83360 (0.8769) Data (t): 0.001 Batch (t): 0.902, 565.066/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,12:40:34 | INFO | Train Epoch: 0 [ 9626112/18327966 (53%)] Loss: 0.75350 (0.8762) Data (t): 0.001 Batch (t): 0.903, 566.198/s LR: 0.000004 Logit Scale: 99.998 - V4 -2024-11-27,12:42:05 | INFO | Train Epoch: 0 [ 9677312/18327966 (53%)] Loss: 0.61873 (0.8749) Data (t): 0.001 Batch (t): 0.903, 567.390/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,12:43:35 | INFO | Train Epoch: 0 [ 9728512/18327966 (53%)] Loss: 0.72432 (0.8741) Data (t): 0.001 Batch (t): 0.903, 563.114/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,12:45:09 | INFO | Train Epoch: 0 [ 9779712/18327966 (53%)] Loss: 0.63052 (0.8728) Data (t): 0.001 Batch (t): 0.943, 566.924/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,12:46:45 | INFO | Train Epoch: 0 [ 9830912/18327966 (54%)] Loss: 0.57580 (0.8713) Data (t): 0.001 Batch (t): 0.961, 568.091/s LR: 0.000004 Logit Scale: 99.999 - V4 -2024-11-27,12:48:16 | INFO | Train Epoch: 0 [ 9882112/18327966 (54%)] Loss: 0.81321 (0.8710) Data (t): 0.001 Batch (t): 0.904, 568.195/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,12:49:46 | INFO | Train Epoch: 0 [ 9933312/18327966 (54%)] Loss: 0.77754 (0.8705) Data (t): 0.001 Batch (t): 0.903, 566.709/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,12:51:16 | INFO | Train Epoch: 0 [ 9984512/18327966 (54%)] Loss: 0.76874 (0.8700) Data (t): 0.001 Batch (t): 0.904, 566.722/s LR: 0.000004 Logit Scale: 99.999 - V4 -2024-11-27,12:52:50 | INFO | Train Epoch: 0 [10035712/18327966 (55%)] Loss: 0.74869 (0.8694) Data (t): 0.001 Batch (t): 0.934, 568.776/s LR: 0.000004 Logit Scale: 99.999 - V4 -2024-11-27,12:54:26 | INFO | Train Epoch: 0 [10086912/18327966 (55%)] Loss: 0.80981 (0.8691) Data (t): 0.001 Batch (t): 0.967, 570.562/s LR: 0.000004 Logit Scale: 99.999 - V4 -2024-11-27,12:55:57 | INFO | Train Epoch: 0 [10138112/18327966 (55%)] Loss: 0.65179 (0.8680) Data (t): 0.001 Batch (t): 0.903, 570.154/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,12:57:27 | INFO | Train Epoch: 0 [10189312/18327966 (56%)] Loss: 0.65235 (0.8669) Data (t): 0.001 Batch (t): 0.904, 566.108/s LR: 0.000004 Logit Scale: 99.999 - V4 -2024-11-27,12:58:58 | INFO | Train Epoch: 0 [10240512/18327966 (56%)] Loss: 0.60298 (0.8656) Data (t): 0.001 Batch (t): 0.904, 566.960/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,13:00:29 | INFO | Train Epoch: 0 [10291712/18327966 (56%)] Loss: 0.79871 (0.8652) Data (t): 0.001 Batch (t): 0.915, 566.505/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,13:02:08 | INFO | Train Epoch: 0 [10342912/18327966 (56%)] Loss: 0.71952 (0.8645) Data (t): 0.001 Batch (t): 0.985, 566.804/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,13:03:38 | INFO | Train Epoch: 0 [10394112/18327966 (57%)] Loss: 0.81810 (0.8643) Data (t): 0.001 Batch (t): 0.905, 567.417/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,13:05:08 | INFO | Train Epoch: 0 [10445312/18327966 (57%)] Loss: 0.64084 (0.8632) Data (t): 0.001 Batch (t): 0.904, 567.157/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,13:06:39 | INFO | Train Epoch: 0 [10496512/18327966 (57%)] Loss: 0.81764 (0.8630) Data (t): 0.001 Batch (t): 0.903, 564.261/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,13:08:10 | INFO | Train Epoch: 0 [10547712/18327966 (58%)] Loss: 0.66073 (0.8620) Data (t): 0.001 Batch (t): 0.916, 568.191/s LR: 0.000004 Logit Scale: 99.997 - V4 -2024-11-27,13:09:51 | INFO | Train Epoch: 0 [10598912/18327966 (58%)] Loss: 0.71161 (0.8613) Data (t): 0.001 Batch (t): 1.003, 568.921/s LR: 0.000004 Logit Scale: 99.999 - V4 -2024-11-27,13:11:21 | INFO | Train Epoch: 0 [10650112/18327966 (58%)] Loss: 0.75694 (0.8608) Data (t): 0.001 Batch (t): 0.904, 566.214/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,13:12:51 | INFO | Train Epoch: 0 [10701312/18327966 (58%)] Loss: 0.76380 (0.8603) Data (t): 0.001 Batch (t): 0.904, 568.875/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,13:14:22 | INFO | Train Epoch: 0 [10752512/18327966 (59%)] Loss: 0.81833 (0.8601) Data (t): 0.001 Batch (t): 0.903, 567.805/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,13:15:53 | INFO | Train Epoch: 0 [10803712/18327966 (59%)] Loss: 0.71795 (0.8595) Data (t): 0.001 Batch (t): 0.915, 566.972/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,13:17:32 | INFO | Train Epoch: 0 [10854912/18327966 (59%)] Loss: 0.64245 (0.8584) Data (t): 0.001 Batch (t): 0.991, 570.339/s LR: 0.000004 Logit Scale: 99.997 - V4 -2024-11-27,13:19:04 | INFO | Train Epoch: 0 [10906112/18327966 (60%)] Loss: 0.70618 (0.8577) Data (t): 0.001 Batch (t): 0.914, 566.100/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,13:20:34 | INFO | Train Epoch: 0 [10957312/18327966 (60%)] Loss: 0.63576 (0.8567) Data (t): 0.001 Batch (t): 0.905, 562.515/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,13:22:05 | INFO | Train Epoch: 0 [11008512/18327966 (60%)] Loss: 0.60952 (0.8555) Data (t): 0.001 Batch (t): 0.904, 565.703/s LR: 0.000004 Logit Scale: 99.999 - V4 -2024-11-27,13:23:35 | INFO | Train Epoch: 0 [11059712/18327966 (60%)] Loss: 0.70350 (0.8548) Data (t): 0.001 Batch (t): 0.904, 565.216/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,13:25:08 | INFO | Train Epoch: 0 [11110912/18327966 (61%)] Loss: 0.73802 (0.8543) Data (t): 0.001 Batch (t): 0.933, 568.412/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,13:26:46 | INFO | Train Epoch: 0 [11162112/18327966 (61%)] Loss: 0.70849 (0.8536) Data (t): 0.001 Batch (t): 0.972, 570.546/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,13:28:16 | INFO | Train Epoch: 0 [11213312/18327966 (61%)] Loss: 0.70633 (0.8530) Data (t): 0.001 Batch (t): 0.903, 566.952/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,13:29:46 | INFO | Train Epoch: 0 [11264512/18327966 (61%)] Loss: 0.74268 (0.8525) Data (t): 0.001 Batch (t): 0.904, 566.451/s LR: 0.000004 Logit Scale: 99.999 - V4 -2024-11-27,13:31:17 | INFO | Train Epoch: 0 [11315712/18327966 (62%)] Loss: 0.61595 (0.8514) Data (t): 0.001 Batch (t): 0.904, 567.486/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,13:32:50 | INFO | Train Epoch: 0 [11366912/18327966 (62%)] Loss: 0.72600 (0.8509) Data (t): 0.001 Batch (t): 0.933, 565.585/s LR: 0.000004 Logit Scale: 99.999 - V4 -2024-11-27,13:34:29 | INFO | Train Epoch: 0 [11418112/18327966 (62%)] Loss: 0.76554 (0.8505) Data (t): 0.001 Batch (t): 0.986, 567.190/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,13:35:59 | INFO | Train Epoch: 0 [11469312/18327966 (63%)] Loss: 0.65418 (0.8496) Data (t): 0.001 Batch (t): 0.904, 566.150/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,13:37:29 | INFO | Train Epoch: 0 [11520512/18327966 (63%)] Loss: 0.68807 (0.8489) Data (t): 0.001 Batch (t): 0.904, 569.246/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,13:39:00 | INFO | Train Epoch: 0 [11571712/18327966 (63%)] Loss: 0.82289 (0.8488) Data (t): 0.001 Batch (t): 0.904, 568.696/s LR: 0.000004 Logit Scale: 99.999 - V4 -2024-11-27,13:40:31 | INFO | Train Epoch: 0 [11622912/18327966 (63%)] Loss: 0.71870 (0.8482) Data (t): 0.001 Batch (t): 0.916, 569.557/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,13:42:12 | INFO | Train Epoch: 0 [11674112/18327966 (64%)] Loss: 0.72115 (0.8476) Data (t): 0.001 Batch (t): 1.003, 567.088/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,13:43:42 | INFO | Train Epoch: 0 [11725312/18327966 (64%)] Loss: 0.63463 (0.8467) Data (t): 0.001 Batch (t): 0.903, 566.923/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,13:45:12 | INFO | Train Epoch: 0 [11776512/18327966 (64%)] Loss: 0.78907 (0.8465) Data (t): 0.001 Batch (t): 0.904, 563.952/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,13:46:43 | INFO | Train Epoch: 0 [11827712/18327966 (65%)] Loss: 0.73337 (0.8460) Data (t): 0.001 Batch (t): 0.903, 564.912/s LR: 0.000004 Logit Scale: 99.999 - V4 -2024-11-27,13:48:14 | INFO | Train Epoch: 0 [11878912/18327966 (65%)] Loss: 0.70040 (0.8454) Data (t): 0.001 Batch (t): 0.914, 568.614/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,13:49:53 | INFO | Train Epoch: 0 [11930112/18327966 (65%)] Loss: 0.76106 (0.8450) Data (t): 0.001 Batch (t): 0.992, 568.394/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,13:51:24 | INFO | Train Epoch: 0 [11981312/18327966 (65%)] Loss: 0.64349 (0.8441) Data (t): 0.001 Batch (t): 0.904, 567.089/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,13:52:54 | INFO | Train Epoch: 0 [12032512/18327966 (66%)] Loss: 0.79125 (0.8439) Data (t): 0.001 Batch (t): 0.903, 565.771/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,13:54:24 | INFO | Train Epoch: 0 [12083712/18327966 (66%)] Loss: 0.63526 (0.8430) Data (t): 0.001 Batch (t): 0.903, 567.533/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,13:55:56 | INFO | Train Epoch: 0 [12134912/18327966 (66%)] Loss: 0.80669 (0.8429) Data (t): 0.001 Batch (t): 0.915, 572.299/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,13:57:29 | INFO | Train Epoch: 0 [12186112/18327966 (66%)] Loss: 0.59083 (0.8418) Data (t): 0.001 Batch (t): 0.934, 566.750/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,13:59:07 | INFO | Train Epoch: 0 [12237312/18327966 (67%)] Loss: 0.84741 (0.8418) Data (t): 0.001 Batch (t): 0.973, 565.703/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,14:00:37 | INFO | Train Epoch: 0 [12288512/18327966 (67%)] Loss: 0.58551 (0.8408) Data (t): 0.001 Batch (t): 0.905, 568.789/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,14:02:08 | INFO | Train Epoch: 0 [12339712/18327966 (67%)] Loss: 0.81674 (0.8407) Data (t): 0.001 Batch (t): 0.905, 566.037/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,14:03:38 | INFO | Train Epoch: 0 [12390912/18327966 (68%)] Loss: 0.79069 (0.8405) Data (t): 0.001 Batch (t): 0.905, 565.730/s LR: 0.000004 Logit Scale: 99.999 - V4 -2024-11-27,14:05:11 | INFO | Train Epoch: 0 [12442112/18327966 (68%)] Loss: 0.76562 (0.8402) Data (t): 0.001 Batch (t): 0.933, 566.382/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,14:06:50 | INFO | Train Epoch: 0 [12493312/18327966 (68%)] Loss: 0.78576 (0.8400) Data (t): 0.001 Batch (t): 0.986, 564.835/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,14:08:20 | INFO | Train Epoch: 0 [12544512/18327966 (68%)] Loss: 0.61640 (0.8390) Data (t): 0.001 Batch (t): 0.904, 568.035/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,14:09:51 | INFO | Train Epoch: 0 [12595712/18327966 (69%)] Loss: 0.62892 (0.8382) Data (t): 0.001 Batch (t): 0.904, 565.984/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,14:11:21 | INFO | Train Epoch: 0 [12646912/18327966 (69%)] Loss: 0.78805 (0.8380) Data (t): 0.001 Batch (t): 0.904, 569.216/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,14:12:55 | INFO | Train Epoch: 0 [12698112/18327966 (69%)] Loss: 0.78083 (0.8378) Data (t): 0.001 Batch (t): 0.934, 567.707/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,14:14:32 | INFO | Train Epoch: 0 [12749312/18327966 (70%)] Loss: 0.77951 (0.8375) Data (t): 0.001 Batch (t): 0.974, 567.565/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,14:16:02 | INFO | Train Epoch: 0 [12800512/18327966 (70%)] Loss: 0.69432 (0.8370) Data (t): 0.001 Batch (t): 0.904, 564.722/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,14:17:33 | INFO | Train Epoch: 0 [12851712/18327966 (70%)] Loss: 0.74277 (0.8366) Data (t): 0.001 Batch (t): 0.905, 566.572/s LR: 0.000004 Logit Scale: 99.999 - V4 -2024-11-27,14:19:03 | INFO | Train Epoch: 0 [12902912/18327966 (70%)] Loss: 0.79355 (0.8364) Data (t): 0.001 Batch (t): 0.905, 565.137/s LR: 0.000004 Logit Scale: 99.999 - V4 -2024-11-27,14:20:35 | INFO | Train Epoch: 0 [12954112/18327966 (71%)] Loss: 0.72260 (0.8360) Data (t): 0.001 Batch (t): 0.916, 565.780/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,14:22:15 | INFO | Train Epoch: 0 [13005312/18327966 (71%)] Loss: 0.69372 (0.8354) Data (t): 0.001 Batch (t): 1.004, 569.627/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,14:23:46 | INFO | Train Epoch: 0 [13056512/18327966 (71%)] Loss: 0.65720 (0.8347) Data (t): 0.001 Batch (t): 0.903, 568.526/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,14:25:16 | INFO | Train Epoch: 0 [13107712/18327966 (72%)] Loss: 0.62900 (0.8339) Data (t): 0.001 Batch (t): 0.904, 565.934/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,14:26:46 | INFO | Train Epoch: 0 [13158912/18327966 (72%)] Loss: 0.77640 (0.8337) Data (t): 0.001 Batch (t): 0.903, 570.571/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,14:28:18 | INFO | Train Epoch: 0 [13210112/18327966 (72%)] Loss: 0.71228 (0.8332) Data (t): 0.001 Batch (t): 0.914, 569.065/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,14:29:56 | INFO | Train Epoch: 0 [13261312/18327966 (72%)] Loss: 0.66834 (0.8326) Data (t): 0.001 Batch (t): 0.981, 569.876/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,14:31:29 | INFO | Train Epoch: 0 [13312512/18327966 (73%)] Loss: 0.68263 (0.8320) Data (t): 0.001 Batch (t): 0.926, 565.192/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,14:32:59 | INFO | Train Epoch: 0 [13363712/18327966 (73%)] Loss: 0.65168 (0.8313) Data (t): 0.001 Batch (t): 0.902, 569.084/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,14:34:29 | INFO | Train Epoch: 0 [13414912/18327966 (73%)] Loss: 0.67378 (0.8307) Data (t): 0.001 Batch (t): 0.902, 569.795/s LR: 0.000004 Logit Scale: 99.999 - V4 -2024-11-27,14:35:59 | INFO | Train Epoch: 0 [13466112/18327966 (73%)] Loss: 0.63524 (0.8300) Data (t): 0.001 Batch (t): 0.902, 569.713/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,14:37:34 | INFO | Train Epoch: 0 [13517312/18327966 (74%)] Loss: 0.71737 (0.8296) Data (t): 0.001 Batch (t): 0.944, 567.540/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,14:39:11 | INFO | Train Epoch: 0 [13568512/18327966 (74%)] Loss: 0.62451 (0.8288) Data (t): 0.001 Batch (t): 0.974, 568.422/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,14:40:41 | INFO | Train Epoch: 0 [13619712/18327966 (74%)] Loss: 0.62328 (0.8280) Data (t): 0.001 Batch (t): 0.902, 566.445/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,14:42:11 | INFO | Train Epoch: 0 [13670912/18327966 (75%)] Loss: 0.70289 (0.8275) Data (t): 0.001 Batch (t): 0.903, 567.461/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,14:43:42 | INFO | Train Epoch: 0 [13722112/18327966 (75%)] Loss: 0.79118 (0.8274) Data (t): 0.001 Batch (t): 0.903, 565.773/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,14:45:15 | INFO | Train Epoch: 0 [13773312/18327966 (75%)] Loss: 0.86610 (0.8276) Data (t): 0.001 Batch (t): 0.932, 570.389/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,14:46:52 | INFO | Train Epoch: 0 [13824512/18327966 (75%)] Loss: 0.66262 (0.8269) Data (t): 0.001 Batch (t): 0.974, 564.597/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,14:48:23 | INFO | Train Epoch: 0 [13875712/18327966 (76%)] Loss: 0.74123 (0.8266) Data (t): 0.001 Batch (t): 0.902, 566.084/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,14:49:53 | INFO | Train Epoch: 0 [13926912/18327966 (76%)] Loss: 0.71564 (0.8262) Data (t): 0.001 Batch (t): 0.902, 570.567/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,14:51:23 | INFO | Train Epoch: 0 [13978112/18327966 (76%)] Loss: 0.69930 (0.8258) Data (t): 0.001 Batch (t): 0.901, 569.253/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,14:52:54 | INFO | Train Epoch: 0 [14029312/18327966 (77%)] Loss: 0.71140 (0.8253) Data (t): 0.001 Batch (t): 0.914, 567.578/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,14:54:35 | INFO | Train Epoch: 0 [14080512/18327966 (77%)] Loss: 0.79281 (0.8252) Data (t): 0.001 Batch (t): 1.004, 565.572/s LR: 0.000003 Logit Scale: 99.999 - V4 -2024-11-27,14:56:05 | INFO | Train Epoch: 0 [14131712/18327966 (77%)] Loss: 0.70436 (0.8248) Data (t): 0.001 Batch (t): 0.902, 567.566/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,14:57:35 | INFO | Train Epoch: 0 [14182912/18327966 (77%)] Loss: 0.78228 (0.8246) Data (t): 0.001 Batch (t): 0.903, 567.267/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,14:59:06 | INFO | Train Epoch: 0 [14234112/18327966 (78%)] Loss: 0.67759 (0.8241) Data (t): 0.001 Batch (t): 0.902, 565.861/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,15:00:37 | INFO | Train Epoch: 0 [14285312/18327966 (78%)] Loss: 0.80791 (0.8241) Data (t): 0.001 Batch (t): 0.915, 568.810/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,15:02:16 | INFO | Train Epoch: 0 [14336512/18327966 (78%)] Loss: 0.70499 (0.8236) Data (t): 0.001 Batch (t): 0.994, 565.798/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,15:03:47 | INFO | Train Epoch: 0 [14387712/18327966 (79%)] Loss: 0.81614 (0.8236) Data (t): 0.001 Batch (t): 0.905, 566.764/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,15:05:17 | INFO | Train Epoch: 0 [14438912/18327966 (79%)] Loss: 0.69702 (0.8232) Data (t): 0.001 Batch (t): 0.904, 566.722/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,15:06:48 | INFO | Train Epoch: 0 [14490112/18327966 (79%)] Loss: 0.78215 (0.8230) Data (t): 0.001 Batch (t): 0.904, 567.422/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,15:08:19 | INFO | Train Epoch: 0 [14541312/18327966 (79%)] Loss: 0.79244 (0.8229) Data (t): 0.001 Batch (t): 0.915, 566.241/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,15:09:52 | INFO | Train Epoch: 0 [14592512/18327966 (80%)] Loss: 0.64802 (0.8223) Data (t): 0.001 Batch (t): 0.932, 570.245/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,15:11:29 | INFO | Train Epoch: 0 [14643712/18327966 (80%)] Loss: 0.68054 (0.8218) Data (t): 0.001 Batch (t): 0.962, 566.116/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,15:12:59 | INFO | Train Epoch: 0 [14694912/18327966 (80%)] Loss: 0.67870 (0.8213) Data (t): 0.001 Batch (t): 0.902, 568.331/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,15:14:29 | INFO | Train Epoch: 0 [14746112/18327966 (80%)] Loss: 0.76567 (0.8211) Data (t): 0.001 Batch (t): 0.903, 565.210/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,15:15:59 | INFO | Train Epoch: 0 [14797312/18327966 (81%)] Loss: 0.79600 (0.8210) Data (t): 0.001 Batch (t): 0.902, 567.086/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,15:17:34 | INFO | Train Epoch: 0 [14848512/18327966 (81%)] Loss: 0.65100 (0.8204) Data (t): 0.001 Batch (t): 0.945, 568.885/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,15:19:11 | INFO | Train Epoch: 0 [14899712/18327966 (81%)] Loss: 0.57232 (0.8196) Data (t): 0.001 Batch (t): 0.974, 569.300/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,15:20:42 | INFO | Train Epoch: 0 [14950912/18327966 (82%)] Loss: 0.64639 (0.8190) Data (t): 0.001 Batch (t): 0.903, 567.084/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,15:22:12 | INFO | Train Epoch: 0 [15002112/18327966 (82%)] Loss: 0.68045 (0.8185) Data (t): 0.001 Batch (t): 0.903, 566.722/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,15:23:42 | INFO | Train Epoch: 0 [15053312/18327966 (82%)] Loss: 0.73823 (0.8183) Data (t): 0.001 Batch (t): 0.903, 568.185/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,15:25:15 | INFO | Train Epoch: 0 [15104512/18327966 (82%)] Loss: 0.73105 (0.8180) Data (t): 0.001 Batch (t): 0.933, 567.701/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,15:26:54 | INFO | Train Epoch: 0 [15155712/18327966 (83%)] Loss: 0.72709 (0.8177) Data (t): 0.001 Batch (t): 0.988, 566.709/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,15:28:25 | INFO | Train Epoch: 0 [15206912/18327966 (83%)] Loss: 0.66760 (0.8172) Data (t): 0.001 Batch (t): 0.903, 569.126/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,15:29:55 | INFO | Train Epoch: 0 [15258112/18327966 (83%)] Loss: 0.73050 (0.8169) Data (t): 0.001 Batch (t): 0.904, 563.350/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,15:31:25 | INFO | Train Epoch: 0 [15309312/18327966 (84%)] Loss: 0.66645 (0.8164) Data (t): 0.001 Batch (t): 0.904, 566.353/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,15:32:57 | INFO | Train Epoch: 0 [15360512/18327966 (84%)] Loss: 0.64263 (0.8158) Data (t): 0.001 Batch (t): 0.914, 567.711/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,15:34:36 | INFO | Train Epoch: 0 [15411712/18327966 (84%)] Loss: 0.66241 (0.8153) Data (t): 0.001 Batch (t): 0.994, 567.782/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,15:36:07 | INFO | Train Epoch: 0 [15462912/18327966 (84%)] Loss: 0.64162 (0.8147) Data (t): 0.001 Batch (t): 0.902, 568.867/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,15:37:37 | INFO | Train Epoch: 0 [15514112/18327966 (85%)] Loss: 0.75566 (0.8145) Data (t): 0.001 Batch (t): 0.904, 563.036/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,15:39:07 | INFO | Train Epoch: 0 [15565312/18327966 (85%)] Loss: 0.68702 (0.8141) Data (t): 0.001 Batch (t): 0.902, 565.640/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,15:40:39 | INFO | Train Epoch: 0 [15616512/18327966 (85%)] Loss: 0.70031 (0.8137) Data (t): 0.001 Batch (t): 0.914, 568.343/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,15:42:18 | INFO | Train Epoch: 0 [15667712/18327966 (85%)] Loss: 0.71348 (0.8134) Data (t): 0.001 Batch (t): 0.995, 572.105/s LR: 0.000003 Logit Scale: 99.999 - V4 -2024-11-27,15:43:48 | INFO | Train Epoch: 0 [15718912/18327966 (86%)] Loss: 0.62912 (0.8128) Data (t): 0.001 Batch (t): 0.901, 566.617/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,15:45:18 | INFO | Train Epoch: 0 [15770112/18327966 (86%)] Loss: 0.59128 (0.8121) Data (t): 0.001 Batch (t): 0.902, 569.526/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,15:46:48 | INFO | Train Epoch: 0 [15821312/18327966 (86%)] Loss: 0.60883 (0.8114) Data (t): 0.001 Batch (t): 0.901, 569.754/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,15:48:20 | INFO | Train Epoch: 0 [15872512/18327966 (87%)] Loss: 0.61971 (0.8108) Data (t): 0.001 Batch (t): 0.914, 568.781/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,15:49:53 | INFO | Train Epoch: 0 [15923712/18327966 (87%)] Loss: 0.73066 (0.8105) Data (t): 0.001 Batch (t): 0.932, 564.099/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,15:51:30 | INFO | Train Epoch: 0 [15974912/18327966 (87%)] Loss: 0.69198 (0.8102) Data (t): 0.001 Batch (t): 0.975, 567.779/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,15:53:01 | INFO | Train Epoch: 0 [16026112/18327966 (87%)] Loss: 0.76106 (0.8100) Data (t): 0.001 Batch (t): 0.902, 569.604/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,15:54:31 | INFO | Train Epoch: 0 [16077312/18327966 (88%)] Loss: 0.65573 (0.8095) Data (t): 0.001 Batch (t): 0.903, 568.936/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,15:56:01 | INFO | Train Epoch: 0 [16128512/18327966 (88%)] Loss: 0.62561 (0.8089) Data (t): 0.001 Batch (t): 0.901, 566.736/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,15:57:36 | INFO | Train Epoch: 0 [16179712/18327966 (88%)] Loss: 0.74097 (0.8087) Data (t): 0.001 Batch (t): 0.945, 563.838/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,15:59:13 | INFO | Train Epoch: 0 [16230912/18327966 (89%)] Loss: 0.68953 (0.8084) Data (t): 0.001 Batch (t): 0.976, 569.473/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,16:00:43 | INFO | Train Epoch: 0 [16282112/18327966 (89%)] Loss: 0.81091 (0.8084) Data (t): 0.001 Batch (t): 0.902, 567.394/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,16:02:14 | INFO | Train Epoch: 0 [16333312/18327966 (89%)] Loss: 0.72917 (0.8081) Data (t): 0.001 Batch (t): 0.902, 569.539/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,16:03:44 | INFO | Train Epoch: 0 [16384512/18327966 (89%)] Loss: 0.68037 (0.8077) Data (t): 0.001 Batch (t): 0.901, 566.537/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,16:05:17 | INFO | Train Epoch: 0 [16435712/18327966 (90%)] Loss: 0.62987 (0.8072) Data (t): 0.001 Batch (t): 0.933, 567.070/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,16:06:56 | INFO | Train Epoch: 0 [16486912/18327966 (90%)] Loss: 0.62572 (0.8066) Data (t): 0.001 Batch (t): 0.990, 569.179/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,16:08:26 | INFO | Train Epoch: 0 [16538112/18327966 (90%)] Loss: 0.63721 (0.8061) Data (t): 0.001 Batch (t): 0.904, 566.571/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,16:09:57 | INFO | Train Epoch: 0 [16589312/18327966 (91%)] Loss: 0.69772 (0.8057) Data (t): 0.001 Batch (t): 0.902, 566.584/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,16:11:27 | INFO | Train Epoch: 0 [16640512/18327966 (91%)] Loss: 0.70157 (0.8054) Data (t): 0.001 Batch (t): 0.903, 569.763/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,16:12:58 | INFO | Train Epoch: 0 [16691712/18327966 (91%)] Loss: 0.63146 (0.8049) Data (t): 0.001 Batch (t): 0.915, 568.980/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,16:14:39 | INFO | Train Epoch: 0 [16742912/18327966 (91%)] Loss: 0.67144 (0.8045) Data (t): 0.001 Batch (t): 1.008, 568.183/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,16:16:10 | INFO | Train Epoch: 0 [16794112/18327966 (92%)] Loss: 0.55325 (0.8037) Data (t): 0.001 Batch (t): 0.902, 569.296/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,16:17:40 | INFO | Train Epoch: 0 [16845312/18327966 (92%)] Loss: 0.74828 (0.8036) Data (t): 0.001 Batch (t): 0.904, 567.437/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,16:19:10 | INFO | Train Epoch: 0 [16896512/18327966 (92%)] Loss: 0.68635 (0.8032) Data (t): 0.001 Batch (t): 0.903, 569.734/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,16:20:42 | INFO | Train Epoch: 0 [16947712/18327966 (92%)] Loss: 0.63477 (0.8027) Data (t): 0.001 Batch (t): 0.915, 567.730/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,16:22:19 | INFO | Train Epoch: 0 [16998912/18327966 (93%)] Loss: 0.73620 (0.8025) Data (t): 0.001 Batch (t): 0.976, 189.309/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,16:23:53 | INFO | Train Epoch: 0 [17050112/18327966 (93%)] Loss: 0.55610 (0.8018) Data (t): 0.001 Batch (t): 0.933, 567.328/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,16:25:23 | INFO | Train Epoch: 0 [17101312/18327966 (93%)] Loss: 0.66238 (0.8013) Data (t): 0.001 Batch (t): 0.903, 570.807/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,16:26:53 | INFO | Train Epoch: 0 [17152512/18327966 (94%)] Loss: 0.54973 (0.8006) Data (t): 0.001 Batch (t): 0.902, 569.180/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,16:28:25 | INFO | Train Epoch: 0 [17203712/18327966 (94%)] Loss: 0.56252 (0.7999) Data (t): 0.001 Batch (t): 0.916, 567.687/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,16:29:58 | INFO | Train Epoch: 0 [17254912/18327966 (94%)] Loss: 0.61720 (0.7993) Data (t): 0.001 Batch (t): 0.934, 569.445/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,16:31:36 | INFO | Train Epoch: 0 [17306112/18327966 (94%)] Loss: 0.58037 (0.7987) Data (t): 0.001 Batch (t): 0.977, 566.206/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,16:33:06 | INFO | Train Epoch: 0 [17357312/18327966 (95%)] Loss: 0.60022 (0.7981) Data (t): 0.001 Batch (t): 0.905, 568.073/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,16:34:37 | INFO | Train Epoch: 0 [17408512/18327966 (95%)] Loss: 0.77289 (0.7980) Data (t): 0.001 Batch (t): 0.905, 570.396/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,16:36:07 | INFO | Train Epoch: 0 [17459712/18327966 (95%)] Loss: 0.64179 (0.7976) Data (t): 0.000 Batch (t): 0.903, 566.377/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,16:37:41 | INFO | Train Epoch: 0 [17510912/18327966 (96%)] Loss: 0.74745 (0.7974) Data (t): 0.001 Batch (t): 0.933, 565.430/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,16:39:19 | INFO | Train Epoch: 0 [17562112/18327966 (96%)] Loss: 0.72120 (0.7972) Data (t): 0.001 Batch (t): 0.988, 566.417/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,16:40:50 | INFO | Train Epoch: 0 [17613312/18327966 (96%)] Loss: 0.73588 (0.7970) Data (t): 0.001 Batch (t): 0.903, 565.891/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,16:42:20 | INFO | Train Epoch: 0 [17664512/18327966 (96%)] Loss: 0.72019 (0.7968) Data (t): 0.001 Batch (t): 0.902, 567.719/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,16:43:50 | INFO | Train Epoch: 0 [17715712/18327966 (97%)] Loss: 0.72524 (0.7966) Data (t): 0.001 Batch (t): 0.902, 567.796/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,16:45:23 | INFO | Train Epoch: 0 [17766912/18327966 (97%)] Loss: 0.74601 (0.7965) Data (t): 0.001 Batch (t): 0.933, 568.686/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,16:47:02 | INFO | Train Epoch: 0 [17818112/18327966 (97%)] Loss: 0.67925 (0.7961) Data (t): 0.001 Batch (t): 0.989, 568.948/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,16:48:32 | INFO | Train Epoch: 0 [17869312/18327966 (97%)] Loss: 0.67883 (0.7958) Data (t): 0.001 Batch (t): 0.902, 569.305/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,16:50:03 | INFO | Train Epoch: 0 [17920512/18327966 (98%)] Loss: 0.74505 (0.7956) Data (t): 0.001 Batch (t): 0.903, 567.833/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,16:51:33 | INFO | Train Epoch: 0 [17971712/18327966 (98%)] Loss: 0.63477 (0.7952) Data (t): 0.001 Batch (t): 0.903, 568.188/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,16:53:04 | INFO | Train Epoch: 0 [18022912/18327966 (98%)] Loss: 0.68990 (0.7949) Data (t): 0.001 Batch (t): 0.914, 569.464/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,16:54:45 | INFO | Train Epoch: 0 [18074112/18327966 (99%)] Loss: 0.66906 (0.7945) Data (t): 0.001 Batch (t): 1.008, 570.037/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,16:56:15 | INFO | Train Epoch: 0 [18125312/18327966 (99%)] Loss: 0.65814 (0.7942) Data (t): 0.001 Batch (t): 0.902, 568.981/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,16:57:45 | INFO | Train Epoch: 0 [18176512/18327966 (99%)] Loss: 0.74508 (0.7940) Data (t): 0.001 Batch (t): 0.901, 567.820/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,16:59:16 | INFO | Train Epoch: 0 [18227712/18327966 (99%)] Loss: 0.66807 (0.7937) Data (t): 0.001 Batch (t): 0.903, 568.297/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,17:00:47 | INFO | Train Epoch: 0 [18278912/18327966 (100%)] Loss: 0.70460 (0.7934) Data (t): 0.001 Batch (t): 0.915, 570.264/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,17:02:16 | INFO | Train Epoch: 0 [18327552/18327966 (100%)] Loss: 0.66774 (0.7931) Data (t): 0.002 Batch (t): 0.935, 572.846/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,17:02:23 | INFO | Start epoch 1 -2024-11-27,17:02:29 | INFO | Train Epoch: 1 [ 512/18327966 (0%)] Loss: 0.75120 (0.7512) Data (t): 3.605 Batch (t): 5.696, 89.8832/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,17:04:05 | INFO | Train Epoch: 1 [ 51712/18327966 (0%)] Loss: 0.63443 (0.6928) Data (t): 0.001 Batch (t): 0.965, 570.024/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,17:05:36 | INFO | Train Epoch: 1 [ 102912/18327966 (1%)] Loss: 0.55690 (0.6475) Data (t): 0.001 Batch (t): 0.902, 569.040/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,17:07:06 | INFO | Train Epoch: 1 [ 154112/18327966 (1%)] Loss: 0.65087 (0.6484) Data (t): 0.001 Batch (t): 0.903, 567.265/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,17:08:38 | INFO | Train Epoch: 1 [ 205312/18327966 (1%)] Loss: 0.65567 (0.6498) Data (t): 0.001 Batch (t): 0.917, 567.919/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,17:10:11 | INFO | Train Epoch: 1 [ 256512/18327966 (1%)] Loss: 0.61764 (0.6445) Data (t): 0.001 Batch (t): 0.935, 569.666/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,17:11:48 | INFO | Train Epoch: 1 [ 307712/18327966 (2%)] Loss: 0.56360 (0.6329) Data (t): 0.001 Batch (t): 0.969, 565.008/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,17:13:18 | INFO | Train Epoch: 1 [ 358912/18327966 (2%)] Loss: 0.68221 (0.6391) Data (t): 0.001 Batch (t): 0.903, 566.444/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,17:14:49 | INFO | Train Epoch: 1 [ 410112/18327966 (2%)] Loss: 0.60024 (0.6348) Data (t): 0.001 Batch (t): 0.904, 566.296/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,17:16:20 | INFO | Train Epoch: 1 [ 461312/18327966 (3%)] Loss: 0.75988 (0.6473) Data (t): 0.001 Batch (t): 0.915, 568.964/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,17:17:52 | INFO | Train Epoch: 1 [ 512512/18327966 (3%)] Loss: 0.66380 (0.6488) Data (t): 0.001 Batch (t): 0.920, 556.240/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,17:19:30 | INFO | Train Epoch: 1 [ 563712/18327966 (3%)] Loss: 0.71264 (0.6541) Data (t): 0.001 Batch (t): 0.980, 566.523/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,17:21:00 | INFO | Train Epoch: 1 [ 614912/18327966 (3%)] Loss: 0.60040 (0.6500) Data (t): 0.001 Batch (t): 0.903, 568.934/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,17:22:31 | INFO | Train Epoch: 1 [ 666112/18327966 (4%)] Loss: 0.62288 (0.6480) Data (t): 0.001 Batch (t): 0.903, 567.690/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,17:24:03 | INFO | Train Epoch: 1 [ 717312/18327966 (4%)] Loss: 0.67613 (0.6499) Data (t): 0.001 Batch (t): 0.918, 567.370/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,17:25:33 | INFO | Train Epoch: 1 [ 768512/18327966 (4%)] Loss: 0.68466 (0.6521) Data (t): 0.001 Batch (t): 0.904, 566.192/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,17:27:11 | INFO | Train Epoch: 1 [ 819712/18327966 (4%)] Loss: 0.62536 (0.6505) Data (t): 0.001 Batch (t): 0.980, 569.682/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,17:28:41 | INFO | Train Epoch: 1 [ 870912/18327966 (5%)] Loss: 0.66069 (0.6511) Data (t): 0.001 Batch (t): 0.902, 569.526/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,17:30:11 | INFO | Train Epoch: 1 [ 922112/18327966 (5%)] Loss: 0.60002 (0.6484) Data (t): 0.001 Batch (t): 0.903, 562.864/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,17:31:42 | INFO | Train Epoch: 1 [ 973312/18327966 (5%)] Loss: 0.73996 (0.6530) Data (t): 0.001 Batch (t): 0.902, 557.698/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,17:33:13 | INFO | Train Epoch: 1 [ 1024512/18327966 (6%)] Loss: 0.57249 (0.6491) Data (t): 0.001 Batch (t): 0.912, 570.411/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,17:34:50 | INFO | Train Epoch: 1 [ 1075712/18327966 (6%)] Loss: 0.62687 (0.6481) Data (t): 0.001 Batch (t): 0.972, 203.388/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,17:36:22 | INFO | Train Epoch: 1 [ 1126912/18327966 (6%)] Loss: 0.63891 (0.6477) Data (t): 0.001 Batch (t): 0.922, 570.773/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,17:37:52 | INFO | Train Epoch: 1 [ 1178112/18327966 (6%)] Loss: 0.60519 (0.6459) Data (t): 0.001 Batch (t): 0.901, 569.341/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,17:39:22 | INFO | Train Epoch: 1 [ 1229312/18327966 (7%)] Loss: 0.59000 (0.6437) Data (t): 0.001 Batch (t): 0.901, 569.751/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,17:40:54 | INFO | Train Epoch: 1 [ 1280512/18327966 (7%)] Loss: 0.58395 (0.6414) Data (t): 0.001 Batch (t): 0.913, 569.453/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,17:42:27 | INFO | Train Epoch: 1 [ 1331712/18327966 (7%)] Loss: 0.56869 (0.6387) Data (t): 0.001 Batch (t): 0.928, 569.499/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,17:44:03 | INFO | Train Epoch: 1 [ 1382912/18327966 (8%)] Loss: 0.55163 (0.6356) Data (t): 0.001 Batch (t): 0.965, 570.789/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,17:45:33 | INFO | Train Epoch: 1 [ 1434112/18327966 (8%)] Loss: 0.63915 (0.6357) Data (t): 0.001 Batch (t): 0.902, 568.528/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,17:47:03 | INFO | Train Epoch: 1 [ 1485312/18327966 (8%)] Loss: 0.73823 (0.6391) Data (t): 0.001 Batch (t): 0.902, 563.364/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,17:48:35 | INFO | Train Epoch: 1 [ 1536512/18327966 (8%)] Loss: 0.67664 (0.6404) Data (t): 0.001 Batch (t): 0.913, 562.549/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,17:50:07 | INFO | Train Epoch: 1 [ 1587712/18327966 (9%)] Loss: 0.66682 (0.6412) Data (t): 0.001 Batch (t): 0.918, 562.795/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,17:51:43 | INFO | Train Epoch: 1 [ 1638912/18327966 (9%)] Loss: 0.60676 (0.6401) Data (t): 0.001 Batch (t): 0.967, 568.271/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,17:53:13 | INFO | Train Epoch: 1 [ 1690112/18327966 (9%)] Loss: 0.63534 (0.6400) Data (t): 0.001 Batch (t): 0.901, 567.809/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,17:54:44 | INFO | Train Epoch: 1 [ 1741312/18327966 (10%)] Loss: 0.60941 (0.6391) Data (t): 0.001 Batch (t): 0.901, 569.420/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,17:56:15 | INFO | Train Epoch: 1 [ 1792512/18327966 (10%)] Loss: 0.59763 (0.6380) Data (t): 0.001 Batch (t): 0.912, 571.491/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,17:57:46 | INFO | Train Epoch: 1 [ 1843712/18327966 (10%)] Loss: 0.60775 (0.6372) Data (t): 0.001 Batch (t): 0.917, 567.672/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,17:59:24 | INFO | Train Epoch: 1 [ 1894912/18327966 (10%)] Loss: 0.55751 (0.6351) Data (t): 0.001 Batch (t): 0.978, 569.186/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,18:00:54 | INFO | Train Epoch: 1 [ 1946112/18327966 (11%)] Loss: 0.64905 (0.6354) Data (t): 0.001 Batch (t): 0.901, 568.317/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,18:02:25 | INFO | Train Epoch: 1 [ 1997312/18327966 (11%)] Loss: 0.58431 (0.6341) Data (t): 0.001 Batch (t): 0.902, 570.541/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,18:03:55 | INFO | Train Epoch: 1 [ 2048512/18327966 (11%)] Loss: 0.64978 (0.6345) Data (t): 0.001 Batch (t): 0.902, 571.506/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,18:05:26 | INFO | Train Epoch: 1 [ 2099712/18327966 (11%)] Loss: 0.66345 (0.6352) Data (t): 0.001 Batch (t): 0.914, 568.919/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,18:07:05 | INFO | Train Epoch: 1 [ 2150912/18327966 (12%)] Loss: 0.62783 (0.6350) Data (t): 0.001 Batch (t): 0.984, 569.682/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,18:08:36 | INFO | Train Epoch: 1 [ 2202112/18327966 (12%)] Loss: 0.60896 (0.6344) Data (t): 0.001 Batch (t): 0.912, 567.766/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,18:10:06 | INFO | Train Epoch: 1 [ 2253312/18327966 (12%)] Loss: 0.63694 (0.6345) Data (t): 0.001 Batch (t): 0.902, 568.073/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,18:11:36 | INFO | Train Epoch: 1 [ 2304512/18327966 (13%)] Loss: 0.47835 (0.6311) Data (t): 0.001 Batch (t): 0.901, 568.906/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,18:13:07 | INFO | Train Epoch: 1 [ 2355712/18327966 (13%)] Loss: 0.61984 (0.6309) Data (t): 0.001 Batch (t): 0.913, 570.377/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,18:14:43 | INFO | Train Epoch: 1 [ 2406912/18327966 (13%)] Loss: 0.64323 (0.6311) Data (t): 0.001 Batch (t): 0.956, 571.051/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,18:16:17 | INFO | Train Epoch: 1 [ 2458112/18327966 (13%)] Loss: 0.66416 (0.6318) Data (t): 0.001 Batch (t): 0.940, 567.958/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,18:17:47 | INFO | Train Epoch: 1 [ 2509312/18327966 (14%)] Loss: 0.67694 (0.6327) Data (t): 0.001 Batch (t): 0.902, 563.704/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,18:19:18 | INFO | Train Epoch: 1 [ 2560512/18327966 (14%)] Loss: 0.68143 (0.6337) Data (t): 0.001 Batch (t): 0.903, 566.538/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,18:20:49 | INFO | Train Epoch: 1 [ 2611712/18327966 (14%)] Loss: 0.71533 (0.6352) Data (t): 0.001 Batch (t): 0.914, 569.666/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,18:22:22 | INFO | Train Epoch: 1 [ 2662912/18327966 (15%)] Loss: 0.69103 (0.6363) Data (t): 0.001 Batch (t): 0.930, 567.987/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,18:23:59 | INFO | Train Epoch: 1 [ 2714112/18327966 (15%)] Loss: 0.60609 (0.6357) Data (t): 0.001 Batch (t): 0.968, 567.382/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,18:25:29 | INFO | Train Epoch: 1 [ 2765312/18327966 (15%)] Loss: 0.66202 (0.6362) Data (t): 0.001 Batch (t): 0.903, 570.418/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,18:26:59 | INFO | Train Epoch: 1 [ 2816512/18327966 (15%)] Loss: 0.61197 (0.6358) Data (t): 0.001 Batch (t): 0.902, 567.202/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,18:28:31 | INFO | Train Epoch: 1 [ 2867712/18327966 (16%)] Loss: 0.63398 (0.6357) Data (t): 0.001 Batch (t): 0.913, 567.729/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,18:30:02 | INFO | Train Epoch: 1 [ 2918912/18327966 (16%)] Loss: 0.60002 (0.6351) Data (t): 0.001 Batch (t): 0.918, 565.983/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,18:31:40 | INFO | Train Epoch: 1 [ 2970112/18327966 (16%)] Loss: 0.70049 (0.6362) Data (t): 0.001 Batch (t): 0.980, 568.178/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,18:33:11 | INFO | Train Epoch: 1 [ 3021312/18327966 (16%)] Loss: 0.58317 (0.6353) Data (t): 0.001 Batch (t): 0.903, 567.159/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,18:34:41 | INFO | Train Epoch: 1 [ 3072512/18327966 (17%)] Loss: 0.71872 (0.6367) Data (t): 0.001 Batch (t): 0.904, 567.382/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,18:36:11 | INFO | Train Epoch: 1 [ 3123712/18327966 (17%)] Loss: 0.59874 (0.6361) Data (t): 0.001 Batch (t): 0.902, 569.357/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,18:37:44 | INFO | Train Epoch: 1 [ 3174912/18327966 (17%)] Loss: 0.68173 (0.6368) Data (t): 0.001 Batch (t): 0.930, 570.013/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,18:39:22 | INFO | Train Epoch: 1 [ 3226112/18327966 (18%)] Loss: 0.69018 (0.6377) Data (t): 0.001 Batch (t): 0.980, 568.500/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,18:40:52 | INFO | Train Epoch: 1 [ 3277312/18327966 (18%)] Loss: 0.62971 (0.6375) Data (t): 0.001 Batch (t): 0.902, 568.190/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,18:42:23 | INFO | Train Epoch: 1 [ 3328512/18327966 (18%)] Loss: 0.66439 (0.6379) Data (t): 0.001 Batch (t): 0.902, 564.411/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,18:43:53 | INFO | Train Epoch: 1 [ 3379712/18327966 (18%)] Loss: 0.65695 (0.6382) Data (t): 0.001 Batch (t): 0.902, 566.662/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,18:45:24 | INFO | Train Epoch: 1 [ 3430912/18327966 (19%)] Loss: 0.77622 (0.6403) Data (t): 0.001 Batch (t): 0.913, 566.117/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,18:47:01 | INFO | Train Epoch: 1 [ 3482112/18327966 (19%)] Loss: 0.60423 (0.6397) Data (t): 0.001 Batch (t): 0.973, 566.055/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,18:48:34 | INFO | Train Epoch: 1 [ 3533312/18327966 (19%)] Loss: 0.64140 (0.6398) Data (t): 0.001 Batch (t): 0.924, 563.059/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,18:50:04 | INFO | Train Epoch: 1 [ 3584512/18327966 (20%)] Loss: 0.66744 (0.6401) Data (t): 0.001 Batch (t): 0.903, 564.644/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,18:51:34 | INFO | Train Epoch: 1 [ 3635712/18327966 (20%)] Loss: 0.51865 (0.6385) Data (t): 0.001 Batch (t): 0.902, 565.453/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,18:53:06 | INFO | Train Epoch: 1 [ 3686912/18327966 (20%)] Loss: 0.69820 (0.6393) Data (t): 0.001 Batch (t): 0.913, 564.687/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,18:54:39 | INFO | Train Epoch: 1 [ 3738112/18327966 (20%)] Loss: 0.60881 (0.6389) Data (t): 0.001 Batch (t): 0.929, 564.310/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,18:56:15 | INFO | Train Epoch: 1 [ 3789312/18327966 (21%)] Loss: 0.57052 (0.6380) Data (t): 0.001 Batch (t): 0.967, 564.118/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,18:57:46 | INFO | Train Epoch: 1 [ 3840512/18327966 (21%)] Loss: 0.69868 (0.6388) Data (t): 0.001 Batch (t): 0.902, 566.990/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,18:59:16 | INFO | Train Epoch: 1 [ 3891712/18327966 (21%)] Loss: 0.71233 (0.6397) Data (t): 0.001 Batch (t): 0.900, 569.075/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,19:00:47 | INFO | Train Epoch: 1 [ 3942912/18327966 (22%)] Loss: 0.64713 (0.6398) Data (t): 0.001 Batch (t): 0.912, 568.058/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,19:02:20 | INFO | Train Epoch: 1 [ 3994112/18327966 (22%)] Loss: 0.64975 (0.6399) Data (t): 0.001 Batch (t): 0.929, 570.610/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,19:03:56 | INFO | Train Epoch: 1 [ 4045312/18327966 (22%)] Loss: 0.62197 (0.6397) Data (t): 0.001 Batch (t): 0.967, 567.424/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,19:05:26 | INFO | Train Epoch: 1 [ 4096512/18327966 (22%)] Loss: 0.63268 (0.6396) Data (t): 0.001 Batch (t): 0.900, 570.043/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,19:06:57 | INFO | Train Epoch: 1 [ 4147712/18327966 (23%)] Loss: 0.65573 (0.6398) Data (t): 0.001 Batch (t): 0.902, 570.132/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,19:08:27 | INFO | Train Epoch: 1 [ 4198912/18327966 (23%)] Loss: 0.58288 (0.6391) Data (t): 0.001 Batch (t): 0.901, 570.116/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,19:10:00 | INFO | Train Epoch: 1 [ 4250112/18327966 (23%)] Loss: 0.60637 (0.6387) Data (t): 0.001 Batch (t): 0.928, 569.319/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,19:11:37 | INFO | Train Epoch: 1 [ 4301312/18327966 (23%)] Loss: 0.74624 (0.6400) Data (t): 0.001 Batch (t): 0.978, 567.803/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,19:13:07 | INFO | Train Epoch: 1 [ 4352512/18327966 (24%)] Loss: 0.57163 (0.6392) Data (t): 0.001 Batch (t): 0.901, 568.985/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,19:14:38 | INFO | Train Epoch: 1 [ 4403712/18327966 (24%)] Loss: 0.65204 (0.6394) Data (t): 0.001 Batch (t): 0.901, 570.097/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,19:16:08 | INFO | Train Epoch: 1 [ 4454912/18327966 (24%)] Loss: 0.50096 (0.6378) Data (t): 0.001 Batch (t): 0.901, 567.209/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,19:17:41 | INFO | Train Epoch: 1 [ 4506112/18327966 (25%)] Loss: 0.58848 (0.6372) Data (t): 0.001 Batch (t): 0.932, 568.770/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,19:19:18 | INFO | Train Epoch: 1 [ 4557312/18327966 (25%)] Loss: 0.60658 (0.6369) Data (t): 0.001 Batch (t): 0.966, 567.090/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,19:20:49 | INFO | Train Epoch: 1 [ 4608512/18327966 (25%)] Loss: 0.64751 (0.6370) Data (t): 0.001 Batch (t): 0.914, 567.909/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,19:22:19 | INFO | Train Epoch: 1 [ 4659712/18327966 (25%)] Loss: 0.56752 (0.6362) Data (t): 0.001 Batch (t): 0.902, 567.991/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,19:23:49 | INFO | Train Epoch: 1 [ 4710912/18327966 (26%)] Loss: 0.68280 (0.6367) Data (t): 0.001 Batch (t): 0.901, 569.443/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,19:25:20 | INFO | Train Epoch: 1 [ 4762112/18327966 (26%)] Loss: 0.64980 (0.6369) Data (t): 0.001 Batch (t): 0.912, 569.247/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,19:26:56 | INFO | Train Epoch: 1 [ 4813312/18327966 (26%)] Loss: 0.64517 (0.6370) Data (t): 0.001 Batch (t): 0.955, 570.707/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,19:28:30 | INFO | Train Epoch: 1 [ 4864512/18327966 (27%)] Loss: 0.74958 (0.6381) Data (t): 0.001 Batch (t): 0.939, 566.514/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,19:30:00 | INFO | Train Epoch: 1 [ 4915712/18327966 (27%)] Loss: 0.66348 (0.6384) Data (t): 0.001 Batch (t): 0.902, 568.032/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,19:31:30 | INFO | Train Epoch: 1 [ 4966912/18327966 (27%)] Loss: 0.60858 (0.6381) Data (t): 0.001 Batch (t): 0.902, 569.107/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,19:33:02 | INFO | Train Epoch: 1 [ 5018112/18327966 (27%)] Loss: 0.68408 (0.6386) Data (t): 0.001 Batch (t): 0.912, 570.326/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,19:34:34 | INFO | Train Epoch: 1 [ 5069312/18327966 (28%)] Loss: 0.60620 (0.6382) Data (t): 0.001 Batch (t): 0.929, 566.639/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,19:36:11 | INFO | Train Epoch: 1 [ 5120512/18327966 (28%)] Loss: 0.67433 (0.6386) Data (t): 0.001 Batch (t): 0.967, 564.866/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,19:37:41 | INFO | Train Epoch: 1 [ 5171712/18327966 (28%)] Loss: 0.68278 (0.6390) Data (t): 0.001 Batch (t): 0.902, 567.935/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,19:39:12 | INFO | Train Epoch: 1 [ 5222912/18327966 (28%)] Loss: 0.56417 (0.6383) Data (t): 0.001 Batch (t): 0.902, 568.283/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,19:40:42 | INFO | Train Epoch: 1 [ 5274112/18327966 (29%)] Loss: 0.64844 (0.6384) Data (t): 0.001 Batch (t): 0.901, 566.745/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,19:42:16 | INFO | Train Epoch: 1 [ 5325312/18327966 (29%)] Loss: 0.58795 (0.6379) Data (t): 0.001 Batch (t): 0.940, 570.487/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,19:43:52 | INFO | Train Epoch: 1 [ 5376512/18327966 (29%)] Loss: 0.61420 (0.6377) Data (t): 0.001 Batch (t): 0.967, 569.046/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,19:45:22 | INFO | Train Epoch: 1 [ 5427712/18327966 (30%)] Loss: 0.66819 (0.6380) Data (t): 0.001 Batch (t): 0.901, 567.592/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,19:46:52 | INFO | Train Epoch: 1 [ 5478912/18327966 (30%)] Loss: 0.67412 (0.6383) Data (t): 0.001 Batch (t): 0.901, 571.498/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,19:48:23 | INFO | Train Epoch: 1 [ 5530112/18327966 (30%)] Loss: 0.66720 (0.6386) Data (t): 0.001 Batch (t): 0.902, 568.609/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,19:49:56 | INFO | Train Epoch: 1 [ 5581312/18327966 (30%)] Loss: 0.65503 (0.6387) Data (t): 0.001 Batch (t): 0.930, 568.791/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,19:51:34 | INFO | Train Epoch: 1 [ 5632512/18327966 (31%)] Loss: 0.69117 (0.6392) Data (t): 0.001 Batch (t): 0.979, 566.801/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,19:53:04 | INFO | Train Epoch: 1 [ 5683712/18327966 (31%)] Loss: 0.69541 (0.6397) Data (t): 0.001 Batch (t): 0.901, 567.925/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,19:54:34 | INFO | Train Epoch: 1 [ 5734912/18327966 (31%)] Loss: 0.63419 (0.6397) Data (t): 0.001 Batch (t): 0.901, 569.113/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,19:56:04 | INFO | Train Epoch: 1 [ 5786112/18327966 (32%)] Loss: 0.52716 (0.6387) Data (t): 0.001 Batch (t): 0.902, 568.855/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,19:57:37 | INFO | Train Epoch: 1 [ 5837312/18327966 (32%)] Loss: 0.60734 (0.6384) Data (t): 0.001 Batch (t): 0.929, 567.198/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,19:59:12 | INFO | Train Epoch: 1 [ 5888512/18327966 (32%)] Loss: 0.65078 (0.6385) Data (t): 0.001 Batch (t): 0.954, 567.965/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,20:00:45 | INFO | Train Epoch: 1 [ 5939712/18327966 (32%)] Loss: 0.68337 (0.6389) Data (t): 0.001 Batch (t): 0.924, 569.468/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,20:02:15 | INFO | Train Epoch: 1 [ 5990912/18327966 (33%)] Loss: 0.61207 (0.6387) Data (t): 0.001 Batch (t): 0.901, 568.385/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,20:03:45 | INFO | Train Epoch: 1 [ 6042112/18327966 (33%)] Loss: 0.63665 (0.6386) Data (t): 0.001 Batch (t): 0.901, 568.052/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,20:05:18 | INFO | Train Epoch: 1 [ 6093312/18327966 (33%)] Loss: 0.57920 (0.6382) Data (t): 0.001 Batch (t): 0.929, 254.024/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,20:06:52 | INFO | Train Epoch: 1 [ 6144512/18327966 (34%)] Loss: 0.66879 (0.6384) Data (t): 0.001 Batch (t): 0.939, 569.960/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,20:08:25 | INFO | Train Epoch: 1 [ 6195712/18327966 (34%)] Loss: 0.60182 (0.6381) Data (t): 0.001 Batch (t): 0.938, 565.733/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,20:09:56 | INFO | Train Epoch: 1 [ 6246912/18327966 (34%)] Loss: 0.62171 (0.6380) Data (t): 0.001 Batch (t): 0.901, 568.328/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,20:11:26 | INFO | Train Epoch: 1 [ 6298112/18327966 (34%)] Loss: 0.58943 (0.6376) Data (t): 0.001 Batch (t): 0.900, 568.817/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,20:12:56 | INFO | Train Epoch: 1 [ 6349312/18327966 (35%)] Loss: 0.63759 (0.6376) Data (t): 0.001 Batch (t): 0.901, 570.455/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,20:14:30 | INFO | Train Epoch: 1 [ 6400512/18327966 (35%)] Loss: 0.73063 (0.6383) Data (t): 0.001 Batch (t): 0.939, 570.712/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,20:16:06 | INFO | Train Epoch: 1 [ 6451712/18327966 (35%)] Loss: 0.53109 (0.6375) Data (t): 0.001 Batch (t): 0.967, 573.273/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,20:17:36 | INFO | Train Epoch: 1 [ 6502912/18327966 (35%)] Loss: 0.67862 (0.6378) Data (t): 0.001 Batch (t): 0.900, 568.995/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,20:19:06 | INFO | Train Epoch: 1 [ 6554112/18327966 (36%)] Loss: 0.67593 (0.6381) Data (t): 0.001 Batch (t): 0.900, 567.140/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,20:20:36 | INFO | Train Epoch: 1 [ 6605312/18327966 (36%)] Loss: 0.57908 (0.6376) Data (t): 0.001 Batch (t): 0.899, 571.058/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,20:22:10 | INFO | Train Epoch: 1 [ 6656512/18327966 (36%)] Loss: 0.66805 (0.6379) Data (t): 0.001 Batch (t): 0.939, 569.618/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,20:23:47 | INFO | Train Epoch: 1 [ 6707712/18327966 (37%)] Loss: 0.57539 (0.6374) Data (t): 0.001 Batch (t): 0.967, 569.580/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,20:25:17 | INFO | Train Epoch: 1 [ 6758912/18327966 (37%)] Loss: 0.59361 (0.6371) Data (t): 0.001 Batch (t): 0.900, 567.530/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,20:26:47 | INFO | Train Epoch: 1 [ 6810112/18327966 (37%)] Loss: 0.65132 (0.6372) Data (t): 0.001 Batch (t): 0.900, 571.607/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,20:28:17 | INFO | Train Epoch: 1 [ 6861312/18327966 (37%)] Loss: 0.60318 (0.6369) Data (t): 0.001 Batch (t): 0.901, 570.878/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,20:29:50 | INFO | Train Epoch: 1 [ 6912512/18327966 (38%)] Loss: 0.64535 (0.6370) Data (t): 0.001 Batch (t): 0.929, 568.980/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,20:31:27 | INFO | Train Epoch: 1 [ 6963712/18327966 (38%)] Loss: 0.63702 (0.6370) Data (t): 0.001 Batch (t): 0.967, 569.709/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,20:32:58 | INFO | Train Epoch: 1 [ 7014912/18327966 (38%)] Loss: 0.58317 (0.6366) Data (t): 0.001 Batch (t): 0.912, 572.105/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,20:34:28 | INFO | Train Epoch: 1 [ 7066112/18327966 (39%)] Loss: 0.59440 (0.6363) Data (t): 0.001 Batch (t): 0.901, 569.626/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,20:35:58 | INFO | Train Epoch: 1 [ 7117312/18327966 (39%)] Loss: 0.63242 (0.6363) Data (t): 0.001 Batch (t): 0.901, 568.714/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,20:37:30 | INFO | Train Epoch: 1 [ 7168512/18327966 (39%)] Loss: 0.51860 (0.6354) Data (t): 0.001 Batch (t): 0.917, 568.053/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,20:39:07 | INFO | Train Epoch: 1 [ 7219712/18327966 (39%)] Loss: 0.57355 (0.6350) Data (t): 0.001 Batch (t): 0.968, 566.123/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,20:40:39 | INFO | Train Epoch: 1 [ 7270912/18327966 (40%)] Loss: 0.56554 (0.6345) Data (t): 0.001 Batch (t): 0.923, 571.369/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,20:42:09 | INFO | Train Epoch: 1 [ 7322112/18327966 (40%)] Loss: 0.58947 (0.6342) Data (t): 0.001 Batch (t): 0.901, 567.440/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,20:43:39 | INFO | Train Epoch: 1 [ 7373312/18327966 (40%)] Loss: 0.69236 (0.6346) Data (t): 0.001 Batch (t): 0.902, 568.081/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,20:45:11 | INFO | Train Epoch: 1 [ 7424512/18327966 (41%)] Loss: 0.56871 (0.6341) Data (t): 0.001 Batch (t): 0.918, 569.777/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,20:46:43 | INFO | Train Epoch: 1 [ 7475712/18327966 (41%)] Loss: 0.45271 (0.6329) Data (t): 0.001 Batch (t): 0.925, 570.946/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,20:48:20 | INFO | Train Epoch: 1 [ 7526912/18327966 (41%)] Loss: 0.65789 (0.6331) Data (t): 0.001 Batch (t): 0.968, 568.990/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,20:49:50 | INFO | Train Epoch: 1 [ 7578112/18327966 (41%)] Loss: 0.56757 (0.6326) Data (t): 0.001 Batch (t): 0.901, 569.247/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,20:51:20 | INFO | Train Epoch: 1 [ 7629312/18327966 (42%)] Loss: 0.62228 (0.6326) Data (t): 0.001 Batch (t): 0.901, 569.049/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,20:52:51 | INFO | Train Epoch: 1 [ 7680512/18327966 (42%)] Loss: 0.71062 (0.6331) Data (t): 0.001 Batch (t): 0.901, 570.811/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,20:54:25 | INFO | Train Epoch: 1 [ 7731712/18327966 (42%)] Loss: 0.58808 (0.6328) Data (t): 0.001 Batch (t): 0.940, 569.091/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,20:56:01 | INFO | Train Epoch: 1 [ 7782912/18327966 (42%)] Loss: 0.60946 (0.6326) Data (t): 0.001 Batch (t): 0.968, 568.472/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,20:57:31 | INFO | Train Epoch: 1 [ 7834112/18327966 (43%)] Loss: 0.64924 (0.6327) Data (t): 0.001 Batch (t): 0.901, 568.184/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,20:59:02 | INFO | Train Epoch: 1 [ 7885312/18327966 (43%)] Loss: 0.53260 (0.6321) Data (t): 0.001 Batch (t): 0.902, 565.022/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,21:00:32 | INFO | Train Epoch: 1 [ 7936512/18327966 (43%)] Loss: 0.62688 (0.6321) Data (t): 0.001 Batch (t): 0.902, 567.010/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,21:02:06 | INFO | Train Epoch: 1 [ 7987712/18327966 (44%)] Loss: 0.60319 (0.6319) Data (t): 0.001 Batch (t): 0.941, 570.983/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,21:03:43 | INFO | Train Epoch: 1 [ 8038912/18327966 (44%)] Loss: 0.59621 (0.6317) Data (t): 0.001 Batch (t): 0.967, 569.429/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,21:05:13 | INFO | Train Epoch: 1 [ 8090112/18327966 (44%)] Loss: 0.58048 (0.6313) Data (t): 0.001 Batch (t): 0.901, 564.660/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,21:06:43 | INFO | Train Epoch: 1 [ 8141312/18327966 (44%)] Loss: 0.62708 (0.6313) Data (t): 0.001 Batch (t): 0.901, 568.232/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,21:08:13 | INFO | Train Epoch: 1 [ 8192512/18327966 (45%)] Loss: 0.57844 (0.6310) Data (t): 0.001 Batch (t): 0.901, 568.631/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,21:09:45 | INFO | Train Epoch: 1 [ 8243712/18327966 (45%)] Loss: 0.56728 (0.6306) Data (t): 0.001 Batch (t): 0.918, 571.107/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,21:11:23 | INFO | Train Epoch: 1 [ 8294912/18327966 (45%)] Loss: 0.53030 (0.6300) Data (t): 0.001 Batch (t): 0.980, 568.251/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,21:12:54 | INFO | Train Epoch: 1 [ 8346112/18327966 (46%)] Loss: 0.57507 (0.6296) Data (t): 0.001 Batch (t): 0.913, 569.606/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,21:14:24 | INFO | Train Epoch: 1 [ 8397312/18327966 (46%)] Loss: 0.54851 (0.6291) Data (t): 0.001 Batch (t): 0.902, 568.562/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,21:15:54 | INFO | Train Epoch: 1 [ 8448512/18327966 (46%)] Loss: 0.71548 (0.6297) Data (t): 0.001 Batch (t): 0.902, 570.843/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,21:17:26 | INFO | Train Epoch: 1 [ 8499712/18327966 (46%)] Loss: 0.61815 (0.6296) Data (t): 0.001 Batch (t): 0.917, 567.860/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,21:19:03 | INFO | Train Epoch: 1 [ 8550912/18327966 (47%)] Loss: 0.47765 (0.6287) Data (t): 0.001 Batch (t): 0.969, 571.019/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,21:20:35 | INFO | Train Epoch: 1 [ 8602112/18327966 (47%)] Loss: 0.68050 (0.6290) Data (t): 0.001 Batch (t): 0.924, 567.535/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,21:22:06 | INFO | Train Epoch: 1 [ 8653312/18327966 (47%)] Loss: 0.55261 (0.6285) Data (t): 0.001 Batch (t): 0.902, 566.792/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,21:23:36 | INFO | Train Epoch: 1 [ 8704512/18327966 (47%)] Loss: 0.63608 (0.6286) Data (t): 0.001 Batch (t): 0.901, 569.350/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,21:25:08 | INFO | Train Epoch: 1 [ 8755712/18327966 (48%)] Loss: 0.54861 (0.6281) Data (t): 0.001 Batch (t): 0.919, 568.526/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,21:26:42 | INFO | Train Epoch: 1 [ 8806912/18327966 (48%)] Loss: 0.51132 (0.6275) Data (t): 0.001 Batch (t): 0.940, 568.390/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,21:28:16 | INFO | Train Epoch: 1 [ 8858112/18327966 (48%)] Loss: 0.58229 (0.6272) Data (t): 0.001 Batch (t): 0.941, 569.881/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,21:29:46 | INFO | Train Epoch: 1 [ 8909312/18327966 (49%)] Loss: 0.63818 (0.6273) Data (t): 0.001 Batch (t): 0.902, 566.275/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,21:31:16 | INFO | Train Epoch: 1 [ 8960512/18327966 (49%)] Loss: 0.67980 (0.6276) Data (t): 0.001 Batch (t): 0.902, 569.223/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,21:32:46 | INFO | Train Epoch: 1 [ 9011712/18327966 (49%)] Loss: 0.69166 (0.6279) Data (t): 0.001 Batch (t): 0.902, 566.922/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,21:34:21 | INFO | Train Epoch: 1 [ 9062912/18327966 (49%)] Loss: 0.47937 (0.6271) Data (t): 0.001 Batch (t): 0.942, 570.068/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,21:35:58 | INFO | Train Epoch: 1 [ 9114112/18327966 (50%)] Loss: 0.54870 (0.6266) Data (t): 0.001 Batch (t): 0.969, 568.793/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,21:37:28 | INFO | Train Epoch: 1 [ 9165312/18327966 (50%)] Loss: 0.69303 (0.6270) Data (t): 0.001 Batch (t): 0.902, 566.532/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,21:38:58 | INFO | Train Epoch: 1 [ 9216512/18327966 (50%)] Loss: 0.64252 (0.6271) Data (t): 0.001 Batch (t): 0.902, 568.315/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,21:40:28 | INFO | Train Epoch: 1 [ 9267712/18327966 (51%)] Loss: 0.69264 (0.6275) Data (t): 0.001 Batch (t): 0.902, 566.493/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,21:42:01 | INFO | Train Epoch: 1 [ 9318912/18327966 (51%)] Loss: 0.56287 (0.6271) Data (t): 0.001 Batch (t): 0.931, 569.146/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,21:43:38 | INFO | Train Epoch: 1 [ 9370112/18327966 (51%)] Loss: 0.57692 (0.6268) Data (t): 0.001 Batch (t): 0.970, 568.573/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,21:45:10 | INFO | Train Epoch: 1 [ 9421312/18327966 (51%)] Loss: 0.47260 (0.6260) Data (t): 0.001 Batch (t): 0.915, 569.583/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,21:46:40 | INFO | Train Epoch: 1 [ 9472512/18327966 (52%)] Loss: 0.61511 (0.6259) Data (t): 0.001 Batch (t): 0.904, 569.542/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,21:48:10 | INFO | Train Epoch: 1 [ 9523712/18327966 (52%)] Loss: 0.57121 (0.6256) Data (t): 0.001 Batch (t): 0.903, 564.930/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,21:49:42 | INFO | Train Epoch: 1 [ 9574912/18327966 (52%)] Loss: 0.61749 (0.6256) Data (t): 0.001 Batch (t): 0.920, 567.929/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,21:51:21 | INFO | Train Epoch: 1 [ 9626112/18327966 (53%)] Loss: 0.60026 (0.6255) Data (t): 0.001 Batch (t): 0.982, 568.484/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,21:52:52 | INFO | Train Epoch: 1 [ 9677312/18327966 (53%)] Loss: 0.65071 (0.6256) Data (t): 0.001 Batch (t): 0.915, 565.490/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,21:54:22 | INFO | Train Epoch: 1 [ 9728512/18327966 (53%)] Loss: 0.67810 (0.6259) Data (t): 0.001 Batch (t): 0.903, 567.014/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,21:55:53 | INFO | Train Epoch: 1 [ 9779712/18327966 (53%)] Loss: 0.74462 (0.6265) Data (t): 0.001 Batch (t): 0.902, 569.083/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,21:57:25 | INFO | Train Epoch: 1 [ 9830912/18327966 (54%)] Loss: 0.62114 (0.6265) Data (t): 0.001 Batch (t): 0.920, 565.826/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,21:59:00 | INFO | Train Epoch: 1 [ 9882112/18327966 (54%)] Loss: 0.58931 (0.6263) Data (t): 0.001 Batch (t): 0.958, 199.458/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,22:00:34 | INFO | Train Epoch: 1 [ 9933312/18327966 (54%)] Loss: 0.56175 (0.6259) Data (t): 0.001 Batch (t): 0.935, 570.339/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,22:02:04 | INFO | Train Epoch: 1 [ 9984512/18327966 (54%)] Loss: 0.66229 (0.6261) Data (t): 0.001 Batch (t): 0.901, 568.466/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,22:03:34 | INFO | Train Epoch: 1 [10035712/18327966 (55%)] Loss: 0.58074 (0.6259) Data (t): 0.001 Batch (t): 0.901, 567.150/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,22:05:04 | INFO | Train Epoch: 1 [10086912/18327966 (55%)] Loss: 0.54704 (0.6255) Data (t): 0.001 Batch (t): 0.902, 568.275/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,22:06:40 | INFO | Train Epoch: 1 [10138112/18327966 (55%)] Loss: 0.60373 (0.6254) Data (t): 0.001 Batch (t): 0.959, 566.265/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,22:08:15 | INFO | Train Epoch: 1 [10189312/18327966 (56%)] Loss: 0.67066 (0.6256) Data (t): 0.001 Batch (t): 0.952, 568.985/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,22:09:45 | INFO | Train Epoch: 1 [10240512/18327966 (56%)] Loss: 0.58304 (0.6254) Data (t): 0.001 Batch (t): 0.901, 568.014/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,22:11:15 | INFO | Train Epoch: 1 [10291712/18327966 (56%)] Loss: 0.60606 (0.6253) Data (t): 0.001 Batch (t): 0.902, 568.249/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,22:12:46 | INFO | Train Epoch: 1 [10342912/18327966 (56%)] Loss: 0.60943 (0.6252) Data (t): 0.001 Batch (t): 0.901, 567.287/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,22:14:18 | INFO | Train Epoch: 1 [10394112/18327966 (57%)] Loss: 0.60907 (0.6252) Data (t): 0.001 Batch (t): 0.929, 568.917/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,22:15:57 | INFO | Train Epoch: 1 [10445312/18327966 (57%)] Loss: 0.62285 (0.6251) Data (t): 0.001 Batch (t): 0.981, 565.036/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,22:17:27 | INFO | Train Epoch: 1 [10496512/18327966 (57%)] Loss: 0.63689 (0.6252) Data (t): 0.001 Batch (t): 0.902, 568.347/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,22:18:57 | INFO | Train Epoch: 1 [10547712/18327966 (58%)] Loss: 0.62376 (0.6252) Data (t): 0.001 Batch (t): 0.902, 567.275/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,22:20:27 | INFO | Train Epoch: 1 [10598912/18327966 (58%)] Loss: 0.55049 (0.6248) Data (t): 0.001 Batch (t): 0.902, 570.507/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,22:22:00 | INFO | Train Epoch: 1 [10650112/18327966 (58%)] Loss: 0.55870 (0.6245) Data (t): 0.001 Batch (t): 0.930, 566.813/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,22:23:37 | INFO | Train Epoch: 1 [10701312/18327966 (58%)] Loss: 0.63185 (0.6246) Data (t): 0.001 Batch (t): 0.969, 570.279/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,22:25:08 | INFO | Train Epoch: 1 [10752512/18327966 (59%)] Loss: 0.68574 (0.6248) Data (t): 0.001 Batch (t): 0.913, 569.952/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,22:26:39 | INFO | Train Epoch: 1 [10803712/18327966 (59%)] Loss: 0.67194 (0.6251) Data (t): 0.001 Batch (t): 0.901, 568.037/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,22:28:09 | INFO | Train Epoch: 1 [10854912/18327966 (59%)] Loss: 0.59767 (0.6249) Data (t): 0.001 Batch (t): 0.901, 567.899/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,22:29:40 | INFO | Train Epoch: 1 [10906112/18327966 (60%)] Loss: 0.53840 (0.6245) Data (t): 0.001 Batch (t): 0.918, 570.605/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,22:31:19 | INFO | Train Epoch: 1 [10957312/18327966 (60%)] Loss: 0.63440 (0.6246) Data (t): 0.001 Batch (t): 0.984, 570.068/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,22:32:50 | INFO | Train Epoch: 1 [11008512/18327966 (60%)] Loss: 0.58610 (0.6244) Data (t): 0.001 Batch (t): 0.915, 568.700/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,22:34:21 | INFO | Train Epoch: 1 [11059712/18327966 (60%)] Loss: 0.74144 (0.6249) Data (t): 0.001 Batch (t): 0.903, 568.446/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,22:35:51 | INFO | Train Epoch: 1 [11110912/18327966 (61%)] Loss: 0.61634 (0.6249) Data (t): 0.001 Batch (t): 0.903, 565.260/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,22:37:23 | INFO | Train Epoch: 1 [11162112/18327966 (61%)] Loss: 0.56088 (0.6246) Data (t): 0.001 Batch (t): 0.920, 567.528/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,22:38:57 | INFO | Train Epoch: 1 [11213312/18327966 (61%)] Loss: 0.69764 (0.6249) Data (t): 0.001 Batch (t): 0.942, 565.603/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,22:40:33 | INFO | Train Epoch: 1 [11264512/18327966 (61%)] Loss: 0.58879 (0.6248) Data (t): 0.001 Batch (t): 0.955, 566.528/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,22:42:03 | INFO | Train Epoch: 1 [11315712/18327966 (62%)] Loss: 0.53682 (0.6244) Data (t): 0.001 Batch (t): 0.903, 566.842/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,22:43:33 | INFO | Train Epoch: 1 [11366912/18327966 (62%)] Loss: 0.61320 (0.6243) Data (t): 0.001 Batch (t): 0.903, 568.481/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,22:45:05 | INFO | Train Epoch: 1 [11418112/18327966 (62%)] Loss: 0.69476 (0.6246) Data (t): 0.001 Batch (t): 0.920, 567.494/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,22:46:38 | INFO | Train Epoch: 1 [11469312/18327966 (63%)] Loss: 0.58706 (0.6245) Data (t): 0.001 Batch (t): 0.930, 567.973/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,22:48:14 | INFO | Train Epoch: 1 [11520512/18327966 (63%)] Loss: 0.59498 (0.6243) Data (t): 0.001 Batch (t): 0.965, 568.196/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,22:49:45 | INFO | Train Epoch: 1 [11571712/18327966 (63%)] Loss: 0.46364 (0.6236) Data (t): 0.001 Batch (t): 0.903, 567.379/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,22:51:15 | INFO | Train Epoch: 1 [11622912/18327966 (63%)] Loss: 0.61863 (0.6236) Data (t): 0.001 Batch (t): 0.902, 570.291/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,22:52:45 | INFO | Train Epoch: 1 [11674112/18327966 (64%)] Loss: 0.51260 (0.6231) Data (t): 0.001 Batch (t): 0.902, 567.916/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,22:54:18 | INFO | Train Epoch: 1 [11725312/18327966 (64%)] Loss: 0.73644 (0.6236) Data (t): 0.001 Batch (t): 0.929, 569.262/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,22:55:56 | INFO | Train Epoch: 1 [11776512/18327966 (64%)] Loss: 0.65804 (0.6238) Data (t): 0.001 Batch (t): 0.981, 570.954/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,22:57:26 | INFO | Train Epoch: 1 [11827712/18327966 (65%)] Loss: 0.54333 (0.6234) Data (t): 0.001 Batch (t): 0.903, 570.790/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,22:58:57 | INFO | Train Epoch: 1 [11878912/18327966 (65%)] Loss: 0.56439 (0.6232) Data (t): 0.001 Batch (t): 0.904, 567.103/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,23:00:27 | INFO | Train Epoch: 1 [11930112/18327966 (65%)] Loss: 0.57595 (0.6230) Data (t): 0.001 Batch (t): 0.902, 568.843/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,23:02:00 | INFO | Train Epoch: 1 [11981312/18327966 (65%)] Loss: 0.63309 (0.6230) Data (t): 0.001 Batch (t): 0.931, 567.452/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,23:03:37 | INFO | Train Epoch: 1 [12032512/18327966 (66%)] Loss: 0.57727 (0.6228) Data (t): 0.001 Batch (t): 0.971, 566.544/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,23:05:09 | INFO | Train Epoch: 1 [12083712/18327966 (66%)] Loss: 0.43816 (0.6220) Data (t): 0.001 Batch (t): 0.914, 565.930/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,23:06:39 | INFO | Train Epoch: 1 [12134912/18327966 (66%)] Loss: 0.66541 (0.6222) Data (t): 0.001 Batch (t): 0.902, 567.790/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,23:08:09 | INFO | Train Epoch: 1 [12186112/18327966 (66%)] Loss: 0.54514 (0.6219) Data (t): 0.001 Batch (t): 0.902, 568.404/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,23:09:41 | INFO | Train Epoch: 1 [12237312/18327966 (67%)] Loss: 0.61969 (0.6219) Data (t): 0.001 Batch (t): 0.919, 568.853/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,23:11:17 | INFO | Train Epoch: 1 [12288512/18327966 (67%)] Loss: 0.73086 (0.6223) Data (t): 0.001 Batch (t): 0.961, 567.518/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,23:12:51 | INFO | Train Epoch: 1 [12339712/18327966 (67%)] Loss: 0.54443 (0.6220) Data (t): 0.001 Batch (t): 0.938, 562.956/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,23:14:21 | INFO | Train Epoch: 1 [12390912/18327966 (68%)] Loss: 0.58616 (0.6219) Data (t): 0.001 Batch (t): 0.902, 568.172/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,23:15:51 | INFO | Train Epoch: 1 [12442112/18327966 (68%)] Loss: 0.58574 (0.6217) Data (t): 0.001 Batch (t): 0.903, 566.395/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,23:17:23 | INFO | Train Epoch: 1 [12493312/18327966 (68%)] Loss: 0.61354 (0.6217) Data (t): 0.001 Batch (t): 0.920, 568.326/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,23:18:56 | INFO | Train Epoch: 1 [12544512/18327966 (68%)] Loss: 0.61075 (0.6216) Data (t): 0.001 Batch (t): 0.931, 568.450/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,23:20:33 | INFO | Train Epoch: 1 [12595712/18327966 (69%)] Loss: 0.61022 (0.6216) Data (t): 0.001 Batch (t): 0.968, 568.590/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,23:22:03 | INFO | Train Epoch: 1 [12646912/18327966 (69%)] Loss: 0.53721 (0.6213) Data (t): 0.001 Batch (t): 0.903, 566.796/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,23:23:34 | INFO | Train Epoch: 1 [12698112/18327966 (69%)] Loss: 0.49537 (0.6208) Data (t): 0.001 Batch (t): 0.902, 567.150/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,23:25:06 | INFO | Train Epoch: 1 [12749312/18327966 (70%)] Loss: 0.58882 (0.6206) Data (t): 0.001 Batch (t): 0.920, 567.105/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,23:26:39 | INFO | Train Epoch: 1 [12800512/18327966 (70%)] Loss: 0.70294 (0.6210) Data (t): 0.001 Batch (t): 0.931, 567.667/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,23:28:14 | INFO | Train Epoch: 1 [12851712/18327966 (70%)] Loss: 0.52667 (0.6206) Data (t): 0.001 Batch (t): 0.956, 568.060/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,23:29:45 | INFO | Train Epoch: 1 [12902912/18327966 (70%)] Loss: 0.65766 (0.6207) Data (t): 0.001 Batch (t): 0.903, 566.776/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,23:31:15 | INFO | Train Epoch: 1 [12954112/18327966 (71%)] Loss: 0.56185 (0.6205) Data (t): 0.001 Batch (t): 0.902, 567.306/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,23:32:45 | INFO | Train Epoch: 1 [13005312/18327966 (71%)] Loss: 0.59604 (0.6204) Data (t): 0.001 Batch (t): 0.902, 568.355/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,23:34:18 | INFO | Train Epoch: 1 [13056512/18327966 (71%)] Loss: 0.65782 (0.6205) Data (t): 0.001 Batch (t): 0.932, 568.314/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,23:35:56 | INFO | Train Epoch: 1 [13107712/18327966 (72%)] Loss: 0.56068 (0.6203) Data (t): 0.001 Batch (t): 0.972, 569.072/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,23:37:27 | INFO | Train Epoch: 1 [13158912/18327966 (72%)] Loss: 0.68509 (0.6206) Data (t): 0.001 Batch (t): 0.915, 566.125/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,23:38:57 | INFO | Train Epoch: 1 [13210112/18327966 (72%)] Loss: 0.57500 (0.6204) Data (t): 0.001 Batch (t): 0.902, 568.914/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,23:40:28 | INFO | Train Epoch: 1 [13261312/18327966 (72%)] Loss: 0.67852 (0.6206) Data (t): 0.001 Batch (t): 0.903, 568.451/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,23:42:00 | INFO | Train Epoch: 1 [13312512/18327966 (73%)] Loss: 0.64007 (0.6207) Data (t): 0.001 Batch (t): 0.920, 566.831/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,23:43:38 | INFO | Train Epoch: 1 [13363712/18327966 (73%)] Loss: 0.54181 (0.6204) Data (t): 0.001 Batch (t): 0.984, 569.957/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,23:45:09 | INFO | Train Epoch: 1 [13414912/18327966 (73%)] Loss: 0.55892 (0.6202) Data (t): 0.001 Batch (t): 0.914, 566.980/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,23:46:40 | INFO | Train Epoch: 1 [13466112/18327966 (73%)] Loss: 0.67826 (0.6204) Data (t): 0.001 Batch (t): 0.903, 569.004/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,23:48:10 | INFO | Train Epoch: 1 [13517312/18327966 (74%)] Loss: 0.58789 (0.6203) Data (t): 0.001 Batch (t): 0.903, 567.594/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,23:49:42 | INFO | Train Epoch: 1 [13568512/18327966 (74%)] Loss: 0.48123 (0.6197) Data (t): 0.001 Batch (t): 0.921, 566.198/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,23:51:18 | INFO | Train Epoch: 1 [13619712/18327966 (74%)] Loss: 0.61952 (0.6197) Data (t): 0.001 Batch (t): 0.962, 568.374/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,23:52:52 | INFO | Train Epoch: 1 [13670912/18327966 (75%)] Loss: 0.56987 (0.6195) Data (t): 0.001 Batch (t): 0.939, 568.534/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,23:54:22 | INFO | Train Epoch: 1 [13722112/18327966 (75%)] Loss: 0.58001 (0.6194) Data (t): 0.001 Batch (t): 0.903, 569.756/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,23:55:53 | INFO | Train Epoch: 1 [13773312/18327966 (75%)] Loss: 0.54149 (0.6191) Data (t): 0.001 Batch (t): 0.903, 566.563/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,23:57:25 | INFO | Train Epoch: 1 [13824512/18327966 (75%)] Loss: 0.50438 (0.6187) Data (t): 0.001 Batch (t): 0.920, 568.245/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,23:58:58 | INFO | Train Epoch: 1 [13875712/18327966 (76%)] Loss: 0.60226 (0.6186) Data (t): 0.001 Batch (t): 0.932, 568.338/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,00:00:35 | INFO | Train Epoch: 1 [13926912/18327966 (76%)] Loss: 0.63655 (0.6187) Data (t): 0.001 Batch (t): 0.968, 568.863/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,00:02:05 | INFO | Train Epoch: 1 [13978112/18327966 (76%)] Loss: 0.62939 (0.6187) Data (t): 0.001 Batch (t): 0.902, 566.294/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,00:03:35 | INFO | Train Epoch: 1 [14029312/18327966 (77%)] Loss: 0.66561 (0.6189) Data (t): 0.001 Batch (t): 0.903, 565.253/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,00:05:07 | INFO | Train Epoch: 1 [14080512/18327966 (77%)] Loss: 0.54858 (0.6186) Data (t): 0.001 Batch (t): 0.920, 567.077/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,00:06:40 | INFO | Train Epoch: 1 [14131712/18327966 (77%)] Loss: 0.60786 (0.6186) Data (t): 0.001 Batch (t): 0.930, 569.290/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,00:08:16 | INFO | Train Epoch: 1 [14182912/18327966 (77%)] Loss: 0.62856 (0.6186) Data (t): 0.001 Batch (t): 0.955, 568.900/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,00:09:47 | INFO | Train Epoch: 1 [14234112/18327966 (78%)] Loss: 0.60104 (0.6186) Data (t): 0.001 Batch (t): 0.912, 566.103/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,00:11:17 | INFO | Train Epoch: 1 [14285312/18327966 (78%)] Loss: 0.62492 (0.6186) Data (t): 0.001 Batch (t): 0.901, 567.374/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,00:12:47 | INFO | Train Epoch: 1 [14336512/18327966 (78%)] Loss: 0.59880 (0.6185) Data (t): 0.001 Batch (t): 0.902, 567.289/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,00:14:20 | INFO | Train Epoch: 1 [14387712/18327966 (79%)] Loss: 0.64147 (0.6186) Data (t): 0.001 Batch (t): 0.931, 568.560/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,00:15:58 | INFO | Train Epoch: 1 [14438912/18327966 (79%)] Loss: 0.59065 (0.6185) Data (t): 0.001 Batch (t): 0.973, 570.005/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,00:17:29 | INFO | Train Epoch: 1 [14490112/18327966 (79%)] Loss: 0.64226 (0.6186) Data (t): 0.001 Batch (t): 0.914, 569.844/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,00:18:59 | INFO | Train Epoch: 1 [14541312/18327966 (79%)] Loss: 0.63610 (0.6187) Data (t): 0.001 Batch (t): 0.903, 565.896/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,00:20:30 | INFO | Train Epoch: 1 [14592512/18327966 (80%)] Loss: 0.55018 (0.6184) Data (t): 0.001 Batch (t): 0.902, 570.684/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,00:22:01 | INFO | Train Epoch: 1 [14643712/18327966 (80%)] Loss: 0.56490 (0.6182) Data (t): 0.001 Batch (t): 0.919, 569.259/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,00:23:38 | INFO | Train Epoch: 1 [14694912/18327966 (80%)] Loss: 0.62516 (0.6183) Data (t): 0.001 Batch (t): 0.961, 570.288/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,00:25:11 | INFO | Train Epoch: 1 [14746112/18327966 (80%)] Loss: 0.67262 (0.6184) Data (t): 0.001 Batch (t): 0.937, 567.074/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,00:26:42 | INFO | Train Epoch: 1 [14797312/18327966 (81%)] Loss: 0.52656 (0.6181) Data (t): 0.001 Batch (t): 0.902, 566.045/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,00:28:12 | INFO | Train Epoch: 1 [14848512/18327966 (81%)] Loss: 0.54204 (0.6179) Data (t): 0.001 Batch (t): 0.901, 568.107/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,00:29:44 | INFO | Train Epoch: 1 [14899712/18327966 (81%)] Loss: 0.60959 (0.6178) Data (t): 0.001 Batch (t): 0.919, 566.661/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,00:31:18 | INFO | Train Epoch: 1 [14950912/18327966 (82%)] Loss: 0.56368 (0.6177) Data (t): 0.001 Batch (t): 0.949, 570.998/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,00:32:53 | INFO | Train Epoch: 1 [15002112/18327966 (82%)] Loss: 0.61042 (0.6176) Data (t): 0.001 Batch (t): 0.950, 567.588/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,00:34:24 | INFO | Train Epoch: 1 [15053312/18327966 (82%)] Loss: 0.55611 (0.6174) Data (t): 0.001 Batch (t): 0.902, 568.869/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,00:35:54 | INFO | Train Epoch: 1 [15104512/18327966 (82%)] Loss: 0.56028 (0.6172) Data (t): 0.001 Batch (t): 0.901, 567.846/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,00:37:26 | INFO | Train Epoch: 1 [15155712/18327966 (83%)] Loss: 0.61938 (0.6172) Data (t): 0.001 Batch (t): 0.918, 566.157/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,00:38:59 | INFO | Train Epoch: 1 [15206912/18327966 (83%)] Loss: 0.57830 (0.6171) Data (t): 0.001 Batch (t): 0.930, 567.467/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,00:40:35 | INFO | Train Epoch: 1 [15258112/18327966 (83%)] Loss: 0.50831 (0.6167) Data (t): 0.001 Batch (t): 0.968, 567.196/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,00:42:06 | INFO | Train Epoch: 1 [15309312/18327966 (84%)] Loss: 0.63041 (0.6168) Data (t): 0.001 Batch (t): 0.902, 567.127/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,00:43:36 | INFO | Train Epoch: 1 [15360512/18327966 (84%)] Loss: 0.57188 (0.6166) Data (t): 0.001 Batch (t): 0.902, 567.925/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,00:45:08 | INFO | Train Epoch: 1 [15411712/18327966 (84%)] Loss: 0.53003 (0.6163) Data (t): 0.001 Batch (t): 0.919, 567.923/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,00:46:41 | INFO | Train Epoch: 1 [15462912/18327966 (84%)] Loss: 0.59529 (0.6163) Data (t): 0.001 Batch (t): 0.931, 570.308/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,00:48:16 | INFO | Train Epoch: 1 [15514112/18327966 (85%)] Loss: 0.60234 (0.6162) Data (t): 0.001 Batch (t): 0.956, 569.746/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,00:49:48 | INFO | Train Epoch: 1 [15565312/18327966 (85%)] Loss: 0.54203 (0.6160) Data (t): 0.001 Batch (t): 0.914, 568.435/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,00:51:18 | INFO | Train Epoch: 1 [15616512/18327966 (85%)] Loss: 0.70118 (0.6163) Data (t): 0.001 Batch (t): 0.902, 565.743/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,00:52:48 | INFO | Train Epoch: 1 [15667712/18327966 (85%)] Loss: 0.59888 (0.6162) Data (t): 0.001 Batch (t): 0.902, 564.961/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,00:54:22 | INFO | Train Epoch: 1 [15718912/18327966 (86%)] Loss: 0.45520 (0.6157) Data (t): 0.001 Batch (t): 0.933, 570.229/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,00:55:58 | INFO | Train Epoch: 1 [15770112/18327966 (86%)] Loss: 0.62802 (0.6157) Data (t): 0.001 Batch (t): 0.962, 567.128/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,00:57:30 | INFO | Train Epoch: 1 [15821312/18327966 (86%)] Loss: 0.53581 (0.6155) Data (t): 0.001 Batch (t): 0.927, 567.231/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,00:59:01 | INFO | Train Epoch: 1 [15872512/18327966 (87%)] Loss: 0.54488 (0.6152) Data (t): 0.001 Batch (t): 0.903, 567.228/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,01:00:31 | INFO | Train Epoch: 1 [15923712/18327966 (87%)] Loss: 0.64181 (0.6153) Data (t): 0.001 Batch (t): 0.903, 565.341/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,01:02:03 | INFO | Train Epoch: 1 [15974912/18327966 (87%)] Loss: 0.52856 (0.6151) Data (t): 0.001 Batch (t): 0.920, 568.882/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,01:03:39 | INFO | Train Epoch: 1 [16026112/18327966 (87%)] Loss: 0.57490 (0.6149) Data (t): 0.001 Batch (t): 0.963, 567.962/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,01:05:13 | INFO | Train Epoch: 1 [16077312/18327966 (88%)] Loss: 0.68428 (0.6151) Data (t): 0.001 Batch (t): 0.940, 568.184/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,01:06:44 | INFO | Train Epoch: 1 [16128512/18327966 (88%)] Loss: 0.55432 (0.6150) Data (t): 0.001 Batch (t): 0.903, 566.554/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,01:08:14 | INFO | Train Epoch: 1 [16179712/18327966 (88%)] Loss: 0.57250 (0.6148) Data (t): 0.001 Batch (t): 0.902, 569.466/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,01:09:46 | INFO | Train Epoch: 1 [16230912/18327966 (89%)] Loss: 0.55809 (0.6146) Data (t): 0.001 Batch (t): 0.920, 569.941/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,01:11:19 | INFO | Train Epoch: 1 [16282112/18327966 (89%)] Loss: 0.57564 (0.6145) Data (t): 0.001 Batch (t): 0.932, 568.728/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,01:12:56 | INFO | Train Epoch: 1 [16333312/18327966 (89%)] Loss: 0.60250 (0.6145) Data (t): 0.001 Batch (t): 0.970, 569.989/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,01:14:26 | INFO | Train Epoch: 1 [16384512/18327966 (89%)] Loss: 0.50608 (0.6141) Data (t): 0.001 Batch (t): 0.903, 570.266/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,01:15:56 | INFO | Train Epoch: 1 [16435712/18327966 (90%)] Loss: 0.51684 (0.6138) Data (t): 0.001 Batch (t): 0.902, 567.845/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,01:17:28 | INFO | Train Epoch: 1 [16486912/18327966 (90%)] Loss: 0.53851 (0.6136) Data (t): 0.001 Batch (t): 0.920, 567.807/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,01:19:02 | INFO | Train Epoch: 1 [16538112/18327966 (90%)] Loss: 0.64771 (0.6137) Data (t): 0.001 Batch (t): 0.933, 568.342/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,01:20:39 | INFO | Train Epoch: 1 [16589312/18327966 (91%)] Loss: 0.60126 (0.6137) Data (t): 0.001 Batch (t): 0.970, 569.184/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,01:22:09 | INFO | Train Epoch: 1 [16640512/18327966 (91%)] Loss: 0.43740 (0.6131) Data (t): 0.001 Batch (t): 0.903, 567.657/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,01:23:39 | INFO | Train Epoch: 1 [16691712/18327966 (91%)] Loss: 0.57917 (0.6130) Data (t): 0.001 Batch (t): 0.903, 568.651/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,01:25:11 | INFO | Train Epoch: 1 [16742912/18327966 (91%)] Loss: 0.56121 (0.6129) Data (t): 0.001 Batch (t): 0.921, 567.507/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,01:26:45 | INFO | Train Epoch: 1 [16794112/18327966 (92%)] Loss: 0.59925 (0.6128) Data (t): 0.001 Batch (t): 0.932, 567.866/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,01:28:20 | INFO | Train Epoch: 1 [16845312/18327966 (92%)] Loss: 0.59546 (0.6128) Data (t): 0.001 Batch (t): 0.958, 568.514/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,01:29:52 | INFO | Train Epoch: 1 [16896512/18327966 (92%)] Loss: 0.61423 (0.6128) Data (t): 0.001 Batch (t): 0.915, 564.108/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,01:31:22 | INFO | Train Epoch: 1 [16947712/18327966 (92%)] Loss: 0.53663 (0.6126) Data (t): 0.001 Batch (t): 0.903, 565.532/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,01:32:52 | INFO | Train Epoch: 1 [16998912/18327966 (93%)] Loss: 0.65640 (0.6127) Data (t): 0.001 Batch (t): 0.903, 567.294/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,01:34:26 | INFO | Train Epoch: 1 [17050112/18327966 (93%)] Loss: 0.55614 (0.6125) Data (t): 0.001 Batch (t): 0.933, 569.400/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,01:36:02 | INFO | Train Epoch: 1 [17101312/18327966 (93%)] Loss: 0.58397 (0.6124) Data (t): 0.001 Batch (t): 0.963, 568.529/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,01:37:35 | INFO | Train Epoch: 1 [17152512/18327966 (94%)] Loss: 0.56340 (0.6123) Data (t): 0.001 Batch (t): 0.927, 568.013/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,01:39:05 | INFO | Train Epoch: 1 [17203712/18327966 (94%)] Loss: 0.63362 (0.6123) Data (t): 0.001 Batch (t): 0.902, 565.958/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,01:40:35 | INFO | Train Epoch: 1 [17254912/18327966 (94%)] Loss: 0.66356 (0.6125) Data (t): 0.001 Batch (t): 0.902, 567.272/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,01:42:07 | INFO | Train Epoch: 1 [17306112/18327966 (94%)] Loss: 0.64943 (0.6126) Data (t): 0.001 Batch (t): 0.920, 567.573/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,01:43:42 | INFO | Train Epoch: 1 [17357312/18327966 (95%)] Loss: 0.59497 (0.6126) Data (t): 0.001 Batch (t): 0.951, 567.834/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,01:45:17 | INFO | Train Epoch: 1 [17408512/18327966 (95%)] Loss: 0.54321 (0.6124) Data (t): 0.001 Batch (t): 0.954, 568.283/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,01:46:48 | INFO | Train Epoch: 1 [17459712/18327966 (95%)] Loss: 0.57501 (0.6122) Data (t): 0.001 Batch (t): 0.902, 566.135/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,01:48:18 | INFO | Train Epoch: 1 [17510912/18327966 (96%)] Loss: 0.61556 (0.6123) Data (t): 0.001 Batch (t): 0.902, 566.794/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,01:49:50 | INFO | Train Epoch: 1 [17562112/18327966 (96%)] Loss: 0.54626 (0.6121) Data (t): 0.001 Batch (t): 0.920, 567.581/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,01:51:25 | INFO | Train Epoch: 1 [17613312/18327966 (96%)] Loss: 0.59020 (0.6120) Data (t): 0.001 Batch (t): 0.950, 189.812/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,01:53:00 | INFO | Train Epoch: 1 [17664512/18327966 (96%)] Loss: 0.62160 (0.6120) Data (t): 0.001 Batch (t): 0.953, 570.529/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,01:54:30 | INFO | Train Epoch: 1 [17715712/18327966 (97%)] Loss: 0.51713 (0.6118) Data (t): 0.001 Batch (t): 0.902, 567.433/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,01:56:01 | INFO | Train Epoch: 1 [17766912/18327966 (97%)] Loss: 0.58933 (0.6117) Data (t): 0.001 Batch (t): 0.903, 568.888/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,01:57:33 | INFO | Train Epoch: 1 [17818112/18327966 (97%)] Loss: 0.54346 (0.6115) Data (t): 0.001 Batch (t): 0.920, 568.467/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,01:59:06 | INFO | Train Epoch: 1 [17869312/18327966 (97%)] Loss: 0.60093 (0.6115) Data (t): 0.001 Batch (t): 0.932, 569.721/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,02:00:42 | INFO | Train Epoch: 1 [17920512/18327966 (98%)] Loss: 0.71719 (0.6118) Data (t): 0.001 Batch (t): 0.958, 566.916/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,02:02:13 | INFO | Train Epoch: 1 [17971712/18327966 (98%)] Loss: 0.61262 (0.6118) Data (t): 0.001 Batch (t): 0.915, 566.495/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,02:03:44 | INFO | Train Epoch: 1 [18022912/18327966 (98%)] Loss: 0.64186 (0.6118) Data (t): 0.001 Batch (t): 0.903, 568.554/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,02:05:14 | INFO | Train Epoch: 1 [18074112/18327966 (99%)] Loss: 0.55112 (0.6117) Data (t): 0.001 Batch (t): 0.903, 569.757/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,02:06:49 | INFO | Train Epoch: 1 [18125312/18327966 (99%)] Loss: 0.69282 (0.6119) Data (t): 0.001 Batch (t): 0.951, 568.845/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,02:08:25 | INFO | Train Epoch: 1 [18176512/18327966 (99%)] Loss: 0.70957 (0.6122) Data (t): 0.001 Batch (t): 0.958, 568.585/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,02:09:56 | INFO | Train Epoch: 1 [18227712/18327966 (99%)] Loss: 0.49633 (0.6119) Data (t): 0.001 Batch (t): 0.915, 566.538/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,02:11:26 | INFO | Train Epoch: 1 [18278912/18327966 (100%)] Loss: 0.51686 (0.6116) Data (t): 0.001 Batch (t): 0.902, 566.741/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,02:12:52 | INFO | Train Epoch: 1 [18327552/18327966 (100%)] Loss: 0.61057 (0.6116) Data (t): 0.002 Batch (t): 0.903, 570.783/s LR: 0.000000 Logit Scale: 100.000 - V4 diff --git a/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten/2024_11_27-07_49_03-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/params.txt b/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten/2024_11_27-07_49_03-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/params.txt deleted file mode 100644 index 5fcf3321ebcec0f98712daba381f7f0cde6de92d..0000000000000000000000000000000000000000 --- a/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten/2024_11_27-07_49_03-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/params.txt +++ /dev/null @@ -1,67 +0,0 @@ -batch_size: 64 -beta1: 0.9 -beta2: 0.98 -checkpoint_path: data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten/2024_11_27-07_49_03-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/checkpoints -copy_codebase: False -csv_caption_key: caption -csv_hard_captions_key: neg_caption -csv_img_key: img_path -csv_separator: , -dataset_resampled: False -dataset_type: csv -ddp_static_graph: False -debug: False -device: cuda:0 -dist_backend: nccl -dist_url: env:// -distributed: True -epochs: 2 -eps: 1e-06 -force_quick_gelu: True -gather_with_grad: False -grad_checkpointing: False -horovod: False -imagenet_v2: None -imagenet_val: None -local_loss: False -local_rank: 0 -lock_image: False -lock_image_freeze_bn_stats: False -lock_image_unlocked_groups: 0 -log_level: 20 -log_local: False -log_path: data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten/2024_11_27-07_49_03-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/out.log -logs: data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten -lr: 5e-06 -model: ViT-L-14-336 -name: 2024_11_27-07_49_03-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp -no_set_device_rank: False -norm_gradient_clip: None -precision: amp -pretrained: data/openclip-vit-14-336/openclip_model.pt -pretrained_image: False -rank: 0 -report_to: wandb -resume: None -save_frequency: 1 -save_most_recent: False -seed: 0 -skip_scheduler: False -tensorboard: False -tensorboard_path: -torchscript: False -trace: False -train_data: csv_data/plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten.csv -train_num_samples: None -use_bn_sync: False -val_data: None -val_frequency: 1 -val_num_samples: None -wandb: True -wandb_notes: -wandb_project: neg-clip-plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten -warmup: 0 -wd: 0.1 -workers: 4 -world_size: 8 -zeroshot_frequency: 2 diff --git a/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten_decimal2/2024_11_26-13_30_24-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/checkpoints/epoch_1.pt b/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten_decimal2/2024_11_26-13_30_24-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/checkpoints/epoch_1.pt deleted file mode 100644 index 0ce43c39cefc012d2bf71af946b25ef0b3437f9a..0000000000000000000000000000000000000000 --- a/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten_decimal2/2024_11_26-13_30_24-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/checkpoints/epoch_1.pt +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:16a78771e06baa25ad04201db18113513e36d41127f2f1d8ae8fff2b90060b18 -size 5135890710 diff --git a/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten_decimal2/2024_11_26-13_30_24-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/checkpoints/epoch_2.pt b/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten_decimal2/2024_11_26-13_30_24-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/checkpoints/epoch_2.pt deleted file mode 100644 index aa17e52620c9bb58ebf23d9b88a40d91f079f49f..0000000000000000000000000000000000000000 --- a/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten_decimal2/2024_11_26-13_30_24-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/checkpoints/epoch_2.pt +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:345d9f19643f73c3c2c95fb04d0b37070d82feae3214029bb61ac873a350aa1a -size 5135890710 diff --git a/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten_decimal2/2024_11_26-13_30_24-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/out.log b/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten_decimal2/2024_11_26-13_30_24-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/out.log deleted file mode 100644 index e20c389f6bb73a2ef7eb5d7807c0537fb88d83f7..0000000000000000000000000000000000000000 --- a/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten_decimal2/2024_11_26-13_30_24-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/out.log +++ /dev/null @@ -1,834 +0,0 @@ -2024-11-26,13:30:25 | INFO | Running in distributed mode with multiple processes. Device: cuda:0.Process (global: 0, local 0), total 8. -2024-11-26,13:30:25 | INFO | Loading ViT-L-14-336 model config. -2024-11-26,13:30:28 | INFO | Loading pretrained ViT-L-14-336 weights (data/openclip-vit-14-336/openclip_model.pt). -2024-11-26,13:30:37 | INFO | Model: -2024-11-26,13:30:37 | INFO | CLIP( - (visual): VisualTransformer( - (conv1): Conv2d(3, 1024, kernel_size=(14, 14), stride=(14, 14), bias=False) - (ln_pre): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) - (transformer): Transformer( - (resblocks): ModuleList( - (0-23): 24 x ResidualAttentionBlock( - (attn): MultiheadAttention( - (out_proj): NonDynamicallyQuantizableLinear(in_features=1024, out_features=1024, bias=True) - ) - (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) - (mlp): Sequential( - (c_fc): Linear(in_features=1024, out_features=4096, bias=True) - (gelu): QuickGELU() - (c_proj): Linear(in_features=4096, out_features=1024, bias=True) - ) - (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) - ) - ) - ) - (ln_post): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) - ) - (transformer): Transformer( - (resblocks): ModuleList( - (0-11): 12 x ResidualAttentionBlock( - (attn): MultiheadAttention( - (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) - ) - (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) - (mlp): Sequential( - (c_fc): Linear(in_features=768, out_features=3072, bias=True) - (gelu): QuickGELU() - (c_proj): Linear(in_features=3072, out_features=768, bias=True) - ) - (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) - ) - ) - ) - (token_embedding): Embedding(49408, 768) - (ln_final): LayerNorm((768,), eps=1e-05, elementwise_affine=True) -) -2024-11-26,13:30:37 | INFO | Params: -2024-11-26,13:30:37 | INFO | batch_size: 64 -2024-11-26,13:30:37 | INFO | beta1: 0.9 -2024-11-26,13:30:37 | INFO | beta2: 0.98 -2024-11-26,13:30:37 | INFO | checkpoint_path: data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten_decimal2/2024_11_26-13_30_24-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/checkpoints -2024-11-26,13:30:37 | INFO | copy_codebase: False -2024-11-26,13:30:37 | INFO | csv_caption_key: caption -2024-11-26,13:30:37 | INFO | csv_hard_captions_key: neg_caption -2024-11-26,13:30:37 | INFO | csv_img_key: img_path -2024-11-26,13:30:37 | INFO | csv_separator: , -2024-11-26,13:30:37 | INFO | dataset_resampled: False -2024-11-26,13:30:37 | INFO | dataset_type: csv -2024-11-26,13:30:37 | INFO | ddp_static_graph: False -2024-11-26,13:30:37 | INFO | debug: False -2024-11-26,13:30:37 | INFO | device: cuda:0 -2024-11-26,13:30:37 | INFO | dist_backend: nccl -2024-11-26,13:30:37 | INFO | dist_url: env:// -2024-11-26,13:30:37 | INFO | distributed: True -2024-11-26,13:30:37 | INFO | epochs: 2 -2024-11-26,13:30:37 | INFO | eps: 1e-06 -2024-11-26,13:30:37 | INFO | force_quick_gelu: True -2024-11-26,13:30:37 | INFO | gather_with_grad: False -2024-11-26,13:30:37 | INFO | grad_checkpointing: False -2024-11-26,13:30:37 | INFO | horovod: False -2024-11-26,13:30:37 | INFO | imagenet_v2: None -2024-11-26,13:30:37 | INFO | imagenet_val: None -2024-11-26,13:30:37 | INFO | local_loss: False -2024-11-26,13:30:37 | INFO | local_rank: 0 -2024-11-26,13:30:37 | INFO | lock_image: False -2024-11-26,13:30:37 | INFO | lock_image_freeze_bn_stats: False -2024-11-26,13:30:37 | INFO | lock_image_unlocked_groups: 0 -2024-11-26,13:30:37 | INFO | log_level: 20 -2024-11-26,13:30:37 | INFO | log_local: False -2024-11-26,13:30:37 | INFO | log_path: data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten_decimal2/2024_11_26-13_30_24-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/out.log -2024-11-26,13:30:37 | INFO | logs: data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten_decimal2 -2024-11-26,13:30:37 | INFO | lr: 1e-06 -2024-11-26,13:30:37 | INFO | model: ViT-L-14-336 -2024-11-26,13:30:37 | INFO | name: 2024_11_26-13_30_24-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp -2024-11-26,13:30:37 | INFO | no_set_device_rank: False -2024-11-26,13:30:37 | INFO | norm_gradient_clip: None -2024-11-26,13:30:37 | INFO | precision: amp -2024-11-26,13:30:37 | INFO | pretrained: data/openclip-vit-14-336/openclip_model.pt -2024-11-26,13:30:37 | INFO | pretrained_image: False -2024-11-26,13:30:37 | INFO | rank: 0 -2024-11-26,13:30:37 | INFO | report_to: wandb -2024-11-26,13:30:37 | INFO | resume: None -2024-11-26,13:30:37 | INFO | save_frequency: 1 -2024-11-26,13:30:37 | INFO | save_most_recent: False -2024-11-26,13:30:37 | INFO | seed: 0 -2024-11-26,13:30:37 | INFO | skip_scheduler: False -2024-11-26,13:30:37 | INFO | tensorboard: False -2024-11-26,13:30:37 | INFO | tensorboard_path: -2024-11-26,13:30:37 | INFO | torchscript: False -2024-11-26,13:30:37 | INFO | trace: False -2024-11-26,13:30:37 | INFO | train_data: csv_data/plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten_decimal2.csv -2024-11-26,13:30:37 | INFO | train_num_samples: None -2024-11-26,13:30:37 | INFO | use_bn_sync: False -2024-11-26,13:30:37 | INFO | val_data: None -2024-11-26,13:30:37 | INFO | val_frequency: 1 -2024-11-26,13:30:37 | INFO | val_num_samples: None -2024-11-26,13:30:37 | INFO | wandb: True -2024-11-26,13:30:37 | INFO | wandb_notes: -2024-11-26,13:30:37 | INFO | wandb_project: neg-clip-plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten_decimal2 -2024-11-26,13:30:37 | INFO | warmup: 0 -2024-11-26,13:30:37 | INFO | wd: 0.1 -2024-11-26,13:30:37 | INFO | workers: 4 -2024-11-26,13:30:37 | INFO | world_size: 8 -2024-11-26,13:30:37 | INFO | zeroshot_frequency: 2 -2024-11-26,13:32:09 | INFO | Init a wandb project! -2024-11-26,13:32:15 | INFO | Start epoch 0 -2024-11-26,13:32:23 | INFO | Train Epoch: 0 [ 512/18327966 (0%)] Loss: 5.0606 (5.061) Data (t): 3.713 Batch (t): 7.895, 64.8516/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,13:33:55 | INFO | Train Epoch: 0 [ 51712/18327966 (0%)] Loss: 2.3209 (3.691) Data (t): 0.001 Batch (t): 0.918, 560.846/s LR: 0.000001 Logit Scale: 99.998 - V4 -2024-11-26,13:35:26 | INFO | Train Epoch: 0 [ 102912/18327966 (1%)] Loss: 1.7890 (3.057) Data (t): 0.001 Batch (t): 0.911, 561.170/s LR: 0.000001 Logit Scale: 99.998 - V4 -2024-11-26,13:36:58 | INFO | Train Epoch: 0 [ 154112/18327966 (1%)] Loss: 1.6512 (2.705) Data (t): 0.001 Batch (t): 0.921, 563.739/s LR: 0.000001 Logit Scale: 99.998 - V4 -2024-11-26,13:38:35 | INFO | Train Epoch: 0 [ 205312/18327966 (1%)] Loss: 1.6220 (2.489) Data (t): 0.001 Batch (t): 0.966, 562.120/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,13:40:06 | INFO | Train Epoch: 0 [ 256512/18327966 (1%)] Loss: 1.4997 (2.324) Data (t): 0.001 Batch (t): 0.909, 562.164/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,13:41:37 | INFO | Train Epoch: 0 [ 307712/18327966 (2%)] Loss: 1.4990 (2.206) Data (t): 0.001 Batch (t): 0.910, 561.092/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,13:43:08 | INFO | Train Epoch: 0 [ 358912/18327966 (2%)] Loss: 1.2419 (2.086) Data (t): 0.001 Batch (t): 0.912, 563.352/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,13:44:40 | INFO | Train Epoch: 0 [ 410112/18327966 (2%)] Loss: 1.3806 (2.007) Data (t): 0.001 Batch (t): 0.921, 564.760/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,13:46:18 | INFO | Train Epoch: 0 [ 461312/18327966 (3%)] Loss: 1.4002 (1.947) Data (t): 0.001 Batch (t): 0.980, 562.199/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,13:47:49 | INFO | Train Epoch: 0 [ 512512/18327966 (3%)] Loss: 1.1900 (1.878) Data (t): 0.001 Batch (t): 0.910, 563.951/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,13:49:20 | INFO | Train Epoch: 0 [ 563712/18327966 (3%)] Loss: 1.1985 (1.821) Data (t): 0.001 Batch (t): 0.908, 565.489/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,13:50:51 | INFO | Train Epoch: 0 [ 614912/18327966 (3%)] Loss: 1.2325 (1.776) Data (t): 0.001 Batch (t): 0.909, 562.813/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,13:52:22 | INFO | Train Epoch: 0 [ 666112/18327966 (4%)] Loss: 1.4289 (1.751) Data (t): 0.001 Batch (t): 0.910, 561.592/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,13:54:02 | INFO | Train Epoch: 0 [ 717312/18327966 (4%)] Loss: 1.0938 (1.707) Data (t): 0.001 Batch (t): 1.003, 562.504/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,13:55:33 | INFO | Train Epoch: 0 [ 768512/18327966 (4%)] Loss: 1.1340 (1.671) Data (t): 0.001 Batch (t): 0.910, 562.370/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,13:57:04 | INFO | Train Epoch: 0 [ 819712/18327966 (4%)] Loss: 1.2022 (1.644) Data (t): 0.001 Batch (t): 0.908, 564.663/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,13:58:35 | INFO | Train Epoch: 0 [ 870912/18327966 (5%)] Loss: 1.0730 (1.612) Data (t): 0.001 Batch (t): 0.909, 563.479/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:00:06 | INFO | Train Epoch: 0 [ 922112/18327966 (5%)] Loss: 1.1575 (1.588) Data (t): 0.001 Batch (t): 0.908, 566.921/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:01:46 | INFO | Train Epoch: 0 [ 973312/18327966 (5%)] Loss: 1.3122 (1.574) Data (t): 0.001 Batch (t): 1.002, 562.164/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:03:17 | INFO | Train Epoch: 0 [ 1024512/18327966 (6%)] Loss: 1.1269 (1.553) Data (t): 0.001 Batch (t): 0.909, 564.076/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:04:48 | INFO | Train Epoch: 0 [ 1075712/18327966 (6%)] Loss: 1.1988 (1.537) Data (t): 0.001 Batch (t): 0.910, 563.393/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:06:18 | INFO | Train Epoch: 0 [ 1126912/18327966 (6%)] Loss: 1.1416 (1.520) Data (t): 0.001 Batch (t): 0.909, 562.623/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:07:49 | INFO | Train Epoch: 0 [ 1178112/18327966 (6%)] Loss: 1.0891 (1.502) Data (t): 0.001 Batch (t): 0.910, 562.499/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:09:28 | INFO | Train Epoch: 0 [ 1229312/18327966 (7%)] Loss: 1.1398 (1.487) Data (t): 0.001 Batch (t): 0.981, 257.132/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:11:01 | INFO | Train Epoch: 0 [ 1280512/18327966 (7%)] Loss: 1.1215 (1.473) Data (t): 0.001 Batch (t): 0.930, 564.389/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:12:32 | INFO | Train Epoch: 0 [ 1331712/18327966 (7%)] Loss: 1.1318 (1.461) Data (t): 0.001 Batch (t): 0.909, 562.240/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:14:02 | INFO | Train Epoch: 0 [ 1382912/18327966 (8%)] Loss: 1.0786 (1.447) Data (t): 0.001 Batch (t): 0.909, 565.552/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:15:33 | INFO | Train Epoch: 0 [ 1434112/18327966 (8%)] Loss: 1.2094 (1.439) Data (t): 0.001 Batch (t): 0.910, 563.665/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:17:05 | INFO | Train Epoch: 0 [ 1485312/18327966 (8%)] Loss: 1.2140 (1.431) Data (t): 0.001 Batch (t): 0.920, 566.325/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:18:44 | INFO | Train Epoch: 0 [ 1536512/18327966 (8%)] Loss: 1.0383 (1.419) Data (t): 0.001 Batch (t): 0.990, 563.385/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:20:15 | INFO | Train Epoch: 0 [ 1587712/18327966 (9%)] Loss: 1.1026 (1.409) Data (t): 0.001 Batch (t): 0.909, 562.694/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:21:46 | INFO | Train Epoch: 0 [ 1638912/18327966 (9%)] Loss: 1.0537 (1.398) Data (t): 0.001 Batch (t): 0.909, 565.354/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:23:17 | INFO | Train Epoch: 0 [ 1690112/18327966 (9%)] Loss: 1.1359 (1.390) Data (t): 0.001 Batch (t): 0.909, 561.710/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:24:49 | INFO | Train Epoch: 0 [ 1741312/18327966 (10%)] Loss: 1.1129 (1.382) Data (t): 0.001 Batch (t): 0.919, 566.635/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:26:28 | INFO | Train Epoch: 0 [ 1792512/18327966 (10%)] Loss: 1.1117 (1.375) Data (t): 0.001 Batch (t): 0.991, 564.894/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:27:59 | INFO | Train Epoch: 0 [ 1843712/18327966 (10%)] Loss: 1.2272 (1.371) Data (t): 0.001 Batch (t): 0.909, 561.132/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:29:30 | INFO | Train Epoch: 0 [ 1894912/18327966 (10%)] Loss: 0.93761 (1.359) Data (t): 0.001 Batch (t): 0.909, 561.845/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:31:01 | INFO | Train Epoch: 0 [ 1946112/18327966 (11%)] Loss: 0.96268 (1.349) Data (t): 0.001 Batch (t): 0.909, 563.946/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:32:32 | INFO | Train Epoch: 0 [ 1997312/18327966 (11%)] Loss: 1.0738 (1.342) Data (t): 0.001 Batch (t): 0.907, 567.351/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:34:12 | INFO | Train Epoch: 0 [ 2048512/18327966 (11%)] Loss: 1.1170 (1.337) Data (t): 0.001 Batch (t): 1.002, 569.474/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:35:43 | INFO | Train Epoch: 0 [ 2099712/18327966 (11%)] Loss: 0.98640 (1.329) Data (t): 0.001 Batch (t): 0.908, 562.971/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:37:14 | INFO | Train Epoch: 0 [ 2150912/18327966 (12%)] Loss: 1.0071 (1.321) Data (t): 0.001 Batch (t): 0.910, 563.555/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:38:45 | INFO | Train Epoch: 0 [ 2202112/18327966 (12%)] Loss: 1.1464 (1.317) Data (t): 0.001 Batch (t): 0.908, 565.078/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:40:15 | INFO | Train Epoch: 0 [ 2253312/18327966 (12%)] Loss: 1.0808 (1.312) Data (t): 0.001 Batch (t): 0.909, 561.496/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:41:53 | INFO | Train Epoch: 0 [ 2304512/18327966 (13%)] Loss: 0.95471 (1.304) Data (t): 0.001 Batch (t): 0.971, 564.926/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:43:26 | INFO | Train Epoch: 0 [ 2355712/18327966 (13%)] Loss: 1.0506 (1.299) Data (t): 0.001 Batch (t): 0.930, 559.190/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:44:56 | INFO | Train Epoch: 0 [ 2406912/18327966 (13%)] Loss: 0.91212 (1.291) Data (t): 0.001 Batch (t): 0.909, 563.469/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:46:27 | INFO | Train Epoch: 0 [ 2458112/18327966 (13%)] Loss: 1.0273 (1.285) Data (t): 0.001 Batch (t): 0.910, 559.354/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:47:58 | INFO | Train Epoch: 0 [ 2509312/18327966 (14%)] Loss: 1.1308 (1.282) Data (t): 0.001 Batch (t): 0.910, 562.498/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:49:34 | INFO | Train Epoch: 0 [ 2560512/18327966 (14%)] Loss: 1.1086 (1.279) Data (t): 0.001 Batch (t): 0.951, 564.786/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:51:09 | INFO | Train Epoch: 0 [ 2611712/18327966 (14%)] Loss: 0.89732 (1.271) Data (t): 0.001 Batch (t): 0.958, 564.016/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:52:40 | INFO | Train Epoch: 0 [ 2662912/18327966 (15%)] Loss: 0.85868 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Train Epoch: 0 [ 2970112/18327966 (16%)] Loss: 0.97713 (1.238) Data (t): 0.001 Batch (t): 0.910, 560.647/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:03:25 | INFO | Train Epoch: 0 [ 3021312/18327966 (16%)] Loss: 0.99264 (1.233) Data (t): 0.001 Batch (t): 0.910, 564.380/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:04:56 | INFO | Train Epoch: 0 [ 3072512/18327966 (17%)] Loss: 1.0264 (1.230) Data (t): 0.001 Batch (t): 0.910, 562.631/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:06:36 | INFO | Train Epoch: 0 [ 3123712/18327966 (17%)] Loss: 1.0676 (1.227) Data (t): 0.001 Batch (t): 1.002, 563.290/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:08:07 | INFO | Train Epoch: 0 [ 3174912/18327966 (17%)] Loss: 0.86232 (1.222) Data (t): 0.001 Batch (t): 0.910, 565.872/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:09:38 | INFO | Train Epoch: 0 [ 3226112/18327966 (18%)] Loss: 0.95558 (1.217) Data (t): 0.001 Batch (t): 0.910, 561.245/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:11:09 | INFO | Train Epoch: 0 [ 3277312/18327966 (18%)] Loss: 1.0454 (1.215) Data (t): 0.001 Batch (t): 0.910, 561.837/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:12:40 | INFO | Train Epoch: 0 [ 3328512/18327966 (18%)] Loss: 0.94336 (1.211) Data (t): 0.001 Batch (t): 0.907, 563.189/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:14:18 | INFO | Train Epoch: 0 [ 3379712/18327966 (18%)] Loss: 0.93572 (1.207) Data (t): 0.001 Batch (t): 0.981, 564.176/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:15:51 | INFO | Train Epoch: 0 [ 3430912/18327966 (19%)] Loss: 1.0176 (1.204) Data (t): 0.001 Batch (t): 0.931, 561.659/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:17:22 | INFO | Train Epoch: 0 [ 3482112/18327966 (19%)] Loss: 0.89874 (1.199) Data (t): 0.001 Batch (t): 0.911, 560.031/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:18:53 | INFO | Train Epoch: 0 [ 3533312/18327966 (19%)] Loss: 0.83526 (1.194) Data (t): 0.001 Batch (t): 0.910, 565.574/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:20:24 | INFO | Train Epoch: 0 [ 3584512/18327966 (20%)] Loss: 1.1180 (1.193) Data (t): 0.001 Batch (t): 0.910, 555.910/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:22:01 | INFO | Train Epoch: 0 [ 3635712/18327966 (20%)] Loss: 0.83896 (1.188) Data (t): 0.001 Batch (t): 0.964, 563.365/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:23:35 | INFO | Train Epoch: 0 [ 3686912/18327966 (20%)] Loss: 0.95044 (1.185) Data (t): 0.001 Batch (t): 0.946, 564.144/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:25:06 | INFO | Train Epoch: 0 [ 3738112/18327966 (20%)] Loss: 0.93895 (1.182) Data (t): 0.001 Batch (t): 0.909, 564.482/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:26:37 | INFO | Train Epoch: 0 [ 3789312/18327966 (21%)] Loss: 0.91456 (1.178) Data (t): 0.001 Batch (t): 0.909, 562.835/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:28:08 | INFO | Train 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Scale: 100.000 - V4 -2024-11-26,15:37:24 | INFO | Train Epoch: 0 [ 4147712/18327966 (23%)] Loss: 0.98523 (1.159) Data (t): 0.001 Batch (t): 0.921, 565.445/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:39:03 | INFO | Train Epoch: 0 [ 4198912/18327966 (23%)] Loss: 1.0039 (1.157) Data (t): 0.001 Batch (t): 0.991, 566.419/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:40:34 | INFO | Train Epoch: 0 [ 4250112/18327966 (23%)] Loss: 0.96430 (1.155) Data (t): 0.001 Batch (t): 0.909, 562.149/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:42:05 | INFO | Train Epoch: 0 [ 4301312/18327966 (23%)] Loss: 0.86351 (1.152) Data (t): 0.001 Batch (t): 0.908, 564.789/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:43:36 | INFO | Train Epoch: 0 [ 4352512/18327966 (24%)] Loss: 0.89141 (1.149) Data (t): 0.001 Batch (t): 0.909, 564.212/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:45:07 | INFO | Train Epoch: 0 [ 4403712/18327966 (24%)] Loss: 0.85033 (1.145) Data (t): 0.001 Batch (t): 0.909, 565.568/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:46:45 | INFO | Train Epoch: 0 [ 4454912/18327966 (24%)] Loss: 0.96677 (1.143) Data (t): 0.001 Batch (t): 0.979, 563.376/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:48:18 | INFO | Train Epoch: 0 [ 4506112/18327966 (25%)] Loss: 0.94450 (1.141) Data (t): 0.001 Batch (t): 0.931, 567.628/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:49:49 | INFO | Train Epoch: 0 [ 4557312/18327966 (25%)] Loss: 0.74599 (1.136) Data (t): 0.001 Batch (t): 0.910, 561.890/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:51:20 | INFO | Train Epoch: 0 [ 4608512/18327966 (25%)] Loss: 0.97683 (1.135) Data (t): 0.001 Batch (t): 0.909, 560.055/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:52:50 | INFO | Train Epoch: 0 [ 4659712/18327966 (25%)] Loss: 0.93662 (1.133) Data (t): 0.001 Batch (t): 0.909, 565.280/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:54:28 | INFO | Train Epoch: 0 [ 4710912/18327966 (26%)] Loss: 0.86308 (1.130) Data (t): 0.001 Batch (t): 0.980, 562.091/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:56:02 | INFO | Train Epoch: 0 [ 4762112/18327966 (26%)] Loss: 0.99791 (1.128) Data (t): 0.001 Batch (t): 0.930, 563.962/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:57:32 | INFO | Train Epoch: 0 [ 4813312/18327966 (26%)] Loss: 0.82379 (1.125) Data (t): 0.001 Batch (t): 0.909, 562.296/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:59:03 | INFO | Train Epoch: 0 [ 4864512/18327966 (27%)] Loss: 0.95055 (1.123) Data (t): 0.001 Batch (t): 0.910, 559.650/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:00:34 | INFO | Train Epoch: 0 [ 4915712/18327966 (27%)] Loss: 0.91227 (1.121) Data (t): 0.001 Batch (t): 0.910, 563.540/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:02:10 | INFO | Train Epoch: 0 [ 4966912/18327966 (27%)] Loss: 0.92536 (1.119) Data (t): 0.001 Batch (t): 0.953, 561.375/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:03:45 | INFO | Train Epoch: 0 [ 5018112/18327966 (27%)] Loss: 0.96316 (1.118) Data (t): 0.001 Batch (t): 0.957, 564.147/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:05:16 | INFO | Train Epoch: 0 [ 5069312/18327966 (28%)] Loss: 0.95037 (1.116) Data (t): 0.001 Batch (t): 0.908, 564.055/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:06:47 | INFO | Train Epoch: 0 [ 5120512/18327966 (28%)] Loss: 1.2310 (1.117) Data (t): 0.001 Batch (t): 0.909, 567.623/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:08:18 | INFO | Train Epoch: 0 [ 5171712/18327966 (28%)] Loss: 0.82460 (1.114) Data (t): 0.001 Batch (t): 0.909, 563.598/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:09:52 | INFO | Train Epoch: 0 [ 5222912/18327966 (28%)] Loss: 0.91665 (1.112) Data (t): 0.001 Batch (t): 0.941, 252.349/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:11:29 | INFO | Train Epoch: 0 [ 5274112/18327966 (29%)] Loss: 0.87343 (1.110) Data (t): 0.001 Batch (t): 0.968, 566.808/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:12:59 | INFO | Train Epoch: 0 [ 5325312/18327966 (29%)] Loss: 0.90090 (1.108) Data (t): 0.001 Batch (t): 0.906, 564.032/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:14:30 | INFO | Train Epoch: 0 [ 5376512/18327966 (29%)] Loss: 0.92454 (1.106) Data (t): 0.001 Batch (t): 0.908, 565.796/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:16:01 | INFO | Train Epoch: 0 [ 5427712/18327966 (30%)] Loss: 0.82018 (1.104) Data (t): 0.001 Batch (t): 0.907, 562.940/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:17:33 | INFO | Train Epoch: 0 [ 5478912/18327966 (30%)] Loss: 1.0403 (1.103) Data (t): 0.001 Batch (t): 0.918, 564.619/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:19:10 | INFO | Train Epoch: 0 [ 5530112/18327966 (30%)] Loss: 0.93398 (1.101) Data (t): 0.001 Batch (t): 0.977, 568.156/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:20:42 | INFO | Train Epoch: 0 [ 5581312/18327966 (30%)] Loss: 0.87568 (1.099) Data (t): 0.001 Batch (t): 0.917, 563.208/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:22:13 | INFO | Train Epoch: 0 [ 5632512/18327966 (31%)] Loss: 1.0351 (1.099) Data (t): 0.001 Batch (t): 0.908, 567.106/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:23:44 | INFO | Train Epoch: 0 [ 5683712/18327966 (31%)] Loss: 0.83552 (1.096) Data (t): 0.001 Batch (t): 0.907, 564.831/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:25:14 | INFO | Train Epoch: 0 [ 5734912/18327966 (31%)] Loss: 0.95770 (1.095) Data (t): 0.001 Batch (t): 0.907, 563.217/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:26:52 | INFO | Train Epoch: 0 [ 5786112/18327966 (32%)] Loss: 0.91158 (1.094) Data (t): 0.001 Batch (t): 0.978, 566.603/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:28:25 | INFO | Train Epoch: 0 [ 5837312/18327966 (32%)] Loss: 0.89693 (1.092) Data (t): 0.001 Batch (t): 0.928, 565.046/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:29:56 | INFO | Train Epoch: 0 [ 5888512/18327966 (32%)] Loss: 0.82873 (1.090) Data (t): 0.001 Batch (t): 0.907, 564.221/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:31:26 | INFO | Train Epoch: 0 [ 5939712/18327966 (32%)] Loss: 0.95690 (1.088) Data (t): 0.001 Batch (t): 0.907, 563.022/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:32:57 | INFO | Train Epoch: 0 [ 5990912/18327966 (33%)] Loss: 0.89207 (1.087) Data (t): 0.001 Batch (t): 0.907, 562.030/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:34:33 | INFO | Train Epoch: 0 [ 6042112/18327966 (33%)] Loss: 0.92586 (1.085) Data (t): 0.001 Batch (t): 0.962, 566.264/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:36:08 | INFO | Train Epoch: 0 [ 6093312/18327966 (33%)] Loss: 0.80322 (1.083) Data (t): 0.001 Batch (t): 0.946, 561.567/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:37:38 | INFO | Train Epoch: 0 [ 6144512/18327966 (34%)] Loss: 0.91975 (1.082) Data (t): 0.001 Batch (t): 0.906, 560.956/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:39:09 | INFO | Train Epoch: 0 [ 6195712/18327966 (34%)] Loss: 1.0008 (1.081) Data (t): 0.001 Batch (t): 0.906, 564.066/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:40:40 | INFO | Train Epoch: 0 [ 6246912/18327966 (34%)] Loss: 0.91236 (1.080) Data (t): 0.001 Batch (t): 0.907, 563.457/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:42:15 | INFO | Train Epoch: 0 [ 6298112/18327966 (34%)] Loss: 0.86050 (1.078) Data (t): 0.001 Batch (t): 0.953, 563.380/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:43:50 | INFO | Train Epoch: 0 [ 6349312/18327966 (35%)] Loss: 0.82045 (1.076) Data (t): 0.001 Batch (t): 0.946, 564.893/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:45:21 | INFO | Train Epoch: 0 [ 6400512/18327966 (35%)] Loss: 0.98161 (1.075) Data (t): 0.001 Batch (t): 0.918, 566.407/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:46:52 | INFO | Train Epoch: 0 [ 6451712/18327966 (35%)] Loss: 0.81191 (1.073) Data (t): 0.001 Batch (t): 0.908, 563.641/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:48:23 | INFO | Train Epoch: 0 [ 6502912/18327966 (35%)] Loss: 0.93137 (1.072) Data (t): 0.001 Batch (t): 0.906, 565.124/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:49:55 | INFO | Train Epoch: 0 [ 6554112/18327966 (36%)] Loss: 0.92426 (1.071) Data (t): 0.001 Batch (t): 0.917, 563.819/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:51:32 | INFO | Train Epoch: 0 [ 6605312/18327966 (36%)] Loss: 0.99105 (1.070) Data (t): 0.001 Batch (t): 0.978, 566.890/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:53:04 | INFO | Train Epoch: 0 [ 6656512/18327966 (36%)] Loss: 0.90313 (1.069) Data (t): 0.001 Batch (t): 0.916, 564.923/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:54:35 | INFO | Train Epoch: 0 [ 6707712/18327966 (37%)] Loss: 0.92252 (1.068) Data (t): 0.001 Batch (t): 0.907, 560.211/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:56:05 | INFO | Train Epoch: 0 [ 6758912/18327966 (37%)] Loss: 0.83976 (1.066) Data (t): 0.001 Batch (t): 0.907, 560.779/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:57:36 | INFO | Train Epoch: 0 [ 6810112/18327966 (37%)] Loss: 1.0138 (1.066) Data (t): 0.001 Batch (t): 0.908, 566.256/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:59:14 | INFO | Train Epoch: 0 [ 6861312/18327966 (37%)] Loss: 0.99456 (1.065) Data (t): 0.001 Batch (t): 0.978, 566.367/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:00:47 | INFO | Train Epoch: 0 [ 6912512/18327966 (38%)] Loss: 0.86654 (1.064) Data (t): 0.001 Batch (t): 0.929, 566.228/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:02:18 | INFO | Train Epoch: 0 [ 6963712/18327966 (38%)] Loss: 0.94953 (1.063) Data (t): 0.001 Batch (t): 0.908, 564.125/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:03:49 | INFO | Train Epoch: 0 [ 7014912/18327966 (38%)] Loss: 0.87336 (1.061) Data (t): 0.001 Batch (t): 0.909, 561.086/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:05:19 | INFO | Train Epoch: 0 [ 7066112/18327966 (39%)] Loss: 0.94692 (1.061) Data (t): 0.001 Batch (t): 0.908, 565.709/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:06:56 | INFO | Train Epoch: 0 [ 7117312/18327966 (39%)] Loss: 0.89895 (1.060) Data (t): 0.001 Batch (t): 0.963, 567.546/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:08:30 | INFO | Train Epoch: 0 [ 7168512/18327966 (39%)] Loss: 0.95252 (1.059) Data (t): 0.001 Batch (t): 0.947, 562.027/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:10:01 | INFO | Train Epoch: 0 [ 7219712/18327966 (39%)] Loss: 0.99618 (1.058) Data (t): 0.001 Batch (t): 0.908, 562.866/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:11:32 | INFO | Train Epoch: 0 [ 7270912/18327966 (40%)] Loss: 0.90495 (1.057) Data (t): 0.001 Batch (t): 0.909, 565.102/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:13:03 | INFO | Train Epoch: 0 [ 7322112/18327966 (40%)] Loss: 0.82223 (1.056) Data (t): 0.001 Batch (t): 0.908, 566.709/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:14:39 | INFO | Train Epoch: 0 [ 7373312/18327966 (40%)] Loss: 0.96857 (1.055) Data (t): 0.001 Batch (t): 0.964, 566.862/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:16:13 | INFO | Train Epoch: 0 [ 7424512/18327966 (41%)] Loss: 0.83368 (1.053) Data (t): 0.001 Batch (t): 0.936, 563.746/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:17:45 | INFO | Train Epoch: 0 [ 7475712/18327966 (41%)] Loss: 0.81019 (1.052) Data (t): 0.001 Batch (t): 0.920, 563.752/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:19:16 | INFO | Train Epoch: 0 [ 7526912/18327966 (41%)] Loss: 0.90366 (1.051) Data (t): 0.001 Batch (t): 0.908, 566.834/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:20:46 | INFO | Train Epoch: 0 [ 7578112/18327966 (41%)] Loss: 0.91523 (1.050) Data (t): 0.001 Batch (t): 0.906, 563.723/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:22:20 | INFO | Train Epoch: 0 [ 7629312/18327966 (42%)] Loss: 0.88777 (1.049) Data (t): 0.001 Batch (t): 0.940, 569.142/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:23:56 | INFO | Train Epoch: 0 [ 7680512/18327966 (42%)] Loss: 0.92361 (1.048) Data (t): 0.001 Batch (t): 0.957, 566.210/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:25:28 | INFO | Train Epoch: 0 [ 7731712/18327966 (42%)] Loss: 0.88350 (1.047) Data (t): 0.001 Batch (t): 0.918, 565.654/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:26:58 | INFO | Train Epoch: 0 [ 7782912/18327966 (42%)] Loss: 0.98731 (1.047) Data (t): 0.001 Batch (t): 0.906, 561.937/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:28:29 | INFO | Train Epoch: 0 [ 7834112/18327966 (43%)] Loss: 0.80924 (1.045) Data (t): 0.001 Batch (t): 0.907, 563.973/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:30:01 | INFO | Train Epoch: 0 [ 7885312/18327966 (43%)] Loss: 0.91558 (1.044) Data (t): 0.001 Batch (t): 0.918, 562.744/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:31:38 | INFO | Train Epoch: 0 [ 7936512/18327966 (43%)] Loss: 0.80303 (1.043) Data (t): 0.001 Batch (t): 0.969, 562.948/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:33:11 | INFO | Train Epoch: 0 [ 7987712/18327966 (44%)] Loss: 0.86545 (1.041) Data (t): 0.001 Batch (t): 0.928, 562.940/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:34:41 | INFO | Train Epoch: 0 [ 8038912/18327966 (44%)] Loss: 0.97703 (1.041) Data (t): 0.001 Batch (t): 0.907, 569.984/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:36:12 | INFO | Train Epoch: 0 [ 8090112/18327966 (44%)] Loss: 0.77179 (1.039) Data (t): 0.001 Batch (t): 0.907, 566.231/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:37:43 | INFO | Train Epoch: 0 [ 8141312/18327966 (44%)] Loss: 0.83885 (1.038) Data (t): 0.001 Batch (t): 0.907, 565.235/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:39:21 | INFO | Train Epoch: 0 [ 8192512/18327966 (45%)] Loss: 0.88391 (1.037) Data (t): 0.001 Batch (t): 0.979, 566.251/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:40:53 | INFO | Train Epoch: 0 [ 8243712/18327966 (45%)] Loss: 0.91787 (1.036) Data (t): 0.001 Batch (t): 0.928, 562.283/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:42:24 | INFO | Train Epoch: 0 [ 8294912/18327966 (45%)] Loss: 0.85257 (1.035) Data (t): 0.001 Batch (t): 0.906, 565.992/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:43:55 | INFO | Train Epoch: 0 [ 8346112/18327966 (46%)] Loss: 0.77126 (1.034) Data (t): 0.001 Batch (t): 0.906, 568.926/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:45:25 | INFO | Train Epoch: 0 [ 8397312/18327966 (46%)] Loss: 0.90779 (1.033) Data (t): 0.001 Batch (t): 0.906, 564.510/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:47:02 | INFO | Train Epoch: 0 [ 8448512/18327966 (46%)] Loss: 0.78025 (1.031) Data (t): 0.001 Batch (t): 0.962, 568.433/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:48:35 | INFO | Train Epoch: 0 [ 8499712/18327966 (46%)] Loss: 0.87204 (1.030) Data (t): 0.001 Batch (t): 0.934, 566.521/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:50:07 | INFO | Train Epoch: 0 [ 8550912/18327966 (47%)] Loss: 0.87748 (1.030) Data (t): 0.001 Batch (t): 0.918, 563.102/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:51:37 | INFO | Train Epoch: 0 [ 8602112/18327966 (47%)] Loss: 0.96566 (1.029) Data (t): 0.001 Batch (t): 0.907, 567.828/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:53:08 | INFO | Train Epoch: 0 [ 8653312/18327966 (47%)] Loss: 1.0425 (1.029) Data (t): 0.001 Batch (t): 0.907, 562.780/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:54:42 | INFO | Train Epoch: 0 [ 8704512/18327966 (47%)] Loss: 0.83704 (1.028) Data (t): 0.001 Batch (t): 0.941, 566.024/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:56:17 | INFO | Train Epoch: 0 [ 8755712/18327966 (48%)] Loss: 0.94783 (1.028) Data (t): 0.001 Batch (t): 0.945, 564.369/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:57:48 | INFO | Train Epoch: 0 [ 8806912/18327966 (48%)] Loss: 0.87061 (1.027) Data (t): 0.001 Batch (t): 0.917, 563.586/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:59:19 | INFO | Train Epoch: 0 [ 8858112/18327966 (48%)] Loss: 1.0099 (1.027) Data (t): 0.001 Batch (t): 0.907, 569.526/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:00:50 | INFO | Train Epoch: 0 [ 8909312/18327966 (49%)] Loss: 0.86977 (1.026) Data (t): 0.001 Batch (t): 0.908, 565.485/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:02:22 | INFO | Train Epoch: 0 [ 8960512/18327966 (49%)] Loss: 0.80902 (1.025) Data (t): 0.001 Batch (t): 0.918, 568.335/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:03:58 | INFO | Train Epoch: 0 [ 9011712/18327966 (49%)] Loss: 0.80779 (1.023) Data (t): 0.001 Batch (t): 0.968, 565.525/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:05:31 | INFO | Train Epoch: 0 [ 9062912/18327966 (49%)] Loss: 0.75706 (1.022) Data (t): 0.001 Batch (t): 0.928, 568.590/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:07:02 | INFO | Train Epoch: 0 [ 9114112/18327966 (50%)] Loss: 0.97572 (1.022) Data (t): 0.001 Batch (t): 0.905, 563.184/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:08:32 | INFO | Train Epoch: 0 [ 9165312/18327966 (50%)] Loss: 0.88415 (1.021) Data (t): 0.001 Batch (t): 0.907, 567.513/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:10:03 | INFO | Train Epoch: 0 [ 9216512/18327966 (50%)] Loss: 0.83161 (1.020) Data (t): 0.001 Batch (t): 0.906, 563.033/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:11:41 | INFO | Train Epoch: 0 [ 9267712/18327966 (51%)] Loss: 0.89693 (1.019) Data (t): 0.001 Batch (t): 0.978, 567.111/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:13:14 | INFO | Train Epoch: 0 [ 9318912/18327966 (51%)] Loss: 0.83726 (1.018) Data (t): 0.001 Batch (t): 0.928, 563.626/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:14:44 | INFO | Train Epoch: 0 [ 9370112/18327966 (51%)] Loss: 0.92275 (1.018) Data (t): 0.001 Batch (t): 0.906, 564.498/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:16:15 | INFO | Train Epoch: 0 [ 9421312/18327966 (51%)] Loss: 0.82060 (1.016) Data (t): 0.001 Batch (t): 0.906, 564.357/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:17:45 | INFO | Train Epoch: 0 [ 9472512/18327966 (52%)] Loss: 0.75555 (1.015) Data (t): 0.001 Batch (t): 0.907, 565.134/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:19:22 | INFO | Train Epoch: 0 [ 9523712/18327966 (52%)] Loss: 0.84594 (1.014) Data (t): 0.001 Batch (t): 0.963, 565.252/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:20:55 | INFO | Train Epoch: 0 [ 9574912/18327966 (52%)] Loss: 0.78003 (1.013) Data (t): 0.001 Batch (t): 0.936, 564.162/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:22:27 | INFO | Train Epoch: 0 [ 9626112/18327966 (53%)] Loss: 0.85177 (1.012) Data (t): 0.001 Batch (t): 0.918, 562.450/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:23:58 | INFO | Train Epoch: 0 [ 9677312/18327966 (53%)] Loss: 0.88989 (1.011) Data (t): 0.001 Batch (t): 0.908, 563.842/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:25:29 | INFO | Train Epoch: 0 [ 9728512/18327966 (53%)] Loss: 0.89160 (1.011) Data (t): 0.001 Batch (t): 0.908, 560.827/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:27:05 | INFO | Train Epoch: 0 [ 9779712/18327966 (53%)] Loss: 0.79652 (1.010) Data (t): 0.001 Batch (t): 0.963, 567.614/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:28:38 | INFO | Train Epoch: 0 [ 9830912/18327966 (54%)] Loss: 0.91050 (1.009) Data (t): 0.001 Batch (t): 0.935, 564.063/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:30:10 | INFO | Train Epoch: 0 [ 9882112/18327966 (54%)] Loss: 0.90415 (1.009) Data (t): 0.001 Batch (t): 0.917, 566.928/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:31:41 | INFO | Train Epoch: 0 [ 9933312/18327966 (54%)] Loss: 0.86483 (1.008) Data (t): 0.001 Batch (t): 0.907, 565.982/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:33:12 | INFO | Train Epoch: 0 [ 9984512/18327966 (54%)] Loss: 0.88328 (1.007) Data (t): 0.001 Batch (t): 0.907, 563.020/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:34:46 | INFO | Train Epoch: 0 [10035712/18327966 (55%)] Loss: 0.97928 (1.007) Data (t): 0.001 Batch (t): 0.941, 565.910/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:36:20 | INFO | Train Epoch: 0 [10086912/18327966 (55%)] Loss: 0.91702 (1.007) Data (t): 0.001 Batch (t): 0.946, 564.863/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:37:53 | INFO | Train Epoch: 0 [10138112/18327966 (55%)] Loss: 0.82419 (1.006) Data (t): 0.001 Batch (t): 0.930, 565.824/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:39:24 | INFO | Train Epoch: 0 [10189312/18327966 (56%)] Loss: 0.98268 (1.006) Data (t): 0.001 Batch (t): 0.906, 564.634/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:40:54 | INFO | Train Epoch: 0 [10240512/18327966 (56%)] Loss: 0.82007 (1.005) Data (t): 0.001 Batch (t): 0.907, 565.652/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:42:26 | INFO | Train Epoch: 0 [10291712/18327966 (56%)] Loss: 0.79655 (1.004) Data (t): 0.001 Batch (t): 0.918, 564.657/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:44:03 | INFO | Train Epoch: 0 [10342912/18327966 (56%)] Loss: 0.87657 (1.003) Data (t): 0.001 Batch (t): 0.969, 564.490/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:45:36 | INFO | Train Epoch: 0 [10394112/18327966 (57%)] Loss: 0.86521 (1.002) Data (t): 0.001 Batch (t): 0.929, 566.897/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:47:07 | INFO | Train Epoch: 0 [10445312/18327966 (57%)] Loss: 0.77090 (1.001) Data (t): 0.001 Batch (t): 0.906, 562.876/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:48:37 | INFO | Train Epoch: 0 [10496512/18327966 (57%)] Loss: 0.85666 (1.001) Data (t): 0.001 Batch (t): 0.905, 565.949/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:50:08 | INFO | Train Epoch: 0 [10547712/18327966 (58%)] Loss: 0.87801 (1.000) Data (t): 0.001 Batch (t): 0.906, 562.190/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:51:46 | INFO | Train Epoch: 0 [10598912/18327966 (58%)] Loss: 0.76759 (0.9988) Data (t): 0.001 Batch (t): 0.981, 565.591/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:53:18 | INFO | Train Epoch: 0 [10650112/18327966 (58%)] Loss: 0.94859 (0.9986) Data (t): 0.001 Batch (t): 0.917, 565.370/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:54:50 | INFO | Train Epoch: 0 [10701312/18327966 (58%)] Loss: 0.76411 (0.9975) Data (t): 0.001 Batch (t): 0.920, 564.198/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:56:20 | INFO | Train Epoch: 0 [10752512/18327966 (59%)] Loss: 0.85120 (0.9968) Data (t): 0.001 Batch (t): 0.906, 563.587/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:57:51 | INFO | Train Epoch: 0 [10803712/18327966 (59%)] Loss: 0.89737 (0.9963) Data (t): 0.001 Batch (t): 0.906, 565.027/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:59:27 | INFO | Train Epoch: 0 [10854912/18327966 (59%)] Loss: 0.93325 (0.9960) Data (t): 0.001 Batch (t): 0.963, 562.825/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,19:01:01 | INFO | Train Epoch: 0 [10906112/18327966 (60%)] Loss: 0.88326 (0.9955) Data (t): 0.001 Batch (t): 0.936, 563.840/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,19:02:32 | INFO | Train Epoch: 0 [10957312/18327966 (60%)] Loss: 0.93584 (0.9952) Data (t): 0.001 Batch (t): 0.918, 564.287/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,19:04:03 | INFO | Train Epoch: 0 [11008512/18327966 (60%)] Loss: 0.80989 (0.9944) Data (t): 0.001 Batch (t): 0.906, 564.312/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,19:05:34 | INFO | Train Epoch: 0 [11059712/18327966 (60%)] Loss: 0.91906 (0.9940) Data (t): 0.001 Batch (t): 0.906, 568.539/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,19:07:10 | INFO | Train Epoch: 0 [11110912/18327966 (61%)] Loss: 0.74077 (0.9929) Data (t): 0.001 Batch (t): 0.964, 565.445/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,19:08:42 | INFO | Train Epoch: 0 [11162112/18327966 (61%)] Loss: 0.88847 (0.9924) Data (t): 0.001 Batch (t): 0.923, 563.880/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,19:10:15 | INFO | Train Epoch: 0 [11213312/18327966 (61%)] Loss: 0.81034 (0.9916) Data (t): 0.001 Batch (t): 0.929, 566.969/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,19:11:46 | INFO | Train Epoch: 0 [11264512/18327966 (61%)] Loss: 0.94613 (0.9914) Data (t): 0.001 Batch (t): 0.907, 566.800/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,19:13:17 | INFO | Train Epoch: 0 [11315712/18327966 (62%)] Loss: 0.96509 (0.9912) Data (t): 0.001 Batch (t): 0.906, 563.599/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,19:14:51 | INFO | Train Epoch: 0 [11366912/18327966 (62%)] Loss: 0.87767 (0.9907) Data (t): 0.001 Batch (t): 0.941, 563.601/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,19:16:25 | INFO | Train Epoch: 0 [11418112/18327966 (62%)] Loss: 0.87397 (0.9902) Data (t): 0.001 Batch (t): 0.946, 567.628/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,19:17:58 | INFO | Train Epoch: 0 [11469312/18327966 (63%)] Loss: 0.90046 (0.9898) Data (t): 0.001 Batch (t): 0.930, 565.953/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,19:19:29 | INFO | Train Epoch: 0 [11520512/18327966 (63%)] Loss: 0.87841 (0.9893) Data (t): 0.001 Batch (t): 0.906, 565.476/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,19:20:59 | INFO | Train Epoch: 0 [11571712/18327966 (63%)] Loss: 0.91831 (0.9890) Data (t): 0.001 Batch (t): 0.906, 565.838/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,19:22:31 | INFO | Train Epoch: 0 [11622912/18327966 (63%)] Loss: 0.90342 (0.9886) Data (t): 0.001 Batch (t): 0.916, 247.880/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,19:24:08 | INFO | Train Epoch: 0 [11674112/18327966 (64%)] Loss: 0.83503 (0.9880) Data (t): 0.001 Batch (t): 0.969, 564.268/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,19:25:40 | INFO | Train Epoch: 0 [11725312/18327966 (64%)] Loss: 0.84730 (0.9873) Data (t): 0.001 Batch (t): 0.917, 566.952/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,19:27:11 | INFO | Train Epoch: 0 [11776512/18327966 (64%)] Loss: 0.94711 (0.9872) Data (t): 0.001 Batch (t): 0.917, 565.192/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,19:28:42 | INFO | Train Epoch: 0 [11827712/18327966 (65%)] Loss: 0.81894 (0.9864) Data (t): 0.001 Batch (t): 0.906, 563.679/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,19:30:12 | INFO | Train Epoch: 0 [11878912/18327966 (65%)] Loss: 0.90315 (0.9861) Data (t): 0.001 Batch (t): 0.905, 567.027/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,19:31:49 | INFO | Train Epoch: 0 [11930112/18327966 (65%)] Loss: 0.78034 (0.9852) Data (t): 0.001 Batch (t): 0.963, 564.068/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,19:33:22 | INFO | Train Epoch: 0 [11981312/18327966 (65%)] Loss: 0.82774 (0.9845) Data (t): 0.001 Batch (t): 0.934, 565.620/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,19:34:54 | INFO | Train Epoch: 0 [12032512/18327966 (66%)] Loss: 0.88675 (0.9841) Data (t): 0.001 Batch (t): 0.917, 566.024/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,19:36:24 | INFO | Train Epoch: 0 [12083712/18327966 (66%)] Loss: 0.79534 (0.9833) Data (t): 0.001 Batch (t): 0.906, 566.907/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,19:37:55 | INFO | Train Epoch: 0 [12134912/18327966 (66%)] Loss: 0.83518 (0.9827) Data (t): 0.001 Batch (t): 0.905, 562.084/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,19:39:31 | INFO | Train Epoch: 0 [12186112/18327966 (66%)] Loss: 0.74453 (0.9817) Data (t): 0.001 Batch (t): 0.963, 566.145/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,19:41:04 | INFO | Train Epoch: 0 [12237312/18327966 (67%)] Loss: 0.88226 (0.9813) Data (t): 0.001 Batch (t): 0.923, 564.525/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,19:42:36 | INFO | Train Epoch: 0 [12288512/18327966 (67%)] Loss: 0.86530 (0.9808) Data (t): 0.001 Batch (t): 0.929, 565.424/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,19:44:07 | INFO | Train Epoch: 0 [12339712/18327966 (67%)] Loss: 0.88251 (0.9804) Data (t): 0.001 Batch (t): 0.904, 567.475/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,19:45:37 | INFO | Train Epoch: 0 [12390912/18327966 (68%)] Loss: 0.82725 (0.9798) Data (t): 0.001 Batch (t): 0.905, 565.555/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,19:47:12 | INFO | Train Epoch: 0 [12442112/18327966 (68%)] Loss: 0.97515 (0.9798) Data (t): 0.001 Batch (t): 0.951, 568.602/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,19:48:46 | INFO | Train Epoch: 0 [12493312/18327966 (68%)] Loss: 0.92476 (0.9795) Data (t): 0.001 Batch (t): 0.933, 565.240/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,19:50:19 | INFO | Train Epoch: 0 [12544512/18327966 (68%)] Loss: 0.85014 (0.9790) Data (t): 0.001 Batch (t): 0.929, 566.307/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,19:51:49 | INFO | Train Epoch: 0 [12595712/18327966 (69%)] Loss: 0.93512 (0.9788) Data (t): 0.001 Batch (t): 0.906, 563.723/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,19:53:20 | INFO | Train Epoch: 0 [12646912/18327966 (69%)] Loss: 0.91490 (0.9786) Data (t): 0.001 Batch (t): 0.906, 563.437/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,19:54:52 | INFO | Train Epoch: 0 [12698112/18327966 (69%)] Loss: 0.82825 (0.9780) Data (t): 0.001 Batch (t): 0.918, 566.195/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,19:56:27 | INFO | Train Epoch: 0 [12749312/18327966 (70%)] Loss: 0.83820 (0.9774) Data (t): 0.001 Batch (t): 0.957, 562.399/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,19:57:59 | INFO | Train Epoch: 0 [12800512/18327966 (70%)] Loss: 0.79446 (0.9767) Data (t): 0.001 Batch (t): 0.917, 565.922/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,19:59:31 | INFO | Train Epoch: 0 [12851712/18327966 (70%)] Loss: 0.85479 (0.9762) Data (t): 0.001 Batch (t): 0.916, 568.150/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,20:01:01 | INFO | Train Epoch: 0 [12902912/18327966 (70%)] Loss: 0.81917 (0.9756) Data (t): 0.001 Batch (t): 0.905, 565.377/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,20:02:32 | INFO | Train Epoch: 0 [12954112/18327966 (71%)] Loss: 0.88391 (0.9752) Data (t): 0.001 Batch (t): 0.907, 564.401/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,20:04:08 | INFO | Train Epoch: 0 [13005312/18327966 (71%)] Loss: 0.83649 (0.9747) Data (t): 0.001 Batch (t): 0.964, 564.772/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,20:05:42 | INFO | Train Epoch: 0 [13056512/18327966 (71%)] Loss: 0.87515 (0.9743) Data (t): 0.001 Batch (t): 0.934, 564.686/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,20:07:13 | INFO | Train Epoch: 0 [13107712/18327966 (72%)] Loss: 0.96335 (0.9742) Data (t): 0.001 Batch (t): 0.918, 563.498/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,20:08:44 | INFO | Train Epoch: 0 [13158912/18327966 (72%)] Loss: 0.77443 (0.9735) Data (t): 0.001 Batch (t): 0.905, 566.272/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,20:10:15 | INFO | Train Epoch: 0 [13210112/18327966 (72%)] Loss: 0.86652 (0.9730) Data (t): 0.001 Batch (t): 0.906, 568.517/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,20:11:51 | INFO | Train Epoch: 0 [13261312/18327966 (72%)] Loss: 0.72390 (0.9721) Data (t): 0.001 Batch (t): 0.965, 563.578/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,20:13:23 | INFO | Train Epoch: 0 [13312512/18327966 (73%)] Loss: 0.82570 (0.9715) Data (t): 0.001 Batch (t): 0.923, 560.392/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,20:14:56 | INFO | Train Epoch: 0 [13363712/18327966 (73%)] Loss: 0.81223 (0.9709) Data (t): 0.001 Batch (t): 0.930, 564.994/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,20:16:27 | INFO | Train Epoch: 0 [13414912/18327966 (73%)] Loss: 0.77555 (0.9702) Data (t): 0.001 Batch (t): 0.907, 563.387/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,20:17:58 | INFO | Train Epoch: 0 [13466112/18327966 (73%)] Loss: 0.81997 (0.9696) Data (t): 0.001 Batch (t): 0.905, 563.780/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,20:19:34 | INFO | Train Epoch: 0 [13517312/18327966 (74%)] Loss: 0.80698 (0.9690) Data (t): 0.001 Batch (t): 0.965, 565.883/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,20:21:07 | INFO | Train Epoch: 0 [13568512/18327966 (74%)] Loss: 0.89179 (0.9687) Data (t): 0.001 Batch (t): 0.925, 562.886/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,20:22:40 | INFO | Train Epoch: 0 [13619712/18327966 (74%)] Loss: 0.74983 (0.9679) Data (t): 0.001 Batch (t): 0.932, 563.309/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,20:24:11 | INFO | Train Epoch: 0 [13670912/18327966 (75%)] Loss: 0.82286 (0.9673) Data (t): 0.001 Batch (t): 0.906, 565.536/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,20:25:41 | INFO | Train Epoch: 0 [13722112/18327966 (75%)] Loss: 0.82128 (0.9668) Data (t): 0.001 Batch (t): 0.906, 563.017/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,20:27:15 | INFO | Train Epoch: 0 [13773312/18327966 (75%)] Loss: 0.74600 (0.9660) Data (t): 0.001 Batch (t): 0.942, 566.850/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,20:28:50 | INFO | Train Epoch: 0 [13824512/18327966 (75%)] Loss: 0.90370 (0.9658) Data (t): 0.001 Batch (t): 0.949, 565.356/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,20:30:22 | INFO | Train Epoch: 0 [13875712/18327966 (76%)] Loss: 0.82099 (0.9652) Data (t): 0.001 Batch (t): 0.919, 563.455/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,20:31:54 | INFO | Train Epoch: 0 [13926912/18327966 (76%)] Loss: 0.78955 (0.9646) Data (t): 0.001 Batch (t): 0.917, 567.527/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,20:33:24 | INFO | Train Epoch: 0 [13978112/18327966 (76%)] Loss: 0.85903 (0.9642) Data (t): 0.001 Batch (t): 0.906, 564.685/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,20:34:56 | INFO | Train Epoch: 0 [14029312/18327966 (77%)] Loss: 0.95421 (0.9642) Data (t): 0.001 Batch (t): 0.918, 567.302/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,20:36:32 | INFO | Train Epoch: 0 [14080512/18327966 (77%)] Loss: 0.81696 (0.9636) Data (t): 0.001 Batch (t): 0.959, 563.466/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,20:38:04 | INFO | Train Epoch: 0 [14131712/18327966 (77%)] Loss: 0.90506 (0.9634) Data (t): 0.001 Batch (t): 0.918, 565.147/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,20:39:36 | INFO | Train Epoch: 0 [14182912/18327966 (77%)] Loss: 0.83084 (0.9629) Data (t): 0.001 Batch (t): 0.919, 564.478/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,20:41:06 | INFO | Train Epoch: 0 [14234112/18327966 (78%)] Loss: 0.75865 (0.9622) Data (t): 0.001 Batch (t): 0.906, 564.983/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,20:42:37 | INFO | Train Epoch: 0 [14285312/18327966 (78%)] Loss: 0.84058 (0.9618) Data (t): 0.001 Batch (t): 0.905, 565.240/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,20:44:13 | INFO | Train Epoch: 0 [14336512/18327966 (78%)] Loss: 0.90222 (0.9616) Data (t): 0.001 Batch (t): 0.965, 567.699/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,20:45:46 | INFO | Train Epoch: 0 [14387712/18327966 (79%)] Loss: 0.93458 (0.9615) Data (t): 0.001 Batch (t): 0.923, 565.105/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,20:47:19 | INFO | Train Epoch: 0 [14438912/18327966 (79%)] Loss: 0.78077 (0.9608) Data (t): 0.001 Batch (t): 0.930, 565.626/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,20:48:49 | INFO | Train Epoch: 0 [14490112/18327966 (79%)] Loss: 0.79050 (0.9602) Data (t): 0.001 Batch (t): 0.907, 564.525/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,20:50:20 | INFO | Train Epoch: 0 [14541312/18327966 (79%)] Loss: 0.86150 (0.9599) Data (t): 0.001 Batch (t): 0.906, 564.968/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,20:51:57 | INFO | Train Epoch: 0 [14592512/18327966 (80%)] Loss: 0.94188 (0.9598) Data (t): 0.001 Batch (t): 0.966, 565.247/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,20:53:29 | INFO | Train Epoch: 0 [14643712/18327966 (80%)] Loss: 0.84169 (0.9594) Data (t): 0.001 Batch (t): 0.925, 564.705/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,20:55:02 | INFO | Train Epoch: 0 [14694912/18327966 (80%)] Loss: 0.89999 (0.9592) Data (t): 0.001 Batch (t): 0.929, 568.823/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,20:56:33 | INFO | Train Epoch: 0 [14746112/18327966 (80%)] Loss: 0.85044 (0.9588) Data (t): 0.001 Batch (t): 0.906, 564.849/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,20:58:03 | INFO | Train Epoch: 0 [14797312/18327966 (81%)] Loss: 0.88736 (0.9586) Data (t): 0.001 Batch (t): 0.905, 570.653/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,20:59:40 | INFO | Train Epoch: 0 [14848512/18327966 (81%)] Loss: 0.89352 (0.9583) Data (t): 0.001 Batch (t): 0.965, 569.082/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,21:01:12 | INFO | Train Epoch: 0 [14899712/18327966 (81%)] Loss: 0.73961 (0.9576) Data (t): 0.001 Batch (t): 0.923, 564.999/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,21:02:44 | INFO | Train Epoch: 0 [14950912/18327966 (82%)] Loss: 0.78209 (0.9570) Data (t): 0.001 Batch (t): 0.918, 564.653/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,21:04:15 | INFO | Train Epoch: 0 [15002112/18327966 (82%)] Loss: 0.87996 (0.9567) Data (t): 0.001 Batch (t): 0.918, 562.372/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,21:05:46 | INFO | Train Epoch: 0 [15053312/18327966 (82%)] Loss: 0.91608 (0.9566) Data (t): 0.001 Batch (t): 0.907, 562.812/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,21:07:19 | INFO | Train Epoch: 0 [15104512/18327966 (82%)] Loss: 0.86909 (0.9563) Data (t): 0.001 Batch (t): 0.931, 570.886/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,21:08:55 | INFO | Train Epoch: 0 [15155712/18327966 (83%)] Loss: 0.70905 (0.9555) Data (t): 0.001 Batch (t): 0.959, 565.352/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,21:10:27 | INFO | Train Epoch: 0 [15206912/18327966 (83%)] Loss: 0.70639 (0.9546) Data (t): 0.001 Batch (t): 0.918, 565.835/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,21:11:59 | INFO | Train Epoch: 0 [15258112/18327966 (83%)] Loss: 0.71317 (0.9538) Data (t): 0.001 Batch (t): 0.919, 568.336/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,21:13:30 | INFO | Train Epoch: 0 [15309312/18327966 (84%)] Loss: 0.82567 (0.9534) Data (t): 0.001 Batch (t): 0.907, 562.956/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,21:15:00 | INFO | Train Epoch: 0 [15360512/18327966 (84%)] Loss: 0.84623 (0.9530) Data (t): 0.001 Batch (t): 0.906, 566.182/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,21:16:36 | INFO | Train Epoch: 0 [15411712/18327966 (84%)] Loss: 0.85821 (0.9527) Data (t): 0.001 Batch (t): 0.955, 563.459/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,21:18:08 | INFO | Train Epoch: 0 [15462912/18327966 (84%)] Loss: 0.84944 (0.9524) Data (t): 0.001 Batch (t): 0.923, 564.672/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,21:19:41 | INFO | Train Epoch: 0 [15514112/18327966 (85%)] Loss: 0.83139 (0.9520) Data (t): 0.001 Batch (t): 0.932, 561.737/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,21:21:12 | INFO | Train Epoch: 0 [15565312/18327966 (85%)] Loss: 0.86694 (0.9517) Data (t): 0.001 Batch (t): 0.908, 565.346/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,21:22:43 | INFO | Train Epoch: 0 [15616512/18327966 (85%)] Loss: 0.83064 (0.9513) Data (t): 0.001 Batch (t): 0.908, 561.835/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,21:24:19 | INFO | Train Epoch: 0 [15667712/18327966 (85%)] Loss: 0.90316 (0.9512) Data (t): 0.001 Batch (t): 0.967, 563.973/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,21:25:52 | INFO | Train Epoch: 0 [15718912/18327966 (86%)] Loss: 0.79477 (0.9507) Data (t): 0.001 Batch (t): 0.925, 565.480/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,21:27:24 | INFO | Train Epoch: 0 [15770112/18327966 (86%)] Loss: 0.91266 (0.9505) Data (t): 0.001 Batch (t): 0.920, 560.476/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,21:28:56 | INFO | Train Epoch: 0 [15821312/18327966 (86%)] Loss: 0.91241 (0.9504) Data (t): 0.001 Batch (t): 0.920, 559.305/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,21:30:27 | INFO | Train Epoch: 0 [15872512/18327966 (87%)] Loss: 0.92942 (0.9503) Data (t): 0.001 Batch (t): 0.908, 562.510/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,21:32:03 | INFO | Train Epoch: 0 [15923712/18327966 (87%)] Loss: 0.80259 (0.9499) Data (t): 0.001 Batch (t): 0.968, 565.382/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,21:33:36 | INFO | Train Epoch: 0 [15974912/18327966 (87%)] Loss: 0.83076 (0.9495) Data (t): 0.001 Batch (t): 0.925, 563.972/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,21:35:08 | INFO | Train Epoch: 0 [16026112/18327966 (87%)] Loss: 0.83507 (0.9491) Data (t): 0.001 Batch (t): 0.920, 564.304/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,21:36:40 | INFO | Train Epoch: 0 [16077312/18327966 (88%)] Loss: 0.89686 (0.9490) Data (t): 0.001 Batch (t): 0.920, 557.053/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,21:38:11 | INFO | Train Epoch: 0 [16128512/18327966 (88%)] Loss: 0.83736 (0.9486) Data (t): 0.001 Batch (t): 0.908, 561.993/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,21:39:46 | INFO | Train Epoch: 0 [16179712/18327966 (88%)] Loss: 0.77602 (0.9481) Data (t): 0.001 Batch (t): 0.955, 561.419/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,21:41:20 | INFO | Train Epoch: 0 [16230912/18327966 (89%)] Loss: 0.76604 (0.9475) Data (t): 0.001 Batch (t): 0.937, 564.330/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,21:42:52 | INFO | Train Epoch: 0 [16282112/18327966 (89%)] Loss: 0.90214 (0.9473) Data (t): 0.001 Batch (t): 0.920, 562.951/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,21:44:24 | INFO | Train Epoch: 0 [16333312/18327966 (89%)] Loss: 0.85028 (0.9470) Data (t): 0.001 Batch (t): 0.921, 567.593/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,21:45:55 | INFO | Train Epoch: 0 [16384512/18327966 (89%)] Loss: 0.83722 (0.9467) Data (t): 0.001 Batch (t): 0.909, 564.056/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,21:47:28 | INFO | Train Epoch: 0 [16435712/18327966 (90%)] Loss: 0.90678 (0.9466) Data (t): 0.001 Batch (t): 0.934, 563.372/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,21:49:04 | INFO | Train Epoch: 0 [16486912/18327966 (90%)] Loss: 0.90701 (0.9465) Data (t): 0.001 Batch (t): 0.962, 565.984/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,21:50:35 | INFO | Train Epoch: 0 [16538112/18327966 (90%)] Loss: 0.83062 (0.9461) Data (t): 0.001 Batch (t): 0.908, 565.538/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,21:52:08 | INFO | Train Epoch: 0 [16589312/18327966 (91%)] Loss: 0.89103 (0.9459) Data (t): 0.001 Batch (t): 0.932, 565.711/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,21:53:39 | INFO | Train Epoch: 0 [16640512/18327966 (91%)] Loss: 0.83503 (0.9456) Data (t): 0.001 Batch (t): 0.907, 562.057/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,21:55:10 | INFO | Train Epoch: 0 [16691712/18327966 (91%)] Loss: 0.80247 (0.9451) Data (t): 0.001 Batch (t): 0.906, 563.263/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,21:56:47 | INFO | Train Epoch: 0 [16742912/18327966 (91%)] Loss: 0.84077 (0.9448) Data (t): 0.001 Batch (t): 0.969, 564.758/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,21:58:19 | INFO | Train Epoch: 0 [16794112/18327966 (92%)] Loss: 0.90084 (0.9447) Data (t): 0.001 Batch (t): 0.923, 564.238/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,21:59:51 | INFO | Train Epoch: 0 [16845312/18327966 (92%)] Loss: 0.93187 (0.9447) Data (t): 0.001 Batch (t): 0.920, 559.754/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,22:01:23 | INFO | Train Epoch: 0 [16896512/18327966 (92%)] Loss: 0.83095 (0.9443) Data (t): 0.001 Batch (t): 0.921, 565.172/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,22:02:54 | INFO | Train Epoch: 0 [16947712/18327966 (92%)] Loss: 0.72198 (0.9436) Data (t): 0.001 Batch (t): 0.908, 564.050/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,22:04:31 | INFO | Train Epoch: 0 [16998912/18327966 (93%)] Loss: 0.87408 (0.9434) Data (t): 0.001 Batch (t): 0.969, 565.341/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,22:06:03 | INFO | Train Epoch: 0 [17050112/18327966 (93%)] Loss: 0.73102 (0.9428) Data (t): 0.001 Batch (t): 0.926, 563.462/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,22:07:36 | INFO | Train Epoch: 0 [17101312/18327966 (93%)] Loss: 0.89492 (0.9427) Data (t): 0.001 Batch (t): 0.921, 566.553/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,22:09:07 | INFO | Train Epoch: 0 [17152512/18327966 (94%)] Loss: 0.70766 (0.9420) Data (t): 0.001 Batch (t): 0.919, 565.352/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,22:10:38 | INFO | Train Epoch: 0 [17203712/18327966 (94%)] Loss: 0.86611 (0.9417) Data (t): 0.001 Batch (t): 0.908, 562.497/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,22:12:15 | INFO | Train Epoch: 0 [17254912/18327966 (94%)] Loss: 0.77451 (0.9412) Data (t): 0.001 Batch (t): 0.971, 565.067/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,22:13:48 | INFO | Train Epoch: 0 [17306112/18327966 (94%)] Loss: 0.86719 (0.9410) Data (t): 0.001 Batch (t): 0.926, 564.114/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,22:15:19 | INFO | Train Epoch: 0 [17357312/18327966 (95%)] Loss: 0.78497 (0.9406) Data (t): 0.001 Batch (t): 0.909, 559.209/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,22:16:52 | INFO | Train Epoch: 0 [17408512/18327966 (95%)] Loss: 0.87822 (0.9404) Data (t): 0.001 Batch (t): 0.932, 563.445/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,22:18:23 | INFO | Train Epoch: 0 [17459712/18327966 (95%)] Loss: 0.79109 (0.9399) Data (t): 0.001 Batch (t): 0.908, 566.306/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,22:19:57 | INFO | Train Epoch: 0 [17510912/18327966 (96%)] Loss: 0.94937 (0.9400) Data (t): 0.001 Batch (t): 0.945, 565.189/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,22:21:33 | INFO | Train Epoch: 0 [17562112/18327966 (96%)] Loss: 0.81641 (0.9396) Data (t): 0.001 Batch (t): 0.951, 563.272/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,22:23:03 | INFO | Train Epoch: 0 [17613312/18327966 (96%)] Loss: 0.86002 (0.9394) Data (t): 0.001 Batch (t): 0.909, 558.820/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,22:24:37 | INFO | Train Epoch: 0 [17664512/18327966 (96%)] Loss: 0.95073 (0.9394) Data (t): 0.001 Batch (t): 0.934, 565.955/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,22:26:08 | INFO | Train Epoch: 0 [17715712/18327966 (97%)] Loss: 0.85661 (0.9392) Data (t): 0.001 Batch (t): 0.908, 564.984/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,22:27:41 | INFO | Train Epoch: 0 [17766912/18327966 (97%)] Loss: 0.79451 (0.9388) Data (t): 0.001 Batch (t): 0.931, 567.709/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,22:29:15 | INFO | Train Epoch: 0 [17818112/18327966 (97%)] Loss: 0.78362 (0.9383) Data (t): 0.001 Batch (t): 0.945, 564.008/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,22:30:48 | INFO | Train Epoch: 0 [17869312/18327966 (97%)] Loss: 0.88444 (0.9382) Data (t): 0.001 Batch (t): 0.926, 563.754/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,22:32:20 | INFO | Train Epoch: 0 [17920512/18327966 (98%)] Loss: 0.77901 (0.9377) Data (t): 0.001 Batch (t): 0.920, 561.614/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,22:33:52 | INFO | Train Epoch: 0 [17971712/18327966 (98%)] Loss: 0.80714 (0.9373) Data (t): 0.001 Batch (t): 0.920, 562.285/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,22:35:23 | INFO | Train Epoch: 0 [18022912/18327966 (98%)] Loss: 0.83518 (0.9370) Data (t): 0.001 Batch (t): 0.908, 566.818/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,22:37:00 | INFO | Train Epoch: 0 [18074112/18327966 (99%)] Loss: 0.79824 (0.9367) Data (t): 0.001 Batch (t): 0.970, 563.900/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,22:38:32 | INFO | Train Epoch: 0 [18125312/18327966 (99%)] Loss: 0.79617 (0.9363) Data (t): 0.001 Batch (t): 0.926, 567.351/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,22:40:04 | INFO | Train Epoch: 0 [18176512/18327966 (99%)] Loss: 0.78950 (0.9358) Data (t): 0.001 Batch (t): 0.919, 567.147/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,22:41:36 | INFO | Train Epoch: 0 [18227712/18327966 (99%)] Loss: 0.80606 (0.9355) Data (t): 0.001 Batch (t): 0.920, 562.522/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,22:43:07 | INFO | Train Epoch: 0 [18278912/18327966 (100%)] Loss: 0.75800 (0.9350) Data (t): 0.001 Batch (t): 0.908, 564.433/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,22:44:40 | INFO | Train Epoch: 0 [18327552/18327966 (100%)] Loss: 0.80463 (0.9346) Data (t): 0.002 Batch (t): 0.974, 568.835/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,22:44:47 | INFO | Start epoch 1 -2024-11-26,22:44:51 | INFO | Train Epoch: 1 [ 512/18327966 (0%)] Loss: 0.80797 (0.8080) Data (t): 3.581 Batch (t): 4.515, 113.401/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:46:24 | INFO | Train Epoch: 1 [ 51712/18327966 (0%)] Loss: 0.76484 (0.7864) Data (t): 0.001 Batch (t): 0.928, 565.027/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:47:57 | INFO | Train Epoch: 1 [ 102912/18327966 (1%)] Loss: 0.82569 (0.7995) Data (t): 0.001 Batch (t): 0.930, 168.365/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:49:29 | INFO | Train Epoch: 1 [ 154112/18327966 (1%)] Loss: 0.85619 (0.8137) Data (t): 0.001 Batch (t): 0.922, 554.734/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:51:02 | INFO | Train Epoch: 1 [ 205312/18327966 (1%)] Loss: 0.78652 (0.8082) Data (t): 0.001 Batch (t): 0.922, 562.666/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:52:36 | INFO | Train Epoch: 1 [ 256512/18327966 (1%)] Loss: 0.76992 (0.8019) Data (t): 0.001 Batch (t): 0.945, 563.264/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:54:10 | INFO | Train Epoch: 1 [ 307712/18327966 (2%)] Loss: 0.89155 (0.8147) Data (t): 0.001 Batch (t): 0.934, 564.164/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:55:40 | INFO | Train Epoch: 1 [ 358912/18327966 (2%)] Loss: 0.70738 (0.8013) Data (t): 0.001 Batch (t): 0.908, 562.375/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:57:13 | INFO | Train Epoch: 1 [ 410112/18327966 (2%)] Loss: 0.83892 (0.8054) Data (t): 0.001 Batch (t): 0.931, 559.461/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:58:44 | INFO | Train Epoch: 1 [ 461312/18327966 (3%)] Loss: 0.83177 (0.8081) Data (t): 0.001 Batch (t): 0.909, 564.863/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:00:17 | INFO | Train Epoch: 1 [ 512512/18327966 (3%)] Loss: 0.85328 (0.8122) Data (t): 0.001 Batch (t): 0.931, 562.916/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:01:53 | INFO | Train Epoch: 1 [ 563712/18327966 (3%)] Loss: 0.81315 (0.8123) Data (t): 0.001 Batch (t): 0.957, 563.622/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:03:24 | INFO | Train Epoch: 1 [ 614912/18327966 (3%)] Loss: 0.80196 (0.8115) Data (t): 0.001 Batch (t): 0.909, 559.488/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:04:56 | INFO | Train Epoch: 1 [ 666112/18327966 (4%)] Loss: 0.88167 (0.8165) Data (t): 0.001 Batch (t): 0.921, 560.123/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:06:28 | INFO | Train Epoch: 1 [ 717312/18327966 (4%)] Loss: 0.92405 (0.8237) Data (t): 0.001 Batch (t): 0.920, 563.793/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:08:01 | INFO | Train Epoch: 1 [ 768512/18327966 (4%)] Loss: 0.78259 (0.8211) Data (t): 0.001 Batch (t): 0.929, 567.490/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:09:35 | INFO | Train Epoch: 1 [ 819712/18327966 (4%)] Loss: 0.81071 (0.8205) Data (t): 0.001 Batch (t): 0.940, 564.132/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:11:07 | INFO | Train Epoch: 1 [ 870912/18327966 (5%)] Loss: 0.87628 (0.8236) Data (t): 0.001 Batch (t): 0.923, 567.084/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:12:39 | INFO | Train Epoch: 1 [ 922112/18327966 (5%)] Loss: 0.71094 (0.8177) Data (t): 0.001 Batch (t): 0.919, 566.760/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:14:11 | INFO | Train Epoch: 1 [ 973312/18327966 (5%)] Loss: 0.82176 (0.8179) Data (t): 0.001 Batch (t): 0.919, 564.167/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:15:43 | INFO | Train Epoch: 1 [ 1024512/18327966 (6%)] Loss: 0.81101 (0.8175) Data (t): 0.001 Batch (t): 0.918, 559.548/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:17:18 | INFO | Train Epoch: 1 [ 1075712/18327966 (6%)] Loss: 0.87313 (0.8201) Data (t): 0.001 Batch (t): 0.951, 565.006/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:18:50 | INFO | Train Epoch: 1 [ 1126912/18327966 (6%)] Loss: 0.81126 (0.8197) Data (t): 0.001 Batch (t): 0.924, 566.450/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:20:22 | INFO | Train Epoch: 1 [ 1178112/18327966 (6%)] Loss: 0.76440 (0.8174) Data (t): 0.001 Batch (t): 0.919, 564.536/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:21:54 | INFO | Train Epoch: 1 [ 1229312/18327966 (7%)] Loss: 0.96153 (0.8231) Data (t): 0.001 Batch (t): 0.919, 564.575/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:23:26 | INFO | Train Epoch: 1 [ 1280512/18327966 (7%)] Loss: 0.74136 (0.8200) Data (t): 0.001 Batch (t): 0.919, 561.826/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:25:00 | INFO | Train Epoch: 1 [ 1331712/18327966 (7%)] Loss: 0.85957 (0.8215) Data (t): 0.001 Batch (t): 0.941, 564.743/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:26:33 | INFO | Train Epoch: 1 [ 1382912/18327966 (8%)] Loss: 0.74025 (0.8186) Data (t): 0.001 Batch (t): 0.934, 559.738/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:28:04 | INFO | Train Epoch: 1 [ 1434112/18327966 (8%)] Loss: 0.78390 (0.8174) Data (t): 0.001 Batch (t): 0.906, 564.184/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:29:37 | INFO | Train Epoch: 1 [ 1485312/18327966 (8%)] Loss: 0.74622 (0.8150) Data (t): 0.001 Batch (t): 0.928, 562.975/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:31:08 | INFO | Train Epoch: 1 [ 1536512/18327966 (8%)] Loss: 0.75719 (0.8131) Data (t): 0.001 Batch (t): 0.907, 564.736/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:32:43 | INFO | Train Epoch: 1 [ 1587712/18327966 (9%)] Loss: 0.81323 (0.8131) Data (t): 0.001 Batch (t): 0.951, 561.939/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:34:16 | INFO | Train Epoch: 1 [ 1638912/18327966 (9%)] Loss: 0.73608 (0.8108) Data (t): 0.001 Batch (t): 0.933, 567.806/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:35:47 | INFO | Train Epoch: 1 [ 1690112/18327966 (9%)] Loss: 0.83681 (0.8116) Data (t): 0.001 Batch (t): 0.906, 562.253/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:37:19 | INFO | Train Epoch: 1 [ 1741312/18327966 (10%)] Loss: 0.77296 (0.8105) Data (t): 0.001 Batch (t): 0.929, 565.629/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:38:50 | INFO | Train Epoch: 1 [ 1792512/18327966 (10%)] Loss: 0.80237 (0.8102) Data (t): 0.001 Batch (t): 0.907, 564.837/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:40:23 | INFO | Train Epoch: 1 [ 1843712/18327966 (10%)] Loss: 0.76589 (0.8090) Data (t): 0.001 Batch (t): 0.930, 563.935/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:41:59 | INFO | Train Epoch: 1 [ 1894912/18327966 (10%)] Loss: 0.83756 (0.8098) Data (t): 0.001 Batch (t): 0.956, 565.266/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:43:29 | INFO | Train Epoch: 1 [ 1946112/18327966 (11%)] Loss: 0.91500 (0.8125) Data (t): 0.001 Batch (t): 0.908, 563.690/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:45:01 | INFO | Train Epoch: 1 [ 1997312/18327966 (11%)] Loss: 0.86784 (0.8139) Data (t): 0.001 Batch (t): 0.918, 564.775/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:46:33 | INFO | Train Epoch: 1 [ 2048512/18327966 (11%)] Loss: 0.74096 (0.8121) Data (t): 0.001 Batch (t): 0.916, 565.351/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:48:05 | INFO | Train Epoch: 1 [ 2099712/18327966 (11%)] Loss: 0.71821 (0.8099) Data (t): 0.001 Batch (t): 0.917, 568.570/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:49:40 | INFO | Train Epoch: 1 [ 2150912/18327966 (12%)] Loss: 0.90840 (0.8121) Data (t): 0.001 Batch (t): 0.951, 566.003/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:51:12 | INFO | Train Epoch: 1 [ 2202112/18327966 (12%)] Loss: 0.89482 (0.8140) Data (t): 0.001 Batch (t): 0.922, 567.330/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:52:44 | INFO | Train Epoch: 1 [ 2253312/18327966 (12%)] Loss: 0.88380 (0.8156) Data (t): 0.001 Batch (t): 0.916, 566.915/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:54:15 | INFO | Train Epoch: 1 [ 2304512/18327966 (13%)] Loss: 0.93430 (0.8182) Data (t): 0.001 Batch (t): 0.917, 565.669/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:55:47 | INFO | Train Epoch: 1 [ 2355712/18327966 (13%)] Loss: 0.80459 (0.8179) Data (t): 0.001 Batch (t): 0.919, 562.979/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:57:21 | INFO | Train Epoch: 1 [ 2406912/18327966 (13%)] Loss: 0.75231 (0.8165) Data (t): 0.001 Batch (t): 0.940, 562.737/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:58:54 | INFO | Train Epoch: 1 [ 2458112/18327966 (13%)] Loss: 0.86414 (0.8175) Data (t): 0.001 Batch (t): 0.934, 562.773/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:00:26 | INFO | Train Epoch: 1 [ 2509312/18327966 (14%)] Loss: 0.77538 (0.8166) Data (t): 0.001 Batch (t): 0.918, 563.376/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:01:58 | INFO | Train Epoch: 1 [ 2560512/18327966 (14%)] Loss: 0.93233 (0.8189) Data (t): 0.001 Batch (t): 0.917, 567.557/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:03:29 | INFO | Train Epoch: 1 [ 2611712/18327966 (14%)] Loss: 0.88405 (0.8202) Data (t): 0.001 Batch (t): 0.906, 564.810/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:05:03 | INFO | Train Epoch: 1 [ 2662912/18327966 (15%)] Loss: 0.79565 (0.8197) Data (t): 0.001 Batch (t): 0.950, 567.801/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:06:37 | INFO | Train Epoch: 1 [ 2714112/18327966 (15%)] Loss: 0.87119 (0.8206) Data (t): 0.001 Batch (t): 0.933, 562.860/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:08:07 | INFO | Train Epoch: 1 [ 2765312/18327966 (15%)] Loss: 0.87709 (0.8217) Data (t): 0.001 Batch (t): 0.907, 562.302/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:09:41 | INFO | Train Epoch: 1 [ 2816512/18327966 (15%)] Loss: 0.74184 (0.8202) Data (t): 0.001 Batch (t): 0.937, 565.265/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:11:12 | INFO | Train Epoch: 1 [ 2867712/18327966 (16%)] Loss: 0.83344 (0.8205) Data (t): 0.001 Batch (t): 0.907, 567.330/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:12:47 | INFO | Train Epoch: 1 [ 2918912/18327966 (16%)] Loss: 0.87809 (0.8215) Data (t): 0.001 Batch (t): 0.952, 561.485/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:14:20 | INFO | Train Epoch: 1 [ 2970112/18327966 (16%)] Loss: 0.83461 (0.8217) Data (t): 0.001 Batch (t): 0.934, 563.911/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:15:51 | INFO | Train Epoch: 1 [ 3021312/18327966 (16%)] Loss: 0.74263 (0.8204) Data (t): 0.001 Batch (t): 0.907, 564.346/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:17:23 | INFO | Train Epoch: 1 [ 3072512/18327966 (17%)] Loss: 0.71931 (0.8187) Data (t): 0.001 Batch (t): 0.919, 564.165/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:18:55 | INFO | Train Epoch: 1 [ 3123712/18327966 (17%)] Loss: 0.70134 (0.8168) Data (t): 0.001 Batch (t): 0.918, 564.412/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:20:28 | INFO | Train Epoch: 1 [ 3174912/18327966 (17%)] Loss: 0.70093 (0.8150) Data (t): 0.001 Batch (t): 0.928, 561.445/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:22:02 | INFO | Train Epoch: 1 [ 3226112/18327966 (18%)] Loss: 0.82515 (0.8151) Data (t): 0.001 Batch (t): 0.940, 562.413/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:23:34 | INFO | Train Epoch: 1 [ 3277312/18327966 (18%)] Loss: 0.82807 (0.8153) Data (t): 0.001 Batch (t): 0.923, 562.081/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:25:06 | INFO | Train Epoch: 1 [ 3328512/18327966 (18%)] Loss: 0.78697 (0.8149) Data (t): 0.001 Batch (t): 0.918, 565.382/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:26:38 | INFO | Train Epoch: 1 [ 3379712/18327966 (18%)] Loss: 0.93294 (0.8167) Data (t): 0.001 Batch (t): 0.918, 562.120/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:28:08 | INFO | Train Epoch: 1 [ 3430912/18327966 (19%)] Loss: 0.82841 (0.8168) Data (t): 0.001 Batch (t): 0.908, 565.894/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:29:43 | INFO | Train Epoch: 1 [ 3482112/18327966 (19%)] Loss: 0.82404 (0.8170) Data (t): 0.001 Batch (t): 0.951, 564.965/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:31:17 | INFO | Train Epoch: 1 [ 3533312/18327966 (19%)] Loss: 0.79497 (0.8166) Data (t): 0.001 Batch (t): 0.934, 562.090/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:32:49 | INFO | Train Epoch: 1 [ 3584512/18327966 (20%)] Loss: 0.73734 (0.8155) Data (t): 0.001 Batch (t): 0.919, 558.352/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:34:21 | INFO | Train Epoch: 1 [ 3635712/18327966 (20%)] Loss: 0.71003 (0.8141) Data (t): 0.001 Batch (t): 0.919, 563.292/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:35:52 | INFO | Train Epoch: 1 [ 3686912/18327966 (20%)] Loss: 0.93922 (0.8158) Data (t): 0.001 Batch (t): 0.908, 565.612/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:37:27 | INFO | Train Epoch: 1 [ 3738112/18327966 (20%)] Loss: 0.73044 (0.8146) Data (t): 0.001 Batch (t): 0.952, 560.995/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:39:00 | INFO | Train Epoch: 1 [ 3789312/18327966 (21%)] Loss: 0.79166 (0.8143) Data (t): 0.001 Batch (t): 0.935, 564.125/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:40:32 | INFO | Train Epoch: 1 [ 3840512/18327966 (21%)] Loss: 0.77780 (0.8138) Data (t): 0.001 Batch (t): 0.920, 246.945/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:42:04 | INFO | Train Epoch: 1 [ 3891712/18327966 (21%)] Loss: 0.83419 (0.8141) Data (t): 0.001 Batch (t): 0.919, 562.596/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:43:35 | INFO | Train Epoch: 1 [ 3942912/18327966 (22%)] Loss: 0.73913 (0.8131) Data (t): 0.001 Batch (t): 0.908, 563.936/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:45:10 | INFO | Train Epoch: 1 [ 3994112/18327966 (22%)] Loss: 0.91509 (0.8144) Data (t): 0.001 Batch (t): 0.952, 564.119/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:46:43 | INFO | Train Epoch: 1 [ 4045312/18327966 (22%)] Loss: 0.74761 (0.8136) Data (t): 0.001 Batch (t): 0.933, 565.090/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:48:14 | INFO | Train Epoch: 1 [ 4096512/18327966 (22%)] Loss: 0.71536 (0.8124) Data (t): 0.001 Batch (t): 0.907, 561.169/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:49:46 | INFO | Train Epoch: 1 [ 4147712/18327966 (23%)] Loss: 0.81019 (0.8124) Data (t): 0.001 Batch (t): 0.919, 563.407/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:51:18 | INFO | Train Epoch: 1 [ 4198912/18327966 (23%)] Loss: 0.81073 (0.8123) Data (t): 0.001 Batch (t): 0.919, 559.761/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:52:52 | INFO | Train Epoch: 1 [ 4250112/18327966 (23%)] Loss: 0.86326 (0.8129) Data (t): 0.001 Batch (t): 0.940, 566.161/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:54:27 | INFO | Train Epoch: 1 [ 4301312/18327966 (23%)] Loss: 0.94114 (0.8144) Data (t): 0.001 Batch (t): 0.945, 564.876/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:55:57 | INFO | Train Epoch: 1 [ 4352512/18327966 (24%)] Loss: 0.73885 (0.8136) Data (t): 0.001 Batch (t): 0.908, 562.550/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:57:29 | INFO | Train Epoch: 1 [ 4403712/18327966 (24%)] Loss: 0.81811 (0.8136) Data (t): 0.001 Batch (t): 0.920, 563.970/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:59:01 | INFO | Train Epoch: 1 [ 4454912/18327966 (24%)] Loss: 0.97083 (0.8154) Data (t): 0.001 Batch (t): 0.918, 569.023/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,01:00:33 | INFO | Train Epoch: 1 [ 4506112/18327966 (25%)] Loss: 0.76063 (0.8148) Data (t): 0.001 Batch (t): 0.918, 560.285/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,01:02:07 | INFO | Train Epoch: 1 [ 4557312/18327966 (25%)] Loss: 0.70375 (0.8136) Data (t): 0.001 Batch (t): 0.941, 566.496/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,01:03:39 | INFO | Train Epoch: 1 [ 4608512/18327966 (25%)] Loss: 0.77269 (0.8131) Data (t): 0.001 Batch (t): 0.923, 565.369/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,01:05:11 | INFO | Train Epoch: 1 [ 4659712/18327966 (25%)] Loss: 0.78233 (0.8128) Data (t): 0.001 Batch (t): 0.919, 562.020/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,01:06:43 | INFO | Train Epoch: 1 [ 4710912/18327966 (26%)] Loss: 0.81093 (0.8128) Data (t): 0.001 Batch (t): 0.918, 563.877/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,01:08:14 | INFO | Train Epoch: 1 [ 4762112/18327966 (26%)] Loss: 0.75608 (0.8121) Data (t): 0.001 Batch (t): 0.908, 563.547/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,01:09:49 | INFO | Train Epoch: 1 [ 4813312/18327966 (26%)] Loss: 0.78938 (0.8119) Data (t): 0.001 Batch (t): 0.953, 564.225/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,01:11:22 | INFO | Train Epoch: 1 [ 4864512/18327966 (27%)] Loss: 0.74688 (0.8112) Data (t): 0.001 Batch (t): 0.934, 564.045/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,01:12:54 | INFO | Train Epoch: 1 [ 4915712/18327966 (27%)] Loss: 0.86601 (0.8118) Data (t): 0.001 Batch (t): 0.920, 563.920/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,01:14:26 | INFO | Train Epoch: 1 [ 4966912/18327966 (27%)] Loss: 0.82525 (0.8119) Data (t): 0.001 Batch (t): 0.919, 565.474/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,01:15:57 | INFO | Train Epoch: 1 [ 5018112/18327966 (27%)] Loss: 0.88736 (0.8127) Data (t): 0.001 Batch (t): 0.908, 564.785/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,01:17:32 | INFO | Train Epoch: 1 [ 5069312/18327966 (28%)] Loss: 0.91873 (0.8138) Data (t): 0.001 Batch (t): 0.952, 561.711/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,01:19:06 | INFO | Train Epoch: 1 [ 5120512/18327966 (28%)] Loss: 0.76415 (0.8133) Data (t): 0.001 Batch (t): 0.935, 560.742/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,01:20:37 | INFO | Train Epoch: 1 [ 5171712/18327966 (28%)] Loss: 0.88449 (0.8140) Data (t): 0.001 Batch (t): 0.907, 565.967/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,01:22:11 | INFO | Train Epoch: 1 [ 5222912/18327966 (28%)] Loss: 0.86291 (0.8144) Data (t): 0.001 Batch (t): 0.939, 565.178/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,01:23:41 | INFO | Train Epoch: 1 [ 5274112/18327966 (29%)] Loss: 0.73243 (0.8137) Data (t): 0.001 Batch (t): 0.907, 564.263/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,01:25:15 | INFO | Train Epoch: 1 [ 5325312/18327966 (29%)] Loss: 0.83814 (0.8139) Data (t): 0.001 Batch (t): 0.939, 564.531/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,01:26:49 | INFO | Train Epoch: 1 [ 5376512/18327966 (29%)] Loss: 0.70155 (0.8128) Data (t): 0.001 Batch (t): 0.945, 566.851/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,01:28:20 | INFO | Train Epoch: 1 [ 5427712/18327966 (30%)] Loss: 0.74628 (0.8122) Data (t): 0.001 Batch (t): 0.907, 567.552/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,01:29:52 | INFO | Train Epoch: 1 [ 5478912/18327966 (30%)] Loss: 0.84717 (0.8125) Data (t): 0.001 Batch (t): 0.917, 565.464/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,01:31:24 | INFO | Train Epoch: 1 [ 5530112/18327966 (30%)] Loss: 0.86373 (0.8130) Data (t): 0.001 Batch (t): 0.918, 565.351/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,01:32:57 | INFO | Train Epoch: 1 [ 5581312/18327966 (30%)] Loss: 0.90377 (0.8138) Data (t): 0.001 Batch (t): 0.930, 566.708/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,01:34:31 | INFO | Train Epoch: 1 [ 5632512/18327966 (31%)] Loss: 0.86038 (0.8142) Data (t): 0.001 Batch (t): 0.944, 568.393/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,01:36:02 | INFO | Train Epoch: 1 [ 5683712/18327966 (31%)] Loss: 0.68210 (0.8131) Data (t): 0.001 Batch (t): 0.907, 564.757/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,01:37:34 | INFO | Train Epoch: 1 [ 5734912/18327966 (31%)] Loss: 0.87605 (0.8136) Data (t): 0.001 Batch (t): 0.919, 565.291/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,01:39:06 | INFO | Train Epoch: 1 [ 5786112/18327966 (32%)] Loss: 0.88194 (0.8142) Data (t): 0.001 Batch (t): 0.919, 569.668/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,01:40:37 | INFO | Train Epoch: 1 [ 5837312/18327966 (32%)] Loss: 0.86965 (0.8147) Data (t): 0.001 Batch (t): 0.918, 563.854/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,01:42:11 | INFO | Train Epoch: 1 [ 5888512/18327966 (32%)] Loss: 1.0705 (0.8169) Data (t): 0.001 Batch (t): 0.939, 568.388/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,01:43:45 | INFO | Train Epoch: 1 [ 5939712/18327966 (32%)] Loss: 0.82667 (0.8170) Data (t): 0.001 Batch (t): 0.933, 564.819/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,01:45:16 | INFO | Train Epoch: 1 [ 5990912/18327966 (33%)] Loss: 0.81769 (0.8170) Data (t): 0.001 Batch (t): 0.917, 567.154/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,01:46:48 | INFO | Train Epoch: 1 [ 6042112/18327966 (33%)] Loss: 0.81790 (0.8170) Data (t): 0.001 Batch (t): 0.919, 562.403/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,01:48:19 | INFO | Train Epoch: 1 [ 6093312/18327966 (33%)] Loss: 0.78019 (0.8167) Data (t): 0.001 Batch (t): 0.905, 566.468/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,01:49:54 | INFO | Train Epoch: 1 [ 6144512/18327966 (34%)] Loss: 0.76832 (0.8163) Data (t): 0.001 Batch (t): 0.951, 563.728/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,01:51:27 | INFO | Train Epoch: 1 [ 6195712/18327966 (34%)] Loss: 0.69572 (0.8153) Data (t): 0.001 Batch (t): 0.934, 565.180/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,01:52:58 | INFO | Train Epoch: 1 [ 6246912/18327966 (34%)] Loss: 0.79496 (0.8151) Data (t): 0.001 Batch (t): 0.908, 564.424/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,01:54:31 | INFO | Train Epoch: 1 [ 6298112/18327966 (34%)] Loss: 0.63316 (0.8137) Data (t): 0.001 Batch (t): 0.933, 563.272/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,01:56:02 | INFO | Train Epoch: 1 [ 6349312/18327966 (35%)] Loss: 0.74967 (0.8132) Data (t): 0.001 Batch (t): 0.908, 564.914/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,01:57:37 | INFO | Train Epoch: 1 [ 6400512/18327966 (35%)] Loss: 0.78904 (0.8130) Data (t): 0.001 Batch (t): 0.952, 565.634/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,01:59:11 | INFO | Train Epoch: 1 [ 6451712/18327966 (35%)] Loss: 0.75315 (0.8125) Data (t): 0.001 Batch (t): 0.935, 567.542/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,02:00:41 | INFO | Train Epoch: 1 [ 6502912/18327966 (35%)] Loss: 0.77680 (0.8122) Data (t): 0.001 Batch (t): 0.907, 563.546/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,02:02:13 | INFO | Train Epoch: 1 [ 6554112/18327966 (36%)] Loss: 0.82661 (0.8123) Data (t): 0.001 Batch (t): 0.919, 565.335/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,02:03:45 | INFO | Train Epoch: 1 [ 6605312/18327966 (36%)] Loss: 0.85636 (0.8127) Data (t): 0.001 Batch (t): 0.918, 565.163/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,02:05:19 | INFO | Train Epoch: 1 [ 6656512/18327966 (36%)] Loss: 0.83461 (0.8128) Data (t): 0.001 Batch (t): 0.940, 566.485/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,02:06:54 | INFO | Train Epoch: 1 [ 6707712/18327966 (37%)] Loss: 0.78251 (0.8126) Data (t): 0.001 Batch (t): 0.946, 565.943/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,02:08:24 | INFO | Train Epoch: 1 [ 6758912/18327966 (37%)] Loss: 0.79591 (0.8125) Data (t): 0.001 Batch (t): 0.906, 565.333/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,02:09:56 | INFO | Train Epoch: 1 [ 6810112/18327966 (37%)] Loss: 0.84482 (0.8127) Data (t): 0.001 Batch (t): 0.918, 562.873/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,02:11:28 | INFO | Train Epoch: 1 [ 6861312/18327966 (37%)] Loss: 0.70911 (0.8120) Data (t): 0.001 Batch (t): 0.917, 567.969/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,02:13:00 | INFO | Train Epoch: 1 [ 6912512/18327966 (38%)] Loss: 0.64192 (0.8107) Data (t): 0.001 Batch (t): 0.927, 567.314/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,02:14:35 | INFO | Train Epoch: 1 [ 6963712/18327966 (38%)] Loss: 0.75697 (0.8103) Data (t): 0.001 Batch (t): 0.943, 565.505/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,02:16:06 | INFO | Train Epoch: 1 [ 7014912/18327966 (38%)] Loss: 0.79610 (0.8102) Data (t): 0.001 Batch (t): 0.916, 565.360/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,02:17:38 | INFO | Train Epoch: 1 [ 7066112/18327966 (39%)] Loss: 0.79517 (0.8101) Data (t): 0.001 Batch (t): 0.918, 563.568/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,02:19:10 | INFO | Train Epoch: 1 [ 7117312/18327966 (39%)] Loss: 0.77811 (0.8099) Data (t): 0.001 Batch (t): 0.916, 564.329/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,02:20:41 | INFO | Train Epoch: 1 [ 7168512/18327966 (39%)] Loss: 0.63854 (0.8087) Data (t): 0.001 Batch (t): 0.917, 566.277/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,02:22:15 | INFO | Train Epoch: 1 [ 7219712/18327966 (39%)] Loss: 0.77025 (0.8084) Data (t): 0.001 Batch (t): 0.939, 564.918/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,02:23:49 | INFO | Train Epoch: 1 [ 7270912/18327966 (40%)] Loss: 0.72422 (0.8078) Data (t): 0.001 Batch (t): 0.934, 563.007/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,02:25:20 | INFO | Train Epoch: 1 [ 7322112/18327966 (40%)] Loss: 0.77021 (0.8075) Data (t): 0.001 Batch (t): 0.916, 565.819/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,02:26:52 | INFO | Train Epoch: 1 [ 7373312/18327966 (40%)] Loss: 0.94462 (0.8085) Data (t): 0.001 Batch (t): 0.918, 565.147/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,02:28:23 | INFO | Train Epoch: 1 [ 7424512/18327966 (41%)] Loss: 0.68475 (0.8076) Data (t): 0.001 Batch (t): 0.906, 569.208/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,02:29:58 | INFO | Train Epoch: 1 [ 7475712/18327966 (41%)] Loss: 0.74106 (0.8072) Data (t): 0.001 Batch (t): 0.950, 566.988/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,02:31:31 | INFO | Train Epoch: 1 [ 7526912/18327966 (41%)] Loss: 0.83231 (0.8074) Data (t): 0.001 Batch (t): 0.933, 564.990/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,02:33:02 | INFO | Train Epoch: 1 [ 7578112/18327966 (41%)] Loss: 0.77094 (0.8071) Data (t): 0.001 Batch (t): 0.906, 566.166/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,02:34:35 | INFO | Train Epoch: 1 [ 7629312/18327966 (42%)] Loss: 0.91423 (0.8078) Data (t): 0.001 Batch (t): 0.931, 566.691/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,02:36:05 | INFO | Train Epoch: 1 [ 7680512/18327966 (42%)] Loss: 0.80805 (0.8078) Data (t): 0.001 Batch (t): 0.905, 563.047/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,02:37:40 | INFO | Train Epoch: 1 [ 7731712/18327966 (42%)] Loss: 0.79370 (0.8077) Data (t): 0.001 Batch (t): 0.950, 251.708/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,02:39:13 | INFO | Train Epoch: 1 [ 7782912/18327966 (42%)] Loss: 0.71301 (0.8071) Data (t): 0.001 Batch (t): 0.933, 562.721/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,02:40:44 | INFO | Train Epoch: 1 [ 7834112/18327966 (43%)] Loss: 0.76547 (0.8068) Data (t): 0.001 Batch (t): 0.906, 563.304/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,02:42:16 | INFO | Train Epoch: 1 [ 7885312/18327966 (43%)] Loss: 0.89994 (0.8074) Data (t): 0.001 Batch (t): 0.918, 564.955/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,02:43:48 | INFO | Train Epoch: 1 [ 7936512/18327966 (43%)] Loss: 0.64508 (0.8064) Data (t): 0.001 Batch (t): 0.917, 567.243/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,02:45:20 | INFO | Train Epoch: 1 [ 7987712/18327966 (44%)] Loss: 0.73830 (0.8060) Data (t): 0.001 Batch (t): 0.926, 567.317/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,02:46:55 | INFO | Train Epoch: 1 [ 8038912/18327966 (44%)] Loss: 0.77850 (0.8058) Data (t): 0.001 Batch (t): 0.945, 565.963/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,02:48:27 | INFO | Train Epoch: 1 [ 8090112/18327966 (44%)] Loss: 0.78184 (0.8056) Data (t): 0.001 Batch (t): 0.919, 562.718/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,02:49:58 | INFO | Train Epoch: 1 [ 8141312/18327966 (44%)] Loss: 0.75921 (0.8054) Data (t): 0.001 Batch (t): 0.919, 561.094/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,02:51:30 | INFO | Train Epoch: 1 [ 8192512/18327966 (45%)] Loss: 0.74279 (0.8050) Data (t): 0.001 Batch (t): 0.919, 567.204/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,02:53:02 | INFO | Train Epoch: 1 [ 8243712/18327966 (45%)] Loss: 0.81275 (0.8050) Data (t): 0.001 Batch (t): 0.918, 565.447/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,02:54:36 | INFO | Train Epoch: 1 [ 8294912/18327966 (45%)] Loss: 0.84184 (0.8052) Data (t): 0.001 Batch (t): 0.941, 565.186/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,02:56:10 | INFO | Train Epoch: 1 [ 8346112/18327966 (46%)] Loss: 0.75310 (0.8049) Data (t): 0.001 Batch (t): 0.936, 564.570/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,02:57:42 | INFO | Train Epoch: 1 [ 8397312/18327966 (46%)] Loss: 0.84996 (0.8052) Data (t): 0.001 Batch (t): 0.919, 564.779/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,02:59:13 | INFO | Train Epoch: 1 [ 8448512/18327966 (46%)] Loss: 0.78497 (0.8051) Data (t): 0.001 Batch (t): 0.917, 565.471/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,03:00:45 | INFO | Train Epoch: 1 [ 8499712/18327966 (46%)] Loss: 0.82269 (0.8052) Data (t): 0.001 Batch (t): 0.919, 565.173/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,03:02:19 | INFO | Train Epoch: 1 [ 8550912/18327966 (47%)] Loss: 0.79388 (0.8051) Data (t): 0.001 Batch (t): 0.941, 559.557/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,03:03:53 | INFO | Train Epoch: 1 [ 8602112/18327966 (47%)] Loss: 0.87868 (0.8055) Data (t): 0.001 Batch (t): 0.935, 562.782/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,03:05:25 | INFO | Train Epoch: 1 [ 8653312/18327966 (47%)] Loss: 0.78576 (0.8054) Data (t): 0.001 Batch (t): 0.920, 564.951/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,03:06:57 | INFO | Train Epoch: 1 [ 8704512/18327966 (47%)] Loss: 0.72024 (0.8049) Data (t): 0.001 Batch (t): 0.918, 563.536/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,03:08:27 | INFO | Train Epoch: 1 [ 8755712/18327966 (48%)] Loss: 0.71532 (0.8044) Data (t): 0.001 Batch (t): 0.907, 564.215/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,03:10:01 | INFO | Train Epoch: 1 [ 8806912/18327966 (48%)] Loss: 0.84379 (0.8046) Data (t): 0.001 Batch (t): 0.941, 250.100/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,03:11:36 | INFO | Train Epoch: 1 [ 8858112/18327966 (48%)] Loss: 0.85658 (0.8049) Data (t): 0.001 Batch (t): 0.946, 566.282/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,03:13:07 | INFO | Train Epoch: 1 [ 8909312/18327966 (49%)] Loss: 0.68441 (0.8042) Data (t): 0.001 Batch (t): 0.907, 565.591/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,03:14:39 | INFO | Train Epoch: 1 [ 8960512/18327966 (49%)] Loss: 0.78610 (0.8041) Data (t): 0.001 Batch (t): 0.920, 566.043/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,03:16:11 | INFO | Train Epoch: 1 [ 9011712/18327966 (49%)] Loss: 0.72548 (0.8037) Data (t): 0.001 Batch (t): 0.917, 562.765/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,03:17:44 | INFO | Train Epoch: 1 [ 9062912/18327966 (49%)] Loss: 0.75328 (0.8034) Data (t): 0.001 Batch (t): 0.930, 564.658/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,03:19:18 | INFO | Train Epoch: 1 [ 9114112/18327966 (50%)] Loss: 0.87189 (0.8038) Data (t): 0.001 Batch (t): 0.946, 562.017/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,03:20:50 | INFO | Train Epoch: 1 [ 9165312/18327966 (50%)] Loss: 0.96402 (0.8047) Data (t): 0.001 Batch (t): 0.918, 563.170/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,03:22:22 | INFO | Train Epoch: 1 [ 9216512/18327966 (50%)] Loss: 0.82477 (0.8048) Data (t): 0.001 Batch (t): 0.919, 562.427/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,03:23:54 | INFO | Train Epoch: 1 [ 9267712/18327966 (51%)] Loss: 0.71390 (0.8043) Data (t): 0.001 Batch (t): 0.918, 564.393/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,03:25:27 | INFO | Train Epoch: 1 [ 9318912/18327966 (51%)] Loss: 0.76523 (0.8041) Data (t): 0.001 Batch (t): 0.930, 561.888/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,03:27:01 | INFO | Train Epoch: 1 [ 9370112/18327966 (51%)] Loss: 0.84900 (0.8043) Data (t): 0.001 Batch (t): 0.947, 570.274/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,03:28:33 | INFO | Train Epoch: 1 [ 9421312/18327966 (51%)] Loss: 0.78381 (0.8042) Data (t): 0.001 Batch (t): 0.918, 561.893/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,03:30:05 | INFO | Train Epoch: 1 [ 9472512/18327966 (52%)] Loss: 0.81584 (0.8043) Data (t): 0.001 Batch (t): 0.919, 567.749/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,03:31:37 | INFO | Train Epoch: 1 [ 9523712/18327966 (52%)] Loss: 0.92409 (0.8049) Data (t): 0.001 Batch (t): 0.919, 564.122/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,03:33:09 | INFO | Train Epoch: 1 [ 9574912/18327966 (52%)] Loss: 0.67531 (0.8042) Data (t): 0.001 Batch (t): 0.918, 563.263/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,03:34:43 | INFO | Train Epoch: 1 [ 9626112/18327966 (53%)] Loss: 0.79249 (0.8042) Data (t): 0.001 Batch (t): 0.942, 562.912/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,03:36:16 | INFO | Train Epoch: 1 [ 9677312/18327966 (53%)] Loss: 0.70457 (0.8036) Data (t): 0.001 Batch (t): 0.934, 556.378/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,03:37:48 | INFO | Train Epoch: 1 [ 9728512/18327966 (53%)] Loss: 0.95374 (0.8044) Data (t): 0.001 Batch (t): 0.917, 567.453/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,03:39:20 | INFO | Train Epoch: 1 [ 9779712/18327966 (53%)] Loss: 0.77607 (0.8043) Data (t): 0.001 Batch (t): 0.918, 558.685/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,03:40:52 | INFO | Train Epoch: 1 [ 9830912/18327966 (54%)] Loss: 0.80386 (0.8043) Data (t): 0.001 Batch (t): 0.920, 563.740/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,03:42:24 | INFO | Train Epoch: 1 [ 9882112/18327966 (54%)] Loss: 0.71891 (0.8038) Data (t): 0.001 Batch (t): 0.921, 560.562/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,03:44:00 | INFO | Train Epoch: 1 [ 9933312/18327966 (54%)] Loss: 0.88260 (0.8042) Data (t): 0.001 Batch (t): 0.959, 566.839/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,03:45:32 | INFO | Train Epoch: 1 [ 9984512/18327966 (54%)] Loss: 0.80947 (0.8043) Data (t): 0.001 Batch (t): 0.919, 563.780/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,03:47:04 | INFO | Train Epoch: 1 [10035712/18327966 (55%)] Loss: 0.84667 (0.8045) Data (t): 0.001 Batch (t): 0.919, 562.947/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,03:48:34 | INFO | Train Epoch: 1 [10086912/18327966 (55%)] Loss: 0.74725 (0.8042) Data (t): 0.001 Batch (t): 0.907, 564.166/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,03:50:07 | INFO | Train Epoch: 1 [10138112/18327966 (55%)] Loss: 0.69009 (0.8036) Data (t): 0.001 Batch (t): 0.931, 565.827/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,03:51:41 | INFO | Train Epoch: 1 [10189312/18327966 (56%)] Loss: 0.80558 (0.8036) Data (t): 0.001 Batch (t): 0.935, 565.077/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,03:53:13 | INFO | Train Epoch: 1 [10240512/18327966 (56%)] Loss: 0.88978 (0.8041) Data (t): 0.001 Batch (t): 0.918, 565.056/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,03:54:45 | INFO | Train Epoch: 1 [10291712/18327966 (56%)] Loss: 0.93701 (0.8047) Data (t): 0.001 Batch (t): 0.920, 558.773/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,03:56:17 | INFO | Train Epoch: 1 [10342912/18327966 (56%)] Loss: 0.71536 (0.8043) Data (t): 0.001 Batch (t): 0.918, 563.923/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,03:57:49 | INFO | Train Epoch: 1 [10394112/18327966 (57%)] Loss: 0.71534 (0.8038) Data (t): 0.001 Batch (t): 0.930, 565.348/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,03:59:24 | INFO | Train Epoch: 1 [10445312/18327966 (57%)] Loss: 0.75052 (0.8036) Data (t): 0.001 Batch (t): 0.946, 566.221/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,04:00:56 | INFO | Train Epoch: 1 [10496512/18327966 (57%)] Loss: 0.69571 (0.8031) Data (t): 0.001 Batch (t): 0.918, 566.084/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,04:02:28 | INFO | Train Epoch: 1 [10547712/18327966 (58%)] Loss: 0.77129 (0.8029) Data (t): 0.001 Batch (t): 0.919, 560.074/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,04:04:00 | INFO | Train Epoch: 1 [10598912/18327966 (58%)] Loss: 0.78749 (0.8028) Data (t): 0.001 Batch (t): 0.918, 564.768/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,04:05:33 | INFO | Train Epoch: 1 [10650112/18327966 (58%)] Loss: 0.83688 (0.8030) Data (t): 0.001 Batch (t): 0.930, 566.464/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,04:07:07 | INFO | Train Epoch: 1 [10701312/18327966 (58%)] Loss: 0.78974 (0.8029) Data (t): 0.001 Batch (t): 0.947, 567.678/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,04:08:39 | INFO | Train Epoch: 1 [10752512/18327966 (59%)] Loss: 0.78707 (0.8029) Data (t): 0.001 Batch (t): 0.919, 562.845/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,04:10:11 | INFO | Train Epoch: 1 [10803712/18327966 (59%)] Loss: 0.90035 (0.8033) Data (t): 0.001 Batch (t): 0.919, 565.884/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,04:11:43 | INFO | Train Epoch: 1 [10854912/18327966 (59%)] Loss: 0.80480 (0.8033) Data (t): 0.001 Batch (t): 0.918, 566.002/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,04:13:15 | INFO | Train Epoch: 1 [10906112/18327966 (60%)] Loss: 0.71582 (0.8029) Data (t): 0.001 Batch (t): 0.919, 564.853/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,04:14:47 | INFO | Train Epoch: 1 [10957312/18327966 (60%)] Loss: 0.70656 (0.8025) Data (t): 0.001 Batch (t): 0.919, 563.332/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,04:16:23 | INFO | Train Epoch: 1 [11008512/18327966 (60%)] Loss: 0.81592 (0.8025) Data (t): 0.001 Batch (t): 0.958, 563.036/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,04:17:54 | INFO | Train Epoch: 1 [11059712/18327966 (60%)] Loss: 0.69833 (0.8021) Data (t): 0.001 Batch (t): 0.918, 565.917/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,04:19:26 | INFO | Train Epoch: 1 [11110912/18327966 (61%)] Loss: 0.78638 (0.8020) Data (t): 0.001 Batch (t): 0.919, 563.945/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,04:20:58 | INFO | Train Epoch: 1 [11162112/18327966 (61%)] Loss: 0.73200 (0.8017) Data (t): 0.001 Batch (t): 0.918, 565.345/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,04:22:30 | INFO | Train Epoch: 1 [11213312/18327966 (61%)] Loss: 0.71456 (0.8013) Data (t): 0.001 Batch (t): 0.919, 563.759/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,04:24:05 | INFO | Train Epoch: 1 [11264512/18327966 (61%)] Loss: 0.80103 (0.8013) Data (t): 0.001 Batch (t): 0.947, 563.989/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,04:25:38 | INFO | Train Epoch: 1 [11315712/18327966 (62%)] Loss: 0.69151 (0.8008) Data (t): 0.001 Batch (t): 0.932, 561.630/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,04:27:10 | INFO | Train Epoch: 1 [11366912/18327966 (62%)] Loss: 0.70145 (0.8003) Data (t): 0.001 Batch (t): 0.918, 564.091/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,04:28:40 | INFO | Train Epoch: 1 [11418112/18327966 (62%)] Loss: 0.66824 (0.7997) Data (t): 0.001 Batch (t): 0.907, 565.696/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,04:30:13 | INFO | Train Epoch: 1 [11469312/18327966 (63%)] Loss: 0.67444 (0.7992) Data (t): 0.001 Batch (t): 0.931, 565.250/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,04:31:48 | INFO | Train Epoch: 1 [11520512/18327966 (63%)] Loss: 0.81500 (0.7992) Data (t): 0.001 Batch (t): 0.948, 564.267/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,04:33:20 | INFO | Train Epoch: 1 [11571712/18327966 (63%)] Loss: 0.88057 (0.7996) Data (t): 0.001 Batch (t): 0.919, 563.753/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,04:34:52 | INFO | Train Epoch: 1 [11622912/18327966 (63%)] Loss: 0.74106 (0.7993) Data (t): 0.001 Batch (t): 0.920, 563.732/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,04:36:24 | INFO | Train Epoch: 1 [11674112/18327966 (64%)] Loss: 0.76647 (0.7992) Data (t): 0.001 Batch (t): 0.919, 563.718/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,04:37:57 | INFO | Train Epoch: 1 [11725312/18327966 (64%)] Loss: 0.78293 (0.7991) Data (t): 0.001 Batch (t): 0.930, 561.952/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,04:39:30 | INFO | Train Epoch: 1 [11776512/18327966 (64%)] Loss: 0.82576 (0.7992) Data (t): 0.001 Batch (t): 0.935, 565.540/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,04:41:02 | INFO | Train Epoch: 1 [11827712/18327966 (65%)] Loss: 0.79248 (0.7992) Data (t): 0.001 Batch (t): 0.919, 562.702/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,04:42:34 | INFO | Train Epoch: 1 [11878912/18327966 (65%)] Loss: 0.75402 (0.7990) Data (t): 0.001 Batch (t): 0.919, 567.433/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,04:44:06 | INFO | Train Epoch: 1 [11930112/18327966 (65%)] Loss: 0.80916 (0.7991) Data (t): 0.001 Batch (t): 0.918, 562.012/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,04:45:39 | INFO | Train Epoch: 1 [11981312/18327966 (65%)] Loss: 0.76650 (0.7989) Data (t): 0.001 Batch (t): 0.931, 564.052/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,04:47:10 | INFO | Train Epoch: 1 [12032512/18327966 (66%)] Loss: 0.74328 (0.7987) Data (t): 0.001 Batch (t): 0.907, 566.224/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,04:48:46 | INFO | Train Epoch: 1 [12083712/18327966 (66%)] Loss: 0.74393 (0.7985) Data (t): 0.001 Batch (t): 0.958, 570.480/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,04:50:17 | INFO | Train Epoch: 1 [12134912/18327966 (66%)] Loss: 0.76213 (0.7983) Data (t): 0.001 Batch (t): 0.918, 563.880/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,04:51:49 | INFO | Train Epoch: 1 [12186112/18327966 (66%)] Loss: 0.75625 (0.7981) Data (t): 0.001 Batch (t): 0.918, 565.756/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,04:53:21 | INFO | Train Epoch: 1 [12237312/18327966 (67%)] Loss: 0.70646 (0.7978) Data (t): 0.001 Batch (t): 0.918, 565.388/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,04:54:53 | INFO | Train Epoch: 1 [12288512/18327966 (67%)] Loss: 0.68835 (0.7973) Data (t): 0.001 Batch (t): 0.919, 565.326/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,04:56:26 | INFO | Train Epoch: 1 [12339712/18327966 (67%)] Loss: 0.80974 (0.7974) Data (t): 0.001 Batch (t): 0.935, 562.891/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,04:57:59 | INFO | Train Epoch: 1 [12390912/18327966 (68%)] Loss: 0.84136 (0.7975) Data (t): 0.001 Batch (t): 0.931, 568.824/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,04:59:31 | INFO | Train Epoch: 1 [12442112/18327966 (68%)] Loss: 0.86294 (0.7978) Data (t): 0.001 Batch (t): 0.919, 566.374/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,05:01:03 | INFO | Train Epoch: 1 [12493312/18327966 (68%)] Loss: 0.74881 (0.7976) Data (t): 0.001 Batch (t): 0.918, 563.752/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,05:02:35 | INFO | Train Epoch: 1 [12544512/18327966 (68%)] Loss: 0.78185 (0.7975) Data (t): 0.001 Batch (t): 0.919, 565.525/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,05:04:10 | INFO | Train Epoch: 1 [12595712/18327966 (69%)] Loss: 0.78345 (0.7975) Data (t): 0.001 Batch (t): 0.947, 564.372/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,05:05:42 | INFO | Train Epoch: 1 [12646912/18327966 (69%)] Loss: 0.88801 (0.7978) Data (t): 0.001 Batch (t): 0.918, 565.285/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,05:07:15 | INFO | Train Epoch: 1 [12698112/18327966 (69%)] Loss: 0.84257 (0.7980) Data (t): 0.001 Batch (t): 0.930, 243.745/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,05:08:45 | INFO | Train Epoch: 1 [12749312/18327966 (70%)] Loss: 0.89681 (0.7984) Data (t): 0.001 Batch (t): 0.907, 565.153/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,05:10:18 | INFO | Train Epoch: 1 [12800512/18327966 (70%)] Loss: 0.89518 (0.7988) Data (t): 0.001 Batch (t): 0.931, 564.618/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,05:11:52 | INFO | Train Epoch: 1 [12851712/18327966 (70%)] Loss: 0.74758 (0.7986) Data (t): 0.001 Batch (t): 0.935, 566.058/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,05:13:24 | INFO | Train Epoch: 1 [12902912/18327966 (70%)] Loss: 0.67833 (0.7981) Data (t): 0.001 Batch (t): 0.919, 566.416/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,05:14:56 | INFO | Train Epoch: 1 [12954112/18327966 (71%)] Loss: 0.76322 (0.7980) Data (t): 0.001 Batch (t): 0.919, 567.064/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,05:16:28 | INFO | Train Epoch: 1 [13005312/18327966 (71%)] Loss: 0.63144 (0.7973) Data (t): 0.001 Batch (t): 0.919, 565.515/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,05:18:01 | INFO | Train Epoch: 1 [13056512/18327966 (71%)] Loss: 0.85134 (0.7975) Data (t): 0.001 Batch (t): 0.930, 566.036/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,05:19:33 | INFO | Train Epoch: 1 [13107712/18327966 (72%)] Loss: 0.82977 (0.7977) Data (t): 0.001 Batch (t): 0.924, 563.985/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,05:21:07 | INFO | Train Epoch: 1 [13158912/18327966 (72%)] Loss: 0.78669 (0.7976) Data (t): 0.001 Batch (t): 0.941, 565.983/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,05:22:39 | INFO | Train Epoch: 1 [13210112/18327966 (72%)] Loss: 0.84008 (0.7978) Data (t): 0.001 Batch (t): 0.919, 566.351/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,05:24:11 | INFO | Train Epoch: 1 [13261312/18327966 (72%)] Loss: 0.75592 (0.7976) Data (t): 0.001 Batch (t): 0.919, 565.666/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,05:25:44 | INFO | Train Epoch: 1 [13312512/18327966 (73%)] Loss: 0.71848 (0.7973) Data (t): 0.001 Batch (t): 0.931, 566.218/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,05:27:15 | INFO | Train Epoch: 1 [13363712/18327966 (73%)] Loss: 0.79061 (0.7973) Data (t): 0.001 Batch (t): 0.906, 564.225/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,05:28:49 | INFO | Train Epoch: 1 [13414912/18327966 (73%)] Loss: 0.85357 (0.7975) Data (t): 0.001 Batch (t): 0.947, 564.493/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,05:30:22 | INFO | Train Epoch: 1 [13466112/18327966 (73%)] Loss: 0.76802 (0.7974) Data (t): 0.001 Batch (t): 0.931, 566.120/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,05:31:54 | INFO | Train Epoch: 1 [13517312/18327966 (74%)] Loss: 0.89489 (0.7978) Data (t): 0.001 Batch (t): 0.918, 565.196/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,05:33:26 | INFO | Train Epoch: 1 [13568512/18327966 (74%)] Loss: 0.76666 (0.7977) Data (t): 0.001 Batch (t): 0.917, 564.910/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,05:34:58 | INFO | Train Epoch: 1 [13619712/18327966 (74%)] Loss: 0.63370 (0.7970) Data (t): 0.001 Batch (t): 0.917, 566.292/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,05:36:33 | INFO | Train Epoch: 1 [13670912/18327966 (75%)] Loss: 0.77405 (0.7970) Data (t): 0.001 Batch (t): 0.949, 564.015/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,05:38:06 | INFO | Train Epoch: 1 [13722112/18327966 (75%)] Loss: 0.82580 (0.7971) Data (t): 0.001 Batch (t): 0.933, 563.238/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,05:39:38 | INFO | Train Epoch: 1 [13773312/18327966 (75%)] Loss: 0.70716 (0.7967) Data (t): 0.001 Batch (t): 0.917, 564.200/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,05:41:09 | INFO | Train Epoch: 1 [13824512/18327966 (75%)] Loss: 0.64974 (0.7962) Data (t): 0.001 Batch (t): 0.919, 564.960/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,05:42:41 | INFO | Train Epoch: 1 [13875712/18327966 (76%)] Loss: 0.89455 (0.7965) Data (t): 0.001 Batch (t): 0.917, 567.431/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,05:44:16 | INFO | Train Epoch: 1 [13926912/18327966 (76%)] Loss: 0.80179 (0.7966) Data (t): 0.001 Batch (t): 0.947, 564.884/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,05:45:48 | INFO | Train Epoch: 1 [13978112/18327966 (76%)] Loss: 0.88606 (0.7969) Data (t): 0.001 Batch (t): 0.917, 567.484/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,05:47:19 | INFO | Train Epoch: 1 [14029312/18327966 (77%)] Loss: 0.75670 (0.7967) Data (t): 0.001 Batch (t): 0.918, 565.803/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,05:48:51 | INFO | Train Epoch: 1 [14080512/18327966 (77%)] Loss: 0.63151 (0.7962) Data (t): 0.001 Batch (t): 0.917, 566.501/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,05:50:24 | INFO | Train Epoch: 1 [14131712/18327966 (77%)] Loss: 0.85069 (0.7963) Data (t): 0.001 Batch (t): 0.929, 560.941/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,05:51:57 | INFO | Train Epoch: 1 [14182912/18327966 (77%)] Loss: 0.81100 (0.7964) Data (t): 0.001 Batch (t): 0.934, 565.556/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,05:53:30 | INFO | Train Epoch: 1 [14234112/18327966 (78%)] Loss: 0.67103 (0.7960) Data (t): 0.001 Batch (t): 0.928, 564.425/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,05:55:02 | INFO | Train Epoch: 1 [14285312/18327966 (78%)] Loss: 0.81341 (0.7960) Data (t): 0.001 Batch (t): 0.918, 563.863/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,05:56:34 | INFO | Train Epoch: 1 [14336512/18327966 (78%)] Loss: 0.87646 (0.7963) Data (t): 0.001 Batch (t): 0.917, 565.381/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,05:58:07 | INFO | Train Epoch: 1 [14387712/18327966 (79%)] Loss: 0.78263 (0.7963) Data (t): 0.001 Batch (t): 0.929, 566.652/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,05:59:37 | INFO | Train Epoch: 1 [14438912/18327966 (79%)] Loss: 0.84584 (0.7964) Data (t): 0.001 Batch (t): 0.905, 565.432/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,06:01:12 | INFO | Train Epoch: 1 [14490112/18327966 (79%)] Loss: 0.72146 (0.7962) Data (t): 0.001 Batch (t): 0.947, 565.536/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,06:02:45 | INFO | Train Epoch: 1 [14541312/18327966 (79%)] Loss: 0.77806 (0.7961) Data (t): 0.001 Batch (t): 0.930, 565.414/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,06:04:17 | INFO | Train Epoch: 1 [14592512/18327966 (80%)] Loss: 0.74327 (0.7959) Data (t): 0.001 Batch (t): 0.918, 566.057/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,06:05:50 | INFO | Train Epoch: 1 [14643712/18327966 (80%)] Loss: 0.87799 (0.7962) Data (t): 0.001 Batch (t): 0.930, 239.952/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,06:07:20 | INFO | Train Epoch: 1 [14694912/18327966 (80%)] Loss: 0.76767 (0.7961) Data (t): 0.001 Batch (t): 0.906, 564.837/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,06:08:55 | INFO | Train Epoch: 1 [14746112/18327966 (80%)] Loss: 0.75211 (0.7959) Data (t): 0.001 Batch (t): 0.945, 569.081/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,06:10:28 | INFO | Train Epoch: 1 [14797312/18327966 (81%)] Loss: 0.82743 (0.7961) Data (t): 0.001 Batch (t): 0.931, 562.674/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,06:12:00 | INFO | Train Epoch: 1 [14848512/18327966 (81%)] Loss: 0.91691 (0.7965) Data (t): 0.001 Batch (t): 0.918, 563.211/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,06:13:31 | INFO | Train Epoch: 1 [14899712/18327966 (81%)] Loss: 0.79240 (0.7965) Data (t): 0.001 Batch (t): 0.917, 565.881/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,06:15:03 | INFO | Train Epoch: 1 [14950912/18327966 (82%)] Loss: 0.84355 (0.7966) Data (t): 0.001 Batch (t): 0.917, 564.896/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,06:16:38 | INFO | Train Epoch: 1 [15002112/18327966 (82%)] Loss: 0.72563 (0.7964) Data (t): 0.001 Batch (t): 0.948, 566.338/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,06:18:11 | INFO | Train Epoch: 1 [15053312/18327966 (82%)] Loss: 0.92858 (0.7968) Data (t): 0.001 Batch (t): 0.931, 563.763/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,06:19:43 | INFO | Train Epoch: 1 [15104512/18327966 (82%)] Loss: 0.75354 (0.7967) Data (t): 0.001 Batch (t): 0.918, 563.463/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,06:21:13 | INFO | Train Epoch: 1 [15155712/18327966 (83%)] Loss: 0.80540 (0.7967) Data (t): 0.001 Batch (t): 0.907, 565.559/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,06:22:47 | INFO | Train Epoch: 1 [15206912/18327966 (83%)] Loss: 0.70471 (0.7964) Data (t): 0.001 Batch (t): 0.932, 565.982/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,06:24:20 | INFO | Train Epoch: 1 [15258112/18327966 (83%)] Loss: 0.79231 (0.7964) Data (t): 0.001 Batch (t): 0.935, 566.611/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,06:25:53 | INFO | Train Epoch: 1 [15309312/18327966 (84%)] Loss: 0.82973 (0.7965) Data (t): 0.001 Batch (t): 0.931, 563.138/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,06:27:25 | INFO | Train Epoch: 1 [15360512/18327966 (84%)] Loss: 0.73386 (0.7963) Data (t): 0.001 Batch (t): 0.919, 565.551/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,06:28:57 | INFO | Train Epoch: 1 [15411712/18327966 (84%)] Loss: 0.89775 (0.7966) Data (t): 0.001 Batch (t): 0.918, 566.389/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,06:30:30 | INFO | Train Epoch: 1 [15462912/18327966 (84%)] Loss: 0.67697 (0.7962) Data (t): 0.001 Batch (t): 0.931, 566.484/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,06:32:02 | INFO | Train Epoch: 1 [15514112/18327966 (85%)] Loss: 0.77415 (0.7962) Data (t): 0.001 Batch (t): 0.924, 566.422/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,06:33:35 | INFO | Train Epoch: 1 [15565312/18327966 (85%)] Loss: 0.83457 (0.7963) Data (t): 0.001 Batch (t): 0.930, 565.591/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,06:35:09 | INFO | Train Epoch: 1 [15616512/18327966 (85%)] Loss: 0.76966 (0.7962) Data (t): 0.001 Batch (t): 0.931, 563.988/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,06:36:40 | INFO | Train Epoch: 1 [15667712/18327966 (85%)] Loss: 0.77668 (0.7961) Data (t): 0.001 Batch (t): 0.919, 569.898/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,06:38:14 | INFO | Train Epoch: 1 [15718912/18327966 (86%)] Loss: 0.69713 (0.7958) Data (t): 0.001 Batch (t): 0.931, 564.088/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,06:39:44 | INFO | Train Epoch: 1 [15770112/18327966 (86%)] Loss: 0.92327 (0.7962) Data (t): 0.001 Batch (t): 0.906, 563.066/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,06:41:19 | INFO | Train Epoch: 1 [15821312/18327966 (86%)] Loss: 0.66231 (0.7958) Data (t): 0.001 Batch (t): 0.948, 565.490/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,06:42:52 | INFO | Train Epoch: 1 [15872512/18327966 (87%)] Loss: 0.70414 (0.7955) Data (t): 0.001 Batch (t): 0.931, 564.100/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,06:44:24 | INFO | Train Epoch: 1 [15923712/18327966 (87%)] Loss: 0.80168 (0.7955) Data (t): 0.001 Batch (t): 0.919, 564.836/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,06:45:57 | INFO | Train Epoch: 1 [15974912/18327966 (87%)] Loss: 0.74176 (0.7953) Data (t): 0.001 Batch (t): 0.930, 569.060/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,06:47:28 | INFO | Train Epoch: 1 [16026112/18327966 (87%)] Loss: 0.76458 (0.7952) Data (t): 0.001 Batch (t): 0.905, 562.925/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,06:49:02 | INFO | Train Epoch: 1 [16077312/18327966 (88%)] Loss: 0.83599 (0.7954) Data (t): 0.001 Batch (t): 0.949, 560.302/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,06:50:36 | INFO | Train Epoch: 1 [16128512/18327966 (88%)] Loss: 0.79025 (0.7954) Data (t): 0.001 Batch (t): 0.931, 564.811/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,06:52:07 | INFO | Train Epoch: 1 [16179712/18327966 (88%)] Loss: 0.77030 (0.7953) Data (t): 0.001 Batch (t): 0.919, 565.000/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,06:53:39 | INFO | Train Epoch: 1 [16230912/18327966 (89%)] Loss: 0.76600 (0.7952) Data (t): 0.001 Batch (t): 0.920, 562.130/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,06:55:11 | INFO | Train Epoch: 1 [16282112/18327966 (89%)] Loss: 0.83937 (0.7953) Data (t): 0.001 Batch (t): 0.919, 562.475/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,06:56:45 | INFO | Train Epoch: 1 [16333312/18327966 (89%)] Loss: 0.79132 (0.7953) Data (t): 0.001 Batch (t): 0.936, 564.675/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,06:58:18 | INFO | Train Epoch: 1 [16384512/18327966 (89%)] Loss: 0.90443 (0.7957) Data (t): 0.001 Batch (t): 0.932, 562.835/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,06:59:50 | INFO | Train Epoch: 1 [16435712/18327966 (90%)] Loss: 0.87648 (0.7959) Data (t): 0.001 Batch (t): 0.921, 564.614/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,07:01:23 | INFO | Train Epoch: 1 [16486912/18327966 (90%)] Loss: 0.75286 (0.7958) Data (t): 0.001 Batch (t): 0.931, 565.403/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,07:02:55 | INFO | Train Epoch: 1 [16538112/18327966 (90%)] Loss: 0.68770 (0.7954) Data (t): 0.001 Batch (t): 0.919, 564.587/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,07:04:28 | INFO | Train Epoch: 1 [16589312/18327966 (91%)] Loss: 0.84020 (0.7956) Data (t): 0.001 Batch (t): 0.924, 565.546/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,07:06:02 | INFO | Train Epoch: 1 [16640512/18327966 (91%)] Loss: 0.74786 (0.7954) Data (t): 0.001 Batch (t): 0.945, 565.087/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,07:07:34 | INFO | Train Epoch: 1 [16691712/18327966 (91%)] Loss: 0.68663 (0.7951) Data (t): 0.001 Batch (t): 0.921, 565.713/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,07:09:06 | INFO | Train Epoch: 1 [16742912/18327966 (91%)] Loss: 0.84688 (0.7953) Data (t): 0.001 Batch (t): 0.921, 567.278/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,07:10:39 | INFO | Train Epoch: 1 [16794112/18327966 (92%)] Loss: 0.82107 (0.7953) Data (t): 0.001 Batch (t): 0.931, 567.127/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,07:12:12 | INFO | Train Epoch: 1 [16845312/18327966 (92%)] Loss: 0.78816 (0.7953) Data (t): 0.001 Batch (t): 0.925, 563.813/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,07:13:45 | INFO | Train Epoch: 1 [16896512/18327966 (92%)] Loss: 0.83110 (0.7954) Data (t): 0.001 Batch (t): 0.931, 563.827/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,07:15:18 | INFO | Train Epoch: 1 [16947712/18327966 (92%)] Loss: 0.85019 (0.7956) Data (t): 0.001 Batch (t): 0.933, 564.191/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,07:16:50 | INFO | Train Epoch: 1 [16998912/18327966 (93%)] Loss: 0.85878 (0.7958) Data (t): 0.001 Batch (t): 0.920, 562.296/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,07:18:24 | INFO | Train Epoch: 1 [17050112/18327966 (93%)] Loss: 0.81731 (0.7958) Data (t): 0.001 Batch (t): 0.933, 565.926/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,07:19:54 | INFO | Train Epoch: 1 [17101312/18327966 (93%)] Loss: 0.76647 (0.7958) Data (t): 0.001 Batch (t): 0.908, 566.027/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,07:21:29 | INFO | Train Epoch: 1 [17152512/18327966 (94%)] Loss: 0.76564 (0.7957) Data (t): 0.001 Batch (t): 0.950, 567.607/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,07:23:03 | INFO | Train Epoch: 1 [17203712/18327966 (94%)] Loss: 0.72001 (0.7954) Data (t): 0.001 Batch (t): 0.934, 560.898/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,07:24:35 | INFO | Train Epoch: 1 [17254912/18327966 (94%)] Loss: 0.73603 (0.7953) Data (t): 0.001 Batch (t): 0.921, 561.883/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,07:26:08 | INFO | Train Epoch: 1 [17306112/18327966 (94%)] Loss: 0.76703 (0.7952) Data (t): 0.001 Batch (t): 0.934, 565.105/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,07:27:39 | INFO | Train Epoch: 1 [17357312/18327966 (95%)] Loss: 0.63875 (0.7947) Data (t): 0.001 Batch (t): 0.907, 564.561/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,07:29:13 | INFO | Train Epoch: 1 [17408512/18327966 (95%)] Loss: 0.73546 (0.7945) Data (t): 0.001 Batch (t): 0.937, 564.923/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,07:30:47 | INFO | Train Epoch: 1 [17459712/18327966 (95%)] Loss: 0.75531 (0.7944) Data (t): 0.001 Batch (t): 0.945, 565.154/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,07:32:18 | INFO | Train Epoch: 1 [17510912/18327966 (96%)] Loss: 0.84992 (0.7946) Data (t): 0.001 Batch (t): 0.906, 564.461/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,07:33:51 | INFO | Train Epoch: 1 [17562112/18327966 (96%)] Loss: 0.78818 (0.7946) Data (t): 0.001 Batch (t): 0.931, 565.378/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,07:35:23 | INFO | Train Epoch: 1 [17613312/18327966 (96%)] Loss: 0.77845 (0.7945) Data (t): 0.001 Batch (t): 0.919, 568.449/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,07:36:55 | INFO | Train Epoch: 1 [17664512/18327966 (96%)] Loss: 0.84285 (0.7947) Data (t): 0.001 Batch (t): 0.923, 564.718/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,07:38:29 | INFO | Train Epoch: 1 [17715712/18327966 (97%)] Loss: 0.77698 (0.7946) Data (t): 0.001 Batch (t): 0.944, 563.832/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,07:40:01 | INFO | Train Epoch: 1 [17766912/18327966 (97%)] Loss: 0.68883 (0.7943) Data (t): 0.001 Batch (t): 0.919, 564.685/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,07:41:35 | INFO | Train Epoch: 1 [17818112/18327966 (97%)] Loss: 0.76970 (0.7942) Data (t): 0.001 Batch (t): 0.931, 235.810/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,07:43:06 | INFO | Train Epoch: 1 [17869312/18327966 (97%)] Loss: 0.71112 (0.7940) Data (t): 0.001 Batch (t): 0.919, 564.466/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,07:44:39 | INFO | Train Epoch: 1 [17920512/18327966 (98%)] Loss: 0.76490 (0.7939) Data (t): 0.001 Batch (t): 0.924, 565.510/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,07:46:12 | INFO | Train Epoch: 1 [17971712/18327966 (98%)] Loss: 0.70885 (0.7937) Data (t): 0.001 Batch (t): 0.932, 565.410/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,07:47:45 | INFO | Train Epoch: 1 [18022912/18327966 (98%)] Loss: 0.86784 (0.7939) Data (t): 0.001 Batch (t): 0.933, 566.470/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,07:49:17 | INFO | Train Epoch: 1 [18074112/18327966 (99%)] Loss: 0.76639 (0.7938) Data (t): 0.001 Batch (t): 0.921, 561.909/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,07:50:51 | INFO | Train Epoch: 1 [18125312/18327966 (99%)] Loss: 0.74836 (0.7937) Data (t): 0.001 Batch (t): 0.932, 565.628/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,07:52:21 | INFO | Train Epoch: 1 [18176512/18327966 (99%)] Loss: 0.91677 (0.7940) Data (t): 0.001 Batch (t): 0.906, 565.883/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,07:53:56 | INFO | Train Epoch: 1 [18227712/18327966 (99%)] Loss: 0.77251 (0.7940) Data (t): 0.001 Batch (t): 0.950, 234.055/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,07:55:30 | INFO | Train Epoch: 1 [18278912/18327966 (100%)] Loss: 0.74478 (0.7938) Data (t): 0.001 Batch (t): 0.933, 565.545/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,07:56:57 | INFO | Train Epoch: 1 [18327552/18327966 (100%)] Loss: 0.76812 (0.7938) Data (t): 0.002 Batch (t): 0.921, 566.683/s LR: 0.000000 Logit Scale: 100.000 - V4 diff --git a/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten_decimal2/2024_11_26-13_30_24-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/params.txt b/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten_decimal2/2024_11_26-13_30_24-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/params.txt deleted file mode 100644 index 6fc67aaf38ae350cc352b549d7b808e536b7b170..0000000000000000000000000000000000000000 --- a/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten_decimal2/2024_11_26-13_30_24-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/params.txt +++ /dev/null @@ -1,67 +0,0 @@ -batch_size: 64 -beta1: 0.9 -beta2: 0.98 -checkpoint_path: data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten_decimal2/2024_11_26-13_30_24-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/checkpoints -copy_codebase: False -csv_caption_key: caption -csv_hard_captions_key: neg_caption -csv_img_key: img_path -csv_separator: , -dataset_resampled: False -dataset_type: csv -ddp_static_graph: False -debug: False -device: cuda:0 -dist_backend: nccl -dist_url: env:// -distributed: True -epochs: 2 -eps: 1e-06 -force_quick_gelu: True -gather_with_grad: False -grad_checkpointing: False -horovod: False -imagenet_v2: None -imagenet_val: None -local_loss: False -local_rank: 0 -lock_image: False -lock_image_freeze_bn_stats: False -lock_image_unlocked_groups: 0 -log_level: 20 -log_local: False -log_path: data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten_decimal2/2024_11_26-13_30_24-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/out.log -logs: data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten_decimal2 -lr: 1e-06 -model: ViT-L-14-336 -name: 2024_11_26-13_30_24-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp -no_set_device_rank: False -norm_gradient_clip: None -precision: amp -pretrained: data/openclip-vit-14-336/openclip_model.pt -pretrained_image: False -rank: 0 -report_to: wandb -resume: None -save_frequency: 1 -save_most_recent: False -seed: 0 -skip_scheduler: False -tensorboard: False -tensorboard_path: -torchscript: False -trace: False -train_data: csv_data/plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten_decimal2.csv -train_num_samples: None -use_bn_sync: False -val_data: None -val_frequency: 1 -val_num_samples: None -wandb: True -wandb_notes: -wandb_project: neg-clip-plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten_decimal2 -warmup: 0 -wd: 0.1 -workers: 4 -world_size: 8 -zeroshot_frequency: 2 diff --git a/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten_decimal2/2024_11_27-07_57_39-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/checkpoints/epoch_1.pt b/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten_decimal2/2024_11_27-07_57_39-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/checkpoints/epoch_1.pt deleted file mode 100644 index e677b880d5c45401b4151b8c2e0a4180375c7013..0000000000000000000000000000000000000000 --- a/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten_decimal2/2024_11_27-07_57_39-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/checkpoints/epoch_1.pt +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:b8e40d53207da9b7a2e89f6f35b3b786bce4d9ce046aebaeeebc7dab7373604d -size 5135890710 diff --git a/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten_decimal2/2024_11_27-07_57_39-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/checkpoints/epoch_2.pt b/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten_decimal2/2024_11_27-07_57_39-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/checkpoints/epoch_2.pt deleted file mode 100644 index 757bce07f394ea781f500066dac3208f9dd00f70..0000000000000000000000000000000000000000 --- a/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten_decimal2/2024_11_27-07_57_39-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/checkpoints/epoch_2.pt +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:932e17d1c182768f5ecb56227aba285f332d2d3326912e8ad0c96326f93f710e -size 5135890710 diff --git a/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten_decimal2/2024_11_27-07_57_39-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/out.log b/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten_decimal2/2024_11_27-07_57_39-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/out.log deleted file mode 100644 index c2945e6c596c025239eec6b39ed155c498f4ebaf..0000000000000000000000000000000000000000 --- a/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten_decimal2/2024_11_27-07_57_39-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/out.log +++ /dev/null @@ -1,834 +0,0 @@ -2024-11-27,07:57:40 | INFO | Running in distributed mode with multiple processes. Device: cuda:0.Process (global: 0, local 0), total 8. -2024-11-27,07:57:40 | INFO | Loading ViT-L-14-336 model config. -2024-11-27,07:57:43 | INFO | Loading pretrained ViT-L-14-336 weights (data/openclip-vit-14-336/openclip_model.pt). -2024-11-27,07:57:50 | INFO | Model: -2024-11-27,07:57:50 | INFO | CLIP( - (visual): VisualTransformer( - (conv1): Conv2d(3, 1024, kernel_size=(14, 14), stride=(14, 14), bias=False) - (ln_pre): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) - (transformer): Transformer( - (resblocks): ModuleList( - (0-23): 24 x ResidualAttentionBlock( - (attn): MultiheadAttention( - (out_proj): NonDynamicallyQuantizableLinear(in_features=1024, out_features=1024, bias=True) - ) - (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) - (mlp): Sequential( - (c_fc): Linear(in_features=1024, out_features=4096, bias=True) - (gelu): QuickGELU() - (c_proj): Linear(in_features=4096, out_features=1024, bias=True) - ) - (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) - ) - ) - ) - (ln_post): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) - ) - (transformer): Transformer( - (resblocks): ModuleList( - (0-11): 12 x ResidualAttentionBlock( - (attn): MultiheadAttention( - (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) - ) - (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) - (mlp): Sequential( - (c_fc): Linear(in_features=768, out_features=3072, bias=True) - (gelu): QuickGELU() - (c_proj): Linear(in_features=3072, out_features=768, bias=True) - ) - (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) - ) - ) - ) - (token_embedding): Embedding(49408, 768) - (ln_final): LayerNorm((768,), eps=1e-05, elementwise_affine=True) -) -2024-11-27,07:57:50 | INFO | Params: -2024-11-27,07:57:50 | INFO | batch_size: 64 -2024-11-27,07:57:50 | INFO | beta1: 0.9 -2024-11-27,07:57:50 | INFO | beta2: 0.98 -2024-11-27,07:57:50 | INFO | checkpoint_path: data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten_decimal2/2024_11_27-07_57_39-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/checkpoints -2024-11-27,07:57:50 | INFO | copy_codebase: False -2024-11-27,07:57:50 | INFO | csv_caption_key: caption -2024-11-27,07:57:50 | INFO | csv_hard_captions_key: neg_caption -2024-11-27,07:57:50 | INFO | csv_img_key: img_path -2024-11-27,07:57:50 | INFO | csv_separator: , -2024-11-27,07:57:50 | INFO | dataset_resampled: False -2024-11-27,07:57:50 | INFO | dataset_type: csv -2024-11-27,07:57:50 | INFO | ddp_static_graph: False -2024-11-27,07:57:50 | INFO | debug: False -2024-11-27,07:57:50 | INFO | device: cuda:0 -2024-11-27,07:57:50 | INFO | dist_backend: nccl -2024-11-27,07:57:50 | INFO | dist_url: env:// -2024-11-27,07:57:50 | INFO | distributed: True -2024-11-27,07:57:50 | INFO | epochs: 2 -2024-11-27,07:57:50 | INFO | eps: 1e-06 -2024-11-27,07:57:50 | INFO | force_quick_gelu: True -2024-11-27,07:57:50 | INFO | gather_with_grad: False -2024-11-27,07:57:50 | INFO | grad_checkpointing: False -2024-11-27,07:57:50 | INFO | horovod: False -2024-11-27,07:57:50 | INFO | imagenet_v2: None -2024-11-27,07:57:50 | INFO | imagenet_val: None -2024-11-27,07:57:50 | INFO | local_loss: False -2024-11-27,07:57:50 | INFO | local_rank: 0 -2024-11-27,07:57:50 | INFO | lock_image: False -2024-11-27,07:57:50 | INFO | lock_image_freeze_bn_stats: False -2024-11-27,07:57:50 | INFO | lock_image_unlocked_groups: 0 -2024-11-27,07:57:50 | INFO | log_level: 20 -2024-11-27,07:57:50 | INFO | log_local: False -2024-11-27,07:57:50 | INFO | log_path: data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten_decimal2/2024_11_27-07_57_39-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/out.log -2024-11-27,07:57:50 | INFO | logs: data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten_decimal2 -2024-11-27,07:57:50 | INFO | lr: 5e-06 -2024-11-27,07:57:50 | INFO | model: ViT-L-14-336 -2024-11-27,07:57:50 | INFO | name: 2024_11_27-07_57_39-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp -2024-11-27,07:57:50 | INFO | no_set_device_rank: False -2024-11-27,07:57:50 | INFO | norm_gradient_clip: None -2024-11-27,07:57:50 | INFO | precision: amp -2024-11-27,07:57:50 | INFO | pretrained: data/openclip-vit-14-336/openclip_model.pt -2024-11-27,07:57:50 | INFO | pretrained_image: False -2024-11-27,07:57:50 | INFO | rank: 0 -2024-11-27,07:57:50 | INFO | report_to: wandb -2024-11-27,07:57:50 | INFO | resume: None -2024-11-27,07:57:50 | INFO | save_frequency: 1 -2024-11-27,07:57:50 | INFO | save_most_recent: False -2024-11-27,07:57:50 | INFO | seed: 0 -2024-11-27,07:57:50 | INFO | skip_scheduler: False -2024-11-27,07:57:50 | INFO | tensorboard: False -2024-11-27,07:57:50 | INFO | tensorboard_path: -2024-11-27,07:57:50 | INFO | torchscript: False -2024-11-27,07:57:50 | INFO | trace: False -2024-11-27,07:57:50 | INFO | train_data: csv_data/plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten_decimal2.csv -2024-11-27,07:57:50 | INFO | train_num_samples: None -2024-11-27,07:57:50 | INFO | use_bn_sync: False -2024-11-27,07:57:50 | INFO | val_data: None -2024-11-27,07:57:50 | INFO | val_frequency: 1 -2024-11-27,07:57:50 | INFO | val_num_samples: None -2024-11-27,07:57:50 | INFO | wandb: True -2024-11-27,07:57:50 | INFO | wandb_notes: -2024-11-27,07:57:50 | INFO | wandb_project: neg-clip-plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten_decimal2 -2024-11-27,07:57:50 | INFO | warmup: 0 -2024-11-27,07:57:50 | INFO | wd: 0.1 -2024-11-27,07:57:50 | INFO | workers: 4 -2024-11-27,07:57:50 | INFO | world_size: 8 -2024-11-27,07:57:50 | INFO | zeroshot_frequency: 2 -2024-11-27,07:59:17 | INFO | Init a wandb project! -2024-11-27,07:59:24 | INFO | Start epoch 0 -2024-11-27,07:59:31 | INFO | Train Epoch: 0 [ 512/18327966 (0%)] Loss: 5.0606 (5.061) Data (t): 3.775 Batch (t): 7.492, 68.3369/s LR: 0.000005 Logit Scale: 100.000 - V4 -2024-11-27,08:01:04 | INFO | Train Epoch: 0 [ 51712/18327966 (0%)] Loss: 1.6181 (3.339) Data (t): 0.001 Batch (t): 0.923, 561.385/s LR: 0.000005 Logit Scale: 99.995 - V4 -2024-11-27,08:02:35 | INFO | Train Epoch: 0 [ 102912/18327966 (1%)] Loss: 1.3159 (2.665) Data (t): 0.001 Batch (t): 0.912, 567.053/s LR: 0.000005 Logit Scale: 99.997 - V4 -2024-11-27,08:04:07 | INFO | Train Epoch: 0 [ 154112/18327966 (1%)] Loss: 1.2449 (2.310) Data (t): 0.001 Batch (t): 0.923, 559.825/s LR: 0.000005 Logit Scale: 99.995 - V4 -2024-11-27,08:05:44 | INFO | Train Epoch: 0 [ 205312/18327966 (1%)] Loss: 1.2796 (2.104) Data (t): 0.001 Batch (t): 0.964, 563.010/s LR: 0.000005 Logit Scale: 99.996 - V4 -2024-11-27,08:07:15 | INFO | Train Epoch: 0 [ 256512/18327966 (1%)] Loss: 1.2208 (1.957) Data (t): 0.001 Batch (t): 0.910, 563.288/s LR: 0.000005 Logit Scale: 99.995 - V4 -2024-11-27,08:08:46 | INFO | Train Epoch: 0 [ 307712/18327966 (2%)] Loss: 1.2502 (1.856) Data (t): 0.001 Batch (t): 0.911, 561.079/s LR: 0.000005 Logit Scale: 99.993 - V4 -2024-11-27,08:10:17 | INFO | Train Epoch: 0 [ 358912/18327966 (2%)] Loss: 1.0592 (1.756) Data (t): 0.001 Batch (t): 0.912, 564.608/s LR: 0.000005 Logit Scale: 99.990 - V4 -2024-11-27,08:11:49 | INFO | Train Epoch: 0 [ 410112/18327966 (2%)] Loss: 1.1632 (1.690) Data (t): 0.001 Batch (t): 0.920, 558.471/s LR: 0.000005 Logit Scale: 99.988 - V4 -2024-11-27,08:13:25 | INFO | Train Epoch: 0 [ 461312/18327966 (3%)] Loss: 1.1426 (1.636) Data (t): 0.001 Batch (t): 0.962, 565.016/s LR: 0.000005 Logit Scale: 99.986 - V4 -2024-11-27,08:14:56 | INFO | Train Epoch: 0 [ 512512/18327966 (3%)] Loss: 0.98734 (1.577) Data (t): 0.001 Batch (t): 0.910, 559.700/s LR: 0.000005 Logit Scale: 99.981 - V4 -2024-11-27,08:16:27 | INFO | Train Epoch: 0 [ 563712/18327966 (3%)] Loss: 1.0069 (1.529) Data (t): 0.001 Batch (t): 0.910, 561.108/s LR: 0.000005 Logit Scale: 99.980 - V4 -2024-11-27,08:17:58 | INFO | Train Epoch: 0 [ 614912/18327966 (3%)] Loss: 1.0545 (1.493) Data (t): 0.001 Batch (t): 0.910, 564.702/s LR: 0.000005 Logit Scale: 99.977 - V4 -2024-11-27,08:19:29 | INFO | Train Epoch: 0 [ 666112/18327966 (4%)] Loss: 1.2115 (1.473) Data (t): 0.001 Batch (t): 0.909, 563.297/s LR: 0.000005 Logit Scale: 99.973 - V4 -2024-11-27,08:21:08 | INFO | Train Epoch: 0 [ 717312/18327966 (4%)] Loss: 0.92810 (1.436) Data (t): 0.001 Batch (t): 0.992, 562.918/s LR: 0.000005 Logit Scale: 99.970 - V4 -2024-11-27,08:22:39 | INFO | Train Epoch: 0 [ 768512/18327966 (4%)] Loss: 0.97925 (1.408) Data (t): 0.001 Batch (t): 0.908, 563.289/s LR: 0.000005 Logit Scale: 99.968 - V4 -2024-11-27,08:24:10 | INFO | Train Epoch: 0 [ 819712/18327966 (4%)] Loss: 1.0355 (1.386) Data (t): 0.001 Batch (t): 0.908, 563.457/s LR: 0.000005 Logit Scale: 99.963 - V4 -2024-11-27,08:25:41 | INFO | Train Epoch: 0 [ 870912/18327966 (5%)] Loss: 0.94014 (1.361) Data (t): 0.001 Batch (t): 0.909, 565.628/s LR: 0.000005 Logit Scale: 99.960 - V4 -2024-11-27,08:27:12 | INFO | Train Epoch: 0 [ 922112/18327966 (5%)] Loss: 0.98591 (1.341) Data (t): 0.001 Batch (t): 0.909, 561.070/s LR: 0.000005 Logit Scale: 99.959 - V4 -2024-11-27,08:28:51 | INFO | Train Epoch: 0 [ 973312/18327966 (5%)] Loss: 1.1589 (1.332) Data (t): 0.001 Batch (t): 0.992, 562.400/s LR: 0.000005 Logit Scale: 99.954 - V4 -2024-11-27,08:30:22 | INFO | Train Epoch: 0 [ 1024512/18327966 (6%)] Loss: 0.96027 (1.314) Data (t): 0.001 Batch (t): 0.909, 563.650/s LR: 0.000005 Logit Scale: 99.949 - V4 -2024-11-27,08:31:52 | INFO | Train Epoch: 0 [ 1075712/18327966 (6%)] Loss: 1.0499 (1.302) Data (t): 0.001 Batch (t): 0.909, 561.702/s LR: 0.000005 Logit Scale: 99.949 - V4 -2024-11-27,08:33:23 | INFO | Train Epoch: 0 [ 1126912/18327966 (6%)] Loss: 1.0136 (1.290) Data (t): 0.001 Batch (t): 0.909, 564.547/s LR: 0.000005 Logit Scale: 99.945 - V4 -2024-11-27,08:34:54 | INFO | Train Epoch: 0 [ 1178112/18327966 (6%)] Loss: 0.97262 (1.277) Data (t): 0.001 Batch (t): 0.908, 564.800/s LR: 0.000005 Logit Scale: 99.944 - V4 -2024-11-27,08:36:30 | INFO | Train Epoch: 0 [ 1229312/18327966 (7%)] Loss: 0.99792 (1.265) Data (t): 0.001 Batch (t): 0.963, 255.093/s LR: 0.000005 Logit Scale: 99.941 - V4 -2024-11-27,08:38:05 | INFO | Train Epoch: 0 [ 1280512/18327966 (7%)] Loss: 0.99364 (1.255) Data (t): 0.001 Batch (t): 0.948, 558.514/s LR: 0.000005 Logit Scale: 99.941 - V4 -2024-11-27,08:39:36 | INFO | Train Epoch: 0 [ 1331712/18327966 (7%)] Loss: 0.96818 (1.244) Data (t): 0.001 Batch (t): 0.910, 566.891/s LR: 0.000005 Logit Scale: 99.938 - V4 -2024-11-27,08:41:07 | INFO | Train Epoch: 0 [ 1382912/18327966 (8%)] Loss: 0.93553 (1.233) Data (t): 0.001 Batch (t): 0.909, 562.066/s LR: 0.000005 Logit Scale: 99.934 - V4 -2024-11-27,08:42:38 | INFO | Train Epoch: 0 [ 1434112/18327966 (8%)] Loss: 1.0753 (1.228) Data (t): 0.001 Batch (t): 0.909, 561.526/s LR: 0.000005 Logit Scale: 99.931 - V4 -2024-11-27,08:44:10 | INFO | Train Epoch: 0 [ 1485312/18327966 (8%)] Loss: 1.0339 (1.221) Data (t): 0.001 Batch (t): 0.920, 562.016/s LR: 0.000005 Logit Scale: 99.928 - V4 -2024-11-27,08:45:48 | INFO | Train Epoch: 0 [ 1536512/18327966 (8%)] Loss: 0.91008 (1.211) Data (t): 0.001 Batch (t): 0.980, 560.091/s LR: 0.000005 Logit Scale: 99.924 - V4 -2024-11-27,08:47:19 | INFO | Train Epoch: 0 [ 1587712/18327966 (9%)] Loss: 0.99676 (1.205) Data (t): 0.001 Batch (t): 0.909, 556.769/s LR: 0.000005 Logit Scale: 99.923 - V4 -2024-11-27,08:48:50 | INFO | Train Epoch: 0 [ 1638912/18327966 (9%)] Loss: 0.93546 (1.197) Data (t): 0.001 Batch (t): 0.909, 563.697/s LR: 0.000005 Logit Scale: 99.922 - V4 -2024-11-27,08:50:21 | INFO | Train Epoch: 0 [ 1690112/18327966 (9%)] Loss: 0.98185 (1.190) Data (t): 0.001 Batch (t): 0.909, 563.665/s LR: 0.000005 Logit Scale: 99.919 - V4 -2024-11-27,08:51:53 | INFO | Train Epoch: 0 [ 1741312/18327966 (10%)] Loss: 1.0108 (1.185) Data (t): 0.001 Batch (t): 0.921, 557.007/s LR: 0.000005 Logit Scale: 99.919 - V4 -2024-11-27,08:53:32 | INFO | Train Epoch: 0 [ 1792512/18327966 (10%)] Loss: 0.98104 (1.179) Data (t): 0.001 Batch (t): 0.991, 562.773/s LR: 0.000005 Logit Scale: 99.915 - V4 -2024-11-27,08:55:03 | INFO | Train Epoch: 0 [ 1843712/18327966 (10%)] Loss: 1.0898 (1.177) Data (t): 0.001 Batch (t): 0.910, 563.083/s LR: 0.000005 Logit Scale: 99.913 - V4 -2024-11-27,08:56:34 | INFO | Train Epoch: 0 [ 1894912/18327966 (10%)] Loss: 0.82164 (1.168) Data (t): 0.001 Batch (t): 0.909, 565.616/s LR: 0.000005 Logit Scale: 99.908 - V4 -2024-11-27,08:58:05 | INFO | Train Epoch: 0 [ 1946112/18327966 (11%)] Loss: 0.85914 (1.160) Data (t): 0.001 Batch (t): 0.910, 565.690/s LR: 0.000005 Logit Scale: 99.907 - V4 -2024-11-27,08:59:36 | INFO | Train Epoch: 0 [ 1997312/18327966 (11%)] Loss: 0.95131 (1.155) Data (t): 0.001 Batch (t): 0.909, 562.107/s LR: 0.000005 Logit Scale: 99.903 - V4 -2024-11-27,09:01:15 | INFO | Train Epoch: 0 [ 2048512/18327966 (11%)] Loss: 0.97630 (1.150) Data (t): 0.001 Batch (t): 0.993, 564.955/s LR: 0.000005 Logit Scale: 99.901 - V4 -2024-11-27,09:02:46 | INFO | Train Epoch: 0 [ 2099712/18327966 (11%)] Loss: 0.90428 (1.144) Data (t): 0.001 Batch (t): 0.909, 560.942/s LR: 0.000005 Logit Scale: 99.902 - V4 -2024-11-27,09:04:17 | INFO | Train Epoch: 0 [ 2150912/18327966 (12%)] Loss: 0.90569 (1.139) Data (t): 0.001 Batch (t): 0.909, 562.556/s LR: 0.000005 Logit Scale: 99.897 - V4 -2024-11-27,09:05:48 | INFO | Train Epoch: 0 [ 2202112/18327966 (12%)] Loss: 1.0570 (1.137) Data (t): 0.001 Batch (t): 0.910, 564.616/s LR: 0.000005 Logit Scale: 99.894 - V4 -2024-11-27,09:07:19 | INFO | Train Epoch: 0 [ 2253312/18327966 (12%)] Loss: 0.95119 (1.133) Data (t): 0.001 Batch (t): 0.909, 566.111/s LR: 0.000005 Logit Scale: 99.891 - V4 -2024-11-27,09:08:58 | INFO | Train Epoch: 0 [ 2304512/18327966 (13%)] Loss: 0.83913 (1.126) Data (t): 0.001 Batch (t): 0.990, 567.967/s LR: 0.000005 Logit Scale: 99.888 - V4 -2024-11-27,09:10:28 | INFO | Train Epoch: 0 [ 2355712/18327966 (13%)] Loss: 0.96048 (1.123) Data (t): 0.001 Batch (t): 0.908, 562.180/s LR: 0.000005 Logit Scale: 99.885 - V4 -2024-11-27,09:11:59 | INFO | Train Epoch: 0 [ 2406912/18327966 (13%)] Loss: 0.76444 (1.115) Data (t): 0.001 Batch (t): 0.908, 564.279/s LR: 0.000005 Logit Scale: 99.883 - V4 -2024-11-27,09:13:30 | INFO | Train Epoch: 0 [ 2458112/18327966 (13%)] Loss: 0.91621 (1.111) Data (t): 0.001 Batch (t): 0.908, 565.171/s LR: 0.000005 Logit Scale: 99.879 - V4 -2024-11-27,09:15:01 | INFO | Train Epoch: 0 [ 2509312/18327966 (14%)] Loss: 1.0133 (1.109) Data (t): 0.001 Batch (t): 0.907, 562.350/s LR: 0.000005 Logit Scale: 99.875 - V4 -2024-11-27,09:16:37 | INFO | Train Epoch: 0 [ 2560512/18327966 (14%)] Loss: 1.0334 (1.108) Data (t): 0.001 Batch (t): 0.958, 193.423/s LR: 0.000005 Logit Scale: 99.875 - V4 -2024-11-27,09:18:10 | INFO | Train Epoch: 0 [ 2611712/18327966 (14%)] Loss: 0.79780 (1.102) Data (t): 0.001 Batch (t): 0.939, 562.723/s LR: 0.000005 Logit Scale: 99.872 - V4 -2024-11-27,09:19:41 | INFO | Train Epoch: 0 [ 2662912/18327966 (15%)] Loss: 0.75784 (1.095) Data (t): 0.001 Batch (t): 0.908, 564.935/s LR: 0.000005 Logit Scale: 99.868 - V4 -2024-11-27,09:21:12 | INFO | Train Epoch: 0 [ 2714112/18327966 (15%)] Loss: 0.82683 (1.090) Data (t): 0.001 Batch (t): 0.909, 556.979/s LR: 0.000005 Logit Scale: 99.864 - V4 -2024-11-27,09:22:43 | INFO | Train Epoch: 0 [ 2765312/18327966 (15%)] Loss: 0.92814 (1.088) Data (t): 0.001 Batch (t): 0.909, 561.902/s LR: 0.000005 Logit Scale: 99.862 - V4 -2024-11-27,09:24:15 | INFO | Train Epoch: 0 [ 2816512/18327966 (15%)] Loss: 0.81453 (1.083) Data (t): 0.001 Batch (t): 0.920, 561.957/s LR: 0.000005 Logit Scale: 99.859 - V4 -2024-11-27,09:25:53 | INFO | Train Epoch: 0 [ 2867712/18327966 (16%)] Loss: 0.82089 (1.078) Data (t): 0.001 Batch (t): 0.980, 563.714/s LR: 0.000005 Logit Scale: 99.857 - V4 -2024-11-27,09:27:24 | INFO | Train Epoch: 0 [ 2918912/18327966 (16%)] Loss: 1.0867 (1.078) Data (t): 0.001 Batch (t): 0.910, 564.785/s LR: 0.000005 Logit Scale: 99.856 - V4 -2024-11-27,09:28:55 | INFO | Train Epoch: 0 [ 2970112/18327966 (16%)] Loss: 0.87039 (1.075) Data (t): 0.001 Batch (t): 0.909, 564.409/s LR: 0.000005 Logit Scale: 99.852 - V4 -2024-11-27,09:30:26 | INFO | Train Epoch: 0 [ 3021312/18327966 (16%)] Loss: 0.89650 (1.072) Data (t): 0.001 Batch (t): 0.909, 563.345/s LR: 0.000005 Logit Scale: 99.850 - V4 -2024-11-27,09:31:57 | INFO | Train Epoch: 0 [ 3072512/18327966 (17%)] Loss: 0.90265 (1.069) Data (t): 0.001 Batch (t): 0.908, 567.248/s LR: 0.000005 Logit Scale: 99.850 - V4 -2024-11-27,09:33:37 | INFO | Train Epoch: 0 [ 3123712/18327966 (17%)] Loss: 0.99153 (1.068) Data (t): 0.001 Batch (t): 1.002, 565.717/s LR: 0.000005 Logit Scale: 99.848 - V4 -2024-11-27,09:35:08 | INFO | Train Epoch: 0 [ 3174912/18327966 (17%)] Loss: 0.77194 (1.063) Data (t): 0.001 Batch (t): 0.908, 562.683/s LR: 0.000005 Logit Scale: 99.845 - V4 -2024-11-27,09:36:39 | INFO | Train Epoch: 0 [ 3226112/18327966 (18%)] Loss: 0.85193 (1.060) Data (t): 0.001 Batch (t): 0.909, 562.284/s LR: 0.000005 Logit Scale: 99.841 - V4 -2024-11-27,09:38:09 | INFO | Train Epoch: 0 [ 3277312/18327966 (18%)] Loss: 0.96566 (1.058) Data (t): 0.001 Batch (t): 0.909, 565.484/s LR: 0.000005 Logit Scale: 99.841 - V4 -2024-11-27,09:39:40 | INFO | Train Epoch: 0 [ 3328512/18327966 (18%)] Loss: 0.84090 (1.055) Data (t): 0.001 Batch (t): 0.908, 565.199/s LR: 0.000005 Logit Scale: 99.837 - V4 -2024-11-27,09:41:20 | INFO | Train Epoch: 0 [ 3379712/18327966 (18%)] Loss: 0.85410 (1.052) Data (t): 0.001 Batch (t): 1.001, 564.783/s LR: 0.000005 Logit Scale: 99.837 - V4 -2024-11-27,09:42:51 | INFO | Train Epoch: 0 [ 3430912/18327966 (19%)] Loss: 0.89552 (1.050) Data (t): 0.001 Batch (t): 0.908, 565.269/s LR: 0.000005 Logit Scale: 99.834 - V4 -2024-11-27,09:44:22 | INFO | Train Epoch: 0 [ 3482112/18327966 (19%)] Loss: 0.78884 (1.046) Data (t): 0.001 Batch (t): 0.908, 561.532/s LR: 0.000005 Logit Scale: 99.832 - V4 -2024-11-27,09:45:53 | INFO | Train Epoch: 0 [ 3533312/18327966 (19%)] Loss: 0.74916 (1.042) Data (t): 0.001 Batch (t): 0.909, 564.111/s LR: 0.000005 Logit Scale: 99.831 - V4 -2024-11-27,09:47:24 | INFO | Train Epoch: 0 [ 3584512/18327966 (20%)] Loss: 1.0060 (1.041) Data (t): 0.001 Batch (t): 0.909, 564.228/s LR: 0.000005 Logit Scale: 99.829 - V4 -2024-11-27,09:49:04 | INFO | Train Epoch: 0 [ 3635712/18327966 (20%)] Loss: 0.79490 (1.038) Data (t): 0.001 Batch (t): 1.002, 560.540/s LR: 0.000005 Logit Scale: 99.828 - V4 -2024-11-27,09:50:35 | INFO | Train Epoch: 0 [ 3686912/18327966 (20%)] Loss: 0.87529 (1.035) Data (t): 0.001 Batch (t): 0.909, 564.618/s LR: 0.000005 Logit Scale: 99.824 - V4 -2024-11-27,09:52:06 | INFO | Train Epoch: 0 [ 3738112/18327966 (20%)] Loss: 0.85811 (1.033) Data (t): 0.001 Batch (t): 0.908, 565.613/s LR: 0.000005 Logit Scale: 99.823 - V4 -2024-11-27,09:53:36 | INFO | Train Epoch: 0 [ 3789312/18327966 (21%)] Loss: 0.82736 (1.030) Data (t): 0.001 Batch (t): 0.907, 562.738/s LR: 0.000005 Logit Scale: 99.819 - V4 -2024-11-27,09:55:07 | INFO | Train Epoch: 0 [ 3840512/18327966 (21%)] Loss: 0.91975 (1.029) Data (t): 0.001 Batch (t): 0.907, 564.499/s LR: 0.000005 Logit Scale: 99.816 - V4 -2024-11-27,09:56:41 | INFO | Train Epoch: 0 [ 3891712/18327966 (21%)] Loss: 0.78535 (1.026) Data (t): 0.001 Batch (t): 0.942, 568.382/s LR: 0.000005 Logit Scale: 99.819 - V4 -2024-11-27,09:58:18 | INFO | Train Epoch: 0 [ 3942912/18327966 (22%)] Loss: 0.84916 (1.023) Data (t): 0.001 Batch (t): 0.968, 563.243/s LR: 0.000005 Logit Scale: 99.817 - V4 -2024-11-27,09:59:49 | INFO | Train Epoch: 0 [ 3994112/18327966 (22%)] Loss: 0.94177 (1.022) Data (t): 0.001 Batch (t): 0.907, 566.413/s LR: 0.000005 Logit Scale: 99.818 - V4 -2024-11-27,10:01:20 | INFO | Train Epoch: 0 [ 4045312/18327966 (22%)] Loss: 0.78203 (1.019) Data (t): 0.001 Batch (t): 0.908, 566.776/s LR: 0.000005 Logit Scale: 99.816 - V4 -2024-11-27,10:02:50 | INFO | Train Epoch: 0 [ 4096512/18327966 (22%)] Loss: 0.87331 (1.018) Data (t): 0.001 Batch (t): 0.907, 565.676/s LR: 0.000005 Logit Scale: 99.818 - V4 -2024-11-27,10:04:22 | INFO | Train Epoch: 0 [ 4147712/18327966 (23%)] Loss: 0.90113 (1.016) Data (t): 0.001 Batch (t): 0.918, 562.012/s LR: 0.000005 Logit Scale: 99.817 - V4 -2024-11-27,10:06:02 | INFO | Train Epoch: 0 [ 4198912/18327966 (23%)] Loss: 0.91425 (1.015) Data (t): 0.001 Batch (t): 1.000, 562.925/s LR: 0.000005 Logit Scale: 99.815 - V4 -2024-11-27,10:07:33 | INFO | Train Epoch: 0 [ 4250112/18327966 (23%)] Loss: 0.88473 (1.013) Data (t): 0.001 Batch (t): 0.908, 561.570/s LR: 0.000005 Logit Scale: 99.816 - V4 -2024-11-27,10:09:04 | INFO | Train Epoch: 0 [ 4301312/18327966 (23%)] Loss: 0.77397 (1.011) Data (t): 0.001 Batch (t): 0.908, 561.245/s LR: 0.000005 Logit Scale: 99.811 - V4 -2024-11-27,10:10:35 | INFO | Train Epoch: 0 [ 4352512/18327966 (24%)] Loss: 0.80335 (1.008) Data (t): 0.001 Batch (t): 0.908, 561.296/s LR: 0.000005 Logit Scale: 99.812 - V4 -2024-11-27,10:12:05 | INFO | Train Epoch: 0 [ 4403712/18327966 (24%)] Loss: 0.72516 (1.005) Data (t): 0.001 Batch (t): 0.909, 563.943/s LR: 0.000005 Logit Scale: 99.811 - V4 -2024-11-27,10:13:45 | INFO | Train Epoch: 0 [ 4454912/18327966 (24%)] Loss: 0.89058 (1.004) Data (t): 0.001 Batch (t): 1.001, 564.796/s LR: 0.000005 Logit Scale: 99.809 - V4 -2024-11-27,10:15:16 | INFO | Train Epoch: 0 [ 4506112/18327966 (25%)] Loss: 0.87446 (1.002) Data (t): 0.001 Batch (t): 0.908, 565.272/s LR: 0.000005 Logit Scale: 99.805 - V4 -2024-11-27,10:16:47 | INFO | Train Epoch: 0 [ 4557312/18327966 (25%)] Loss: 0.66972 (0.9985) Data (t): 0.001 Batch (t): 0.910, 560.842/s LR: 0.000005 Logit Scale: 99.806 - V4 -2024-11-27,10:18:18 | INFO | Train Epoch: 0 [ 4608512/18327966 (25%)] Loss: 0.87360 (0.9971) Data (t): 0.001 Batch (t): 0.910, 563.774/s LR: 0.000005 Logit Scale: 99.806 - V4 -2024-11-27,10:19:49 | INFO | Train Epoch: 0 [ 4659712/18327966 (25%)] Loss: 0.84051 (0.9954) Data (t): 0.001 Batch (t): 0.909, 564.257/s LR: 0.000005 Logit Scale: 99.809 - V4 -2024-11-27,10:21:29 | INFO | Train Epoch: 0 [ 4710912/18327966 (26%)] Loss: 0.77103 (0.9930) Data (t): 0.001 Batch (t): 0.993, 565.821/s LR: 0.000005 Logit Scale: 99.808 - V4 -2024-11-27,10:22:59 | INFO | Train Epoch: 0 [ 4762112/18327966 (26%)] Loss: 0.91254 (0.9922) Data (t): 0.001 Batch (t): 0.908, 564.540/s LR: 0.000005 Logit Scale: 99.809 - V4 -2024-11-27,10:24:30 | INFO | Train Epoch: 0 [ 4813312/18327966 (26%)] Loss: 0.71849 (0.9893) Data (t): 0.001 Batch (t): 0.909, 560.322/s LR: 0.000005 Logit Scale: 99.807 - V4 -2024-11-27,10:26:01 | INFO | Train Epoch: 0 [ 4864512/18327966 (27%)] Loss: 0.85478 (0.9879) Data (t): 0.001 Batch (t): 0.909, 563.253/s LR: 0.000005 Logit Scale: 99.808 - V4 -2024-11-27,10:27:32 | INFO | Train Epoch: 0 [ 4915712/18327966 (27%)] Loss: 0.82864 (0.9862) Data (t): 0.001 Batch (t): 0.909, 563.431/s LR: 0.000005 Logit Scale: 99.807 - V4 -2024-11-27,10:29:08 | INFO | Train Epoch: 0 [ 4966912/18327966 (27%)] Loss: 0.86206 (0.9850) Data (t): 0.001 Batch (t): 0.961, 564.005/s LR: 0.000005 Logit Scale: 99.807 - V4 -2024-11-27,10:30:42 | INFO | Train Epoch: 0 [ 5018112/18327966 (27%)] Loss: 0.86988 (0.9838) Data (t): 0.001 Batch (t): 0.936, 562.339/s LR: 0.000005 Logit Scale: 99.809 - V4 -2024-11-27,10:32:13 | INFO | Train Epoch: 0 [ 5069312/18327966 (28%)] Loss: 0.85414 (0.9825) Data (t): 0.001 Batch (t): 0.909, 560.200/s LR: 0.000005 Logit Scale: 99.808 - V4 -2024-11-27,10:33:44 | INFO | Train Epoch: 0 [ 5120512/18327966 (28%)] Loss: 1.1297 (0.9840) Data (t): 0.001 Batch (t): 0.910, 561.045/s LR: 0.000005 Logit Scale: 99.803 - V4 -2024-11-27,10:35:15 | INFO | Train Epoch: 0 [ 5171712/18327966 (28%)] Loss: 0.72208 (0.9814) Data (t): 0.001 Batch (t): 0.910, 566.079/s LR: 0.000005 Logit Scale: 99.803 - V4 -2024-11-27,10:36:48 | INFO | Train Epoch: 0 [ 5222912/18327966 (28%)] Loss: 0.84402 (0.9801) Data (t): 0.001 Batch (t): 0.931, 564.421/s LR: 0.000005 Logit Scale: 99.801 - V4 -2024-11-27,10:38:26 | INFO | Train Epoch: 0 [ 5274112/18327966 (29%)] Loss: 0.75714 (0.9779) Data (t): 0.001 Batch (t): 0.981, 558.035/s LR: 0.000005 Logit Scale: 99.798 - V4 -2024-11-27,10:39:57 | INFO | Train Epoch: 0 [ 5325312/18327966 (29%)] Loss: 0.78117 (0.9761) Data (t): 0.001 Batch (t): 0.909, 564.892/s LR: 0.000005 Logit Scale: 99.800 - V4 -2024-11-27,10:41:27 | INFO | Train Epoch: 0 [ 5376512/18327966 (29%)] Loss: 0.81179 (0.9745) Data (t): 0.001 Batch (t): 0.909, 560.796/s LR: 0.000005 Logit Scale: 99.802 - V4 -2024-11-27,10:42:58 | INFO | Train Epoch: 0 [ 5427712/18327966 (30%)] Loss: 0.72910 (0.9722) Data (t): 0.001 Batch (t): 0.909, 563.895/s LR: 0.000005 Logit Scale: 99.803 - V4 -2024-11-27,10:44:30 | INFO | Train Epoch: 0 [ 5478912/18327966 (30%)] Loss: 0.95870 (0.9721) Data (t): 0.001 Batch (t): 0.919, 562.400/s LR: 0.000005 Logit Scale: 99.802 - V4 -2024-11-27,10:46:10 | INFO | Train Epoch: 0 [ 5530112/18327966 (30%)] Loss: 0.83738 (0.9708) Data (t): 0.001 Batch (t): 0.993, 561.505/s LR: 0.000005 Logit Scale: 99.803 - V4 -2024-11-27,10:47:40 | INFO | Train Epoch: 0 [ 5581312/18327966 (30%)] Loss: 0.78787 (0.9692) Data (t): 0.001 Batch (t): 0.909, 561.738/s LR: 0.000005 Logit Scale: 99.802 - V4 -2024-11-27,10:49:11 | INFO | Train Epoch: 0 [ 5632512/18327966 (31%)] Loss: 0.93424 (0.9689) Data (t): 0.001 Batch (t): 0.910, 562.789/s LR: 0.000005 Logit Scale: 99.801 - V4 -2024-11-27,10:50:42 | INFO | Train Epoch: 0 [ 5683712/18327966 (31%)] Loss: 0.76552 (0.9671) Data (t): 0.001 Batch (t): 0.910, 563.721/s LR: 0.000005 Logit Scale: 99.801 - V4 -2024-11-27,10:52:13 | INFO | Train Epoch: 0 [ 5734912/18327966 (31%)] Loss: 0.84941 (0.9660) Data (t): 0.001 Batch (t): 0.909, 563.557/s LR: 0.000005 Logit Scale: 99.803 - V4 -2024-11-27,10:53:54 | INFO | Train Epoch: 0 [ 5786112/18327966 (32%)] Loss: 0.83641 (0.9649) Data (t): 0.001 Batch (t): 1.004, 564.609/s LR: 0.000005 Logit Scale: 99.803 - V4 -2024-11-27,10:55:25 | INFO | Train Epoch: 0 [ 5837312/18327966 (32%)] Loss: 0.80368 (0.9635) Data (t): 0.001 Batch (t): 0.910, 558.150/s LR: 0.000005 Logit Scale: 99.803 - V4 -2024-11-27,10:56:56 | INFO | Train Epoch: 0 [ 5888512/18327966 (32%)] Loss: 0.73009 (0.9615) Data (t): 0.001 Batch (t): 0.910, 562.682/s LR: 0.000005 Logit Scale: 99.802 - V4 -2024-11-27,10:58:27 | INFO | Train Epoch: 0 [ 5939712/18327966 (32%)] Loss: 0.85174 (0.9605) Data (t): 0.001 Batch (t): 0.909, 563.947/s LR: 0.000005 Logit Scale: 99.803 - V4 -2024-11-27,10:59:57 | INFO | Train Epoch: 0 [ 5990912/18327966 (33%)] Loss: 0.80854 (0.9592) Data (t): 0.001 Batch (t): 0.909, 564.445/s LR: 0.000005 Logit Scale: 99.805 - V4 -2024-11-27,11:01:37 | INFO | Train Epoch: 0 [ 6042112/18327966 (33%)] Loss: 0.82988 (0.9581) Data (t): 0.001 Batch (t): 0.992, 563.759/s LR: 0.000005 Logit Scale: 99.804 - V4 -2024-11-27,11:03:09 | INFO | Train Epoch: 0 [ 6093312/18327966 (33%)] Loss: 0.71241 (0.9561) Data (t): 0.001 Batch (t): 0.919, 562.773/s LR: 0.000005 Logit Scale: 99.802 - V4 -2024-11-27,11:04:39 | INFO | Train Epoch: 0 [ 6144512/18327966 (34%)] Loss: 0.83932 (0.9551) Data (t): 0.001 Batch (t): 0.909, 560.661/s LR: 0.000005 Logit Scale: 99.801 - V4 -2024-11-27,11:06:10 | INFO | Train Epoch: 0 [ 6195712/18327966 (34%)] Loss: 0.88988 (0.9546) Data (t): 0.001 Batch (t): 0.909, 561.502/s LR: 0.000005 Logit Scale: 99.803 - V4 -2024-11-27,11:07:41 | INFO | Train Epoch: 0 [ 6246912/18327966 (34%)] Loss: 0.80269 (0.9534) Data (t): 0.001 Batch (t): 0.909, 564.919/s LR: 0.000005 Logit Scale: 99.804 - V4 -2024-11-27,11:09:15 | INFO | Train Epoch: 0 [ 6298112/18327966 (34%)] Loss: 0.75097 (0.9517) Data (t): 0.001 Batch (t): 0.941, 565.764/s LR: 0.000005 Logit Scale: 99.810 - V4 -2024-11-27,11:10:52 | INFO | Train Epoch: 0 [ 6349312/18327966 (35%)] Loss: 0.75006 (0.9501) Data (t): 0.001 Batch (t): 0.970, 562.831/s LR: 0.000005 Logit Scale: 99.809 - V4 -2024-11-27,11:12:23 | INFO | Train Epoch: 0 [ 6400512/18327966 (35%)] Loss: 0.91592 (0.9498) Data (t): 0.001 Batch (t): 0.907, 564.463/s LR: 0.000005 Logit Scale: 99.811 - V4 -2024-11-27,11:13:54 | INFO | Train Epoch: 0 [ 6451712/18327966 (35%)] Loss: 0.73815 (0.9482) Data (t): 0.001 Batch (t): 0.908, 565.576/s LR: 0.000005 Logit Scale: 99.811 - V4 -2024-11-27,11:15:25 | INFO | Train Epoch: 0 [ 6502912/18327966 (35%)] Loss: 0.83940 (0.9473) Data (t): 0.001 Batch (t): 0.909, 564.712/s LR: 0.000005 Logit Scale: 99.811 - V4 -2024-11-27,11:16:58 | INFO | Train Epoch: 0 [ 6554112/18327966 (36%)] Loss: 0.84015 (0.9465) Data (t): 0.001 Batch (t): 0.931, 562.472/s LR: 0.000005 Logit Scale: 99.814 - V4 -2024-11-27,11:18:36 | INFO | Train Epoch: 0 [ 6605312/18327966 (36%)] Loss: 0.90939 (0.9462) Data (t): 0.001 Batch (t): 0.981, 561.144/s LR: 0.000005 Logit Scale: 99.813 - V4 -2024-11-27,11:20:07 | INFO | Train Epoch: 0 [ 6656512/18327966 (36%)] Loss: 0.81426 (0.9452) Data (t): 0.001 Batch (t): 0.909, 561.567/s LR: 0.000005 Logit Scale: 99.813 - V4 -2024-11-27,11:21:38 | INFO | Train Epoch: 0 [ 6707712/18327966 (37%)] Loss: 0.84071 (0.9444) Data (t): 0.001 Batch (t): 0.909, 561.280/s LR: 0.000005 Logit Scale: 99.813 - V4 -2024-11-27,11:23:09 | INFO | Train Epoch: 0 [ 6758912/18327966 (37%)] Loss: 0.75955 (0.9430) Data (t): 0.001 Batch (t): 0.908, 561.052/s LR: 0.000005 Logit Scale: 99.815 - V4 -2024-11-27,11:24:39 | INFO | Train Epoch: 0 [ 6810112/18327966 (37%)] Loss: 0.92531 (0.9429) Data (t): 0.001 Batch (t): 0.909, 561.712/s LR: 0.000005 Logit Scale: 99.819 - V4 -2024-11-27,11:26:19 | INFO | Train Epoch: 0 [ 6861312/18327966 (37%)] Loss: 0.90095 (0.9426) Data (t): 0.001 Batch (t): 0.991, 563.462/s LR: 0.000005 Logit Scale: 99.815 - V4 -2024-11-27,11:27:49 | INFO | Train Epoch: 0 [ 6912512/18327966 (38%)] Loss: 0.77518 (0.9414) Data (t): 0.001 Batch (t): 0.908, 562.722/s LR: 0.000005 Logit Scale: 99.818 - V4 -2024-11-27,11:29:20 | INFO | Train Epoch: 0 [ 6963712/18327966 (38%)] Loss: 0.85230 (0.9407) Data (t): 0.001 Batch (t): 0.907, 566.248/s LR: 0.000005 Logit Scale: 99.819 - V4 -2024-11-27,11:30:51 | INFO | Train Epoch: 0 [ 7014912/18327966 (38%)] Loss: 0.79769 (0.9397) Data (t): 0.001 Batch (t): 0.908, 563.383/s LR: 0.000005 Logit Scale: 99.821 - V4 -2024-11-27,11:32:22 | INFO | Train Epoch: 0 [ 7066112/18327966 (39%)] Loss: 0.86202 (0.9391) Data (t): 0.001 Batch (t): 0.908, 562.043/s LR: 0.000005 Logit Scale: 99.823 - V4 -2024-11-27,11:34:01 | INFO | Train Epoch: 0 [ 7117312/18327966 (39%)] Loss: 0.81504 (0.9382) Data (t): 0.001 Batch (t): 0.992, 566.217/s LR: 0.000005 Logit Scale: 99.823 - V4 -2024-11-27,11:35:32 | INFO | Train Epoch: 0 [ 7168512/18327966 (39%)] Loss: 0.85802 (0.9377) Data (t): 0.001 Batch (t): 0.908, 561.824/s LR: 0.000005 Logit Scale: 99.825 - V4 -2024-11-27,11:37:02 | INFO | Train Epoch: 0 [ 7219712/18327966 (39%)] Loss: 0.89429 (0.9373) Data (t): 0.001 Batch (t): 0.907, 562.672/s LR: 0.000005 Logit Scale: 99.825 - V4 -2024-11-27,11:38:33 | INFO | Train Epoch: 0 [ 7270912/18327966 (40%)] Loss: 0.82926 (0.9366) Data (t): 0.001 Batch (t): 0.909, 563.261/s LR: 0.000005 Logit Scale: 99.829 - V4 -2024-11-27,11:40:04 | INFO | Train Epoch: 0 [ 7322112/18327966 (40%)] Loss: 0.73369 (0.9352) Data (t): 0.001 Batch (t): 0.908, 563.426/s LR: 0.000005 Logit Scale: 99.832 - V4 -2024-11-27,11:41:42 | INFO | Train Epoch: 0 [ 7373312/18327966 (40%)] Loss: 0.90973 (0.9350) Data (t): 0.001 Batch (t): 0.974, 566.007/s LR: 0.000005 Logit Scale: 99.833 - V4 -2024-11-27,11:43:15 | INFO | Train Epoch: 0 [ 7424512/18327966 (41%)] Loss: 0.71168 (0.9335) Data (t): 0.001 Batch (t): 0.935, 559.464/s LR: 0.000005 Logit Scale: 99.837 - V4 -2024-11-27,11:44:46 | INFO | Train Epoch: 0 [ 7475712/18327966 (41%)] Loss: 0.73158 (0.9321) Data (t): 0.001 Batch (t): 0.906, 563.544/s LR: 0.000005 Logit Scale: 99.838 - V4 -2024-11-27,11:46:16 | INFO | Train Epoch: 0 [ 7526912/18327966 (41%)] Loss: 0.81123 (0.9313) Data (t): 0.001 Batch (t): 0.908, 566.332/s LR: 0.000004 Logit Scale: 99.838 - V4 -2024-11-27,11:47:47 | INFO | Train Epoch: 0 [ 7578112/18327966 (41%)] Loss: 0.80020 (0.9304) Data (t): 0.001 Batch (t): 0.908, 566.333/s LR: 0.000004 Logit Scale: 99.837 - V4 -2024-11-27,11:49:21 | INFO | Train Epoch: 0 [ 7629312/18327966 (42%)] Loss: 0.81728 (0.9297) Data (t): 0.001 Batch (t): 0.941, 565.691/s LR: 0.000004 Logit Scale: 99.842 - V4 -2024-11-27,11:50:58 | INFO | Train Epoch: 0 [ 7680512/18327966 (42%)] Loss: 0.80117 (0.9288) Data (t): 0.001 Batch (t): 0.968, 564.555/s LR: 0.000004 Logit Scale: 99.842 - V4 -2024-11-27,11:52:29 | INFO | Train Epoch: 0 [ 7731712/18327966 (42%)] Loss: 0.78605 (0.9279) Data (t): 0.001 Batch (t): 0.907, 564.736/s LR: 0.000004 Logit Scale: 99.842 - V4 -2024-11-27,11:54:00 | INFO | Train Epoch: 0 [ 7782912/18327966 (42%)] Loss: 0.90964 (0.9277) Data (t): 0.001 Batch (t): 0.907, 566.214/s LR: 0.000004 Logit Scale: 99.842 - V4 -2024-11-27,11:55:30 | INFO | Train Epoch: 0 [ 7834112/18327966 (43%)] Loss: 0.72903 (0.9265) Data (t): 0.001 Batch (t): 0.907, 562.881/s LR: 0.000004 Logit Scale: 99.844 - V4 -2024-11-27,11:57:02 | INFO | Train Epoch: 0 [ 7885312/18327966 (43%)] Loss: 0.83149 (0.9258) Data (t): 0.001 Batch (t): 0.918, 565.736/s LR: 0.000004 Logit Scale: 99.845 - V4 -2024-11-27,11:58:42 | INFO | Train Epoch: 0 [ 7936512/18327966 (43%)] Loss: 0.72302 (0.9245) Data (t): 0.001 Batch (t): 1.000, 564.707/s LR: 0.000004 Logit Scale: 99.848 - V4 -2024-11-27,12:00:13 | INFO | Train Epoch: 0 [ 7987712/18327966 (44%)] Loss: 0.77759 (0.9236) Data (t): 0.001 Batch (t): 0.907, 565.788/s LR: 0.000004 Logit Scale: 99.849 - V4 -2024-11-27,12:01:43 | INFO | Train Epoch: 0 [ 8038912/18327966 (44%)] Loss: 0.89829 (0.9234) Data (t): 0.001 Batch (t): 0.907, 562.426/s LR: 0.000004 Logit Scale: 99.850 - V4 -2024-11-27,12:03:14 | INFO | Train Epoch: 0 [ 8090112/18327966 (44%)] Loss: 0.73107 (0.9222) Data (t): 0.001 Batch (t): 0.907, 562.925/s LR: 0.000004 Logit Scale: 99.850 - V4 -2024-11-27,12:04:46 | INFO | Train Epoch: 0 [ 8141312/18327966 (44%)] Loss: 0.73542 (0.9211) Data (t): 0.001 Batch (t): 0.917, 564.056/s LR: 0.000004 Logit Scale: 99.854 - V4 -2024-11-27,12:06:25 | INFO | Train Epoch: 0 [ 8192512/18327966 (45%)] Loss: 0.77831 (0.9202) Data (t): 0.001 Batch (t): 0.989, 567.414/s LR: 0.000004 Logit Scale: 99.854 - V4 -2024-11-27,12:07:55 | INFO | Train Epoch: 0 [ 8243712/18327966 (45%)] Loss: 0.84672 (0.9197) Data (t): 0.001 Batch (t): 0.905, 563.968/s LR: 0.000004 Logit Scale: 99.855 - V4 -2024-11-27,12:09:26 | INFO | Train Epoch: 0 [ 8294912/18327966 (45%)] Loss: 0.78030 (0.9189) Data (t): 0.001 Batch (t): 0.906, 566.554/s LR: 0.000004 Logit Scale: 99.855 - V4 -2024-11-27,12:10:57 | INFO | Train Epoch: 0 [ 8346112/18327966 (46%)] Loss: 0.71152 (0.9176) Data (t): 0.001 Batch (t): 0.906, 566.721/s LR: 0.000004 Logit Scale: 99.855 - V4 -2024-11-27,12:12:27 | INFO | Train Epoch: 0 [ 8397312/18327966 (46%)] Loss: 0.80523 (0.9169) Data (t): 0.001 Batch (t): 0.907, 560.927/s LR: 0.000004 Logit Scale: 99.859 - V4 -2024-11-27,12:14:06 | INFO | Train Epoch: 0 [ 8448512/18327966 (46%)] Loss: 0.69879 (0.9156) Data (t): 0.001 Batch (t): 0.990, 565.653/s LR: 0.000004 Logit Scale: 99.863 - V4 -2024-11-27,12:15:38 | INFO | Train Epoch: 0 [ 8499712/18327966 (46%)] Loss: 0.77927 (0.9148) Data (t): 0.001 Batch (t): 0.915, 568.344/s LR: 0.000004 Logit Scale: 99.866 - V4 -2024-11-27,12:17:08 | INFO | Train Epoch: 0 [ 8550912/18327966 (47%)] Loss: 0.80141 (0.9141) Data (t): 0.001 Batch (t): 0.905, 566.460/s LR: 0.000004 Logit Scale: 99.868 - V4 -2024-11-27,12:18:39 | INFO | Train Epoch: 0 [ 8602112/18327966 (47%)] Loss: 0.88859 (0.9140) Data (t): 0.001 Batch (t): 0.906, 564.874/s LR: 0.000004 Logit Scale: 99.870 - V4 -2024-11-27,12:20:10 | INFO | Train Epoch: 0 [ 8653312/18327966 (47%)] Loss: 0.94404 (0.9141) Data (t): 0.001 Batch (t): 0.907, 565.450/s LR: 0.000004 Logit Scale: 99.874 - V4 -2024-11-27,12:21:45 | INFO | Train Epoch: 0 [ 8704512/18327966 (47%)] Loss: 0.73287 (0.9131) Data (t): 0.001 Batch (t): 0.951, 567.830/s LR: 0.000004 Logit Scale: 99.873 - V4 -2024-11-27,12:23:19 | INFO | Train Epoch: 0 [ 8755712/18327966 (48%)] Loss: 0.84168 (0.9127) Data (t): 0.001 Batch (t): 0.944, 565.481/s LR: 0.000004 Logit Scale: 99.881 - V4 -2024-11-27,12:24:50 | INFO | Train Epoch: 0 [ 8806912/18327966 (48%)] Loss: 0.77935 (0.9119) Data (t): 0.001 Batch (t): 0.906, 565.894/s LR: 0.000004 Logit Scale: 99.880 - V4 -2024-11-27,12:26:20 | INFO | Train Epoch: 0 [ 8858112/18327966 (48%)] Loss: 0.91980 (0.9119) Data (t): 0.001 Batch (t): 0.905, 567.735/s LR: 0.000004 Logit Scale: 99.884 - V4 -2024-11-27,12:27:51 | INFO | Train Epoch: 0 [ 8909312/18327966 (49%)] Loss: 0.76175 (0.9111) Data (t): 0.001 Batch (t): 0.905, 562.951/s LR: 0.000004 Logit Scale: 99.886 - V4 -2024-11-27,12:29:22 | INFO | Train Epoch: 0 [ 8960512/18327966 (49%)] Loss: 0.71469 (0.9100) Data (t): 0.001 Batch (t): 0.915, 564.722/s LR: 0.000004 Logit Scale: 99.890 - V4 -2024-11-27,12:31:01 | INFO | Train Epoch: 0 [ 9011712/18327966 (49%)] Loss: 0.69723 (0.9088) Data (t): 0.001 Batch (t): 0.990, 567.097/s LR: 0.000004 Logit Scale: 99.892 - V4 -2024-11-27,12:32:32 | INFO | Train Epoch: 0 [ 9062912/18327966 (49%)] Loss: 0.68791 (0.9075) Data (t): 0.001 Batch (t): 0.907, 564.429/s LR: 0.000004 Logit Scale: 99.897 - V4 -2024-11-27,12:34:02 | INFO | Train Epoch: 0 [ 9114112/18327966 (50%)] Loss: 0.88317 (0.9074) Data (t): 0.001 Batch (t): 0.905, 567.139/s LR: 0.000004 Logit Scale: 99.895 - V4 -2024-11-27,12:35:33 | INFO | Train Epoch: 0 [ 9165312/18327966 (50%)] Loss: 0.79272 (0.9068) Data (t): 0.001 Batch (t): 0.905, 565.706/s LR: 0.000004 Logit Scale: 99.897 - V4 -2024-11-27,12:37:05 | INFO | Train Epoch: 0 [ 9216512/18327966 (50%)] Loss: 0.74769 (0.9059) Data (t): 0.001 Batch (t): 0.917, 563.580/s LR: 0.000004 Logit Scale: 99.898 - V4 -2024-11-27,12:38:44 | INFO | Train Epoch: 0 [ 9267712/18327966 (51%)] Loss: 0.78879 (0.9052) Data (t): 0.001 Batch (t): 0.990, 566.330/s LR: 0.000004 Logit Scale: 99.902 - V4 -2024-11-27,12:40:14 | INFO | Train Epoch: 0 [ 9318912/18327966 (51%)] Loss: 0.74357 (0.9044) Data (t): 0.001 Batch (t): 0.906, 562.787/s LR: 0.000004 Logit Scale: 99.903 - V4 -2024-11-27,12:41:45 | INFO | Train Epoch: 0 [ 9370112/18327966 (51%)] Loss: 0.84939 (0.9041) Data (t): 0.001 Batch (t): 0.906, 561.285/s LR: 0.000004 Logit Scale: 99.907 - V4 -2024-11-27,12:43:15 | INFO | Train Epoch: 0 [ 9421312/18327966 (51%)] Loss: 0.72492 (0.9031) Data (t): 0.001 Batch (t): 0.905, 563.608/s LR: 0.000004 Logit Scale: 99.913 - V4 -2024-11-27,12:44:46 | INFO | Train Epoch: 0 [ 9472512/18327966 (52%)] Loss: 0.65671 (0.9018) Data (t): 0.001 Batch (t): 0.907, 565.290/s LR: 0.000004 Logit Scale: 99.912 - V4 -2024-11-27,12:46:26 | INFO | Train Epoch: 0 [ 9523712/18327966 (52%)] Loss: 0.77024 (0.9011) Data (t): 0.001 Batch (t): 1.004, 564.805/s LR: 0.000004 Logit Scale: 99.915 - V4 -2024-11-27,12:47:57 | INFO | Train Epoch: 0 [ 9574912/18327966 (52%)] Loss: 0.69159 (0.8999) Data (t): 0.001 Batch (t): 0.907, 565.054/s LR: 0.000004 Logit Scale: 99.916 - V4 -2024-11-27,12:49:28 | INFO | Train Epoch: 0 [ 9626112/18327966 (53%)] Loss: 0.74195 (0.8991) Data (t): 0.001 Batch (t): 0.907, 563.179/s LR: 0.000004 Logit Scale: 99.922 - V4 -2024-11-27,12:50:59 | INFO | Train Epoch: 0 [ 9677312/18327966 (53%)] Loss: 0.77758 (0.8985) Data (t): 0.001 Batch (t): 0.907, 565.947/s LR: 0.000004 Logit Scale: 99.926 - V4 -2024-11-27,12:52:29 | INFO | Train Epoch: 0 [ 9728512/18327966 (53%)] Loss: 0.77676 (0.8978) Data (t): 0.001 Batch (t): 0.907, 563.050/s LR: 0.000004 Logit Scale: 99.933 - V4 -2024-11-27,12:54:07 | INFO | Train Epoch: 0 [ 9779712/18327966 (53%)] Loss: 0.69017 (0.8967) Data (t): 0.001 Batch (t): 0.975, 566.296/s LR: 0.000004 Logit Scale: 99.935 - V4 -2024-11-27,12:55:40 | INFO | Train Epoch: 0 [ 9830912/18327966 (54%)] Loss: 0.79983 (0.8962) Data (t): 0.001 Batch (t): 0.934, 564.184/s LR: 0.000004 Logit Scale: 99.938 - V4 -2024-11-27,12:57:11 | INFO | Train Epoch: 0 [ 9882112/18327966 (54%)] Loss: 0.82218 (0.8959) Data (t): 0.001 Batch (t): 0.906, 564.676/s LR: 0.000004 Logit Scale: 99.941 - V4 -2024-11-27,12:58:41 | INFO | Train Epoch: 0 [ 9933312/18327966 (54%)] Loss: 0.75747 (0.8952) Data (t): 0.001 Batch (t): 0.906, 562.785/s LR: 0.000004 Logit Scale: 99.948 - V4 -2024-11-27,13:00:12 | INFO | Train Epoch: 0 [ 9984512/18327966 (54%)] Loss: 0.81563 (0.8947) Data (t): 0.001 Batch (t): 0.905, 563.945/s LR: 0.000004 Logit Scale: 99.949 - V4 -2024-11-27,13:01:47 | INFO | Train Epoch: 0 [10035712/18327966 (55%)] Loss: 0.89085 (0.8947) Data (t): 0.001 Batch (t): 0.951, 250.416/s LR: 0.000004 Logit Scale: 99.949 - V4 -2024-11-27,13:03:23 | INFO | Train Epoch: 0 [10086912/18327966 (55%)] Loss: 0.80501 (0.8943) Data (t): 0.001 Batch (t): 0.956, 563.236/s LR: 0.000004 Logit Scale: 99.957 - V4 -2024-11-27,13:04:53 | INFO | Train Epoch: 0 [10138112/18327966 (55%)] Loss: 0.73318 (0.8935) Data (t): 0.001 Batch (t): 0.907, 562.981/s LR: 0.000004 Logit Scale: 99.963 - V4 -2024-11-27,13:06:24 | INFO | Train Epoch: 0 [10189312/18327966 (56%)] Loss: 0.88370 (0.8934) Data (t): 0.001 Batch (t): 0.907, 565.676/s LR: 0.000004 Logit Scale: 99.966 - V4 -2024-11-27,13:07:55 | INFO | Train Epoch: 0 [10240512/18327966 (56%)] Loss: 0.72707 (0.8926) Data (t): 0.001 Batch (t): 0.908, 562.399/s LR: 0.000004 Logit Scale: 99.968 - V4 -2024-11-27,13:09:27 | INFO | Train Epoch: 0 [10291712/18327966 (56%)] Loss: 0.71263 (0.8917) Data (t): 0.001 Batch (t): 0.919, 564.957/s LR: 0.000004 Logit Scale: 99.970 - V4 -2024-11-27,13:11:05 | INFO | Train Epoch: 0 [10342912/18327966 (56%)] Loss: 0.76023 (0.8911) Data (t): 0.001 Batch (t): 0.981, 563.785/s LR: 0.000004 Logit Scale: 99.977 - V4 -2024-11-27,13:12:36 | INFO | Train Epoch: 0 [10394112/18327966 (57%)] Loss: 0.78214 (0.8905) Data (t): 0.001 Batch (t): 0.908, 563.750/s LR: 0.000004 Logit Scale: 99.979 - V4 -2024-11-27,13:14:06 | INFO | Train Epoch: 0 [10445312/18327966 (57%)] Loss: 0.65490 (0.8894) Data (t): 0.001 Batch (t): 0.907, 562.401/s LR: 0.000004 Logit Scale: 99.980 - V4 -2024-11-27,13:15:37 | INFO | Train Epoch: 0 [10496512/18327966 (57%)] Loss: 0.75146 (0.8887) Data (t): 0.001 Batch (t): 0.907, 564.575/s LR: 0.000004 Logit Scale: 99.985 - V4 -2024-11-27,13:17:09 | INFO | Train Epoch: 0 [10547712/18327966 (58%)] Loss: 0.73735 (0.8880) Data (t): 0.001 Batch (t): 0.918, 565.289/s LR: 0.000004 Logit Scale: 99.992 - V4 -2024-11-27,13:18:48 | INFO | Train Epoch: 0 [10598912/18327966 (58%)] Loss: 0.67599 (0.8869) Data (t): 0.001 Batch (t): 0.993, 563.699/s LR: 0.000004 Logit Scale: 99.996 - V4 -2024-11-27,13:20:19 | INFO | Train Epoch: 0 [10650112/18327966 (58%)] Loss: 0.83778 (0.8867) Data (t): 0.001 Batch (t): 0.905, 564.082/s LR: 0.000004 Logit Scale: 99.999 - V4 -2024-11-27,13:21:50 | INFO | Train Epoch: 0 [10701312/18327966 (58%)] Loss: 0.66301 (0.8856) Data (t): 0.001 Batch (t): 0.907, 563.434/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,13:23:20 | INFO | Train Epoch: 0 [10752512/18327966 (59%)] Loss: 0.76014 (0.8851) Data (t): 0.001 Batch (t): 0.907, 564.824/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,13:24:51 | INFO | Train Epoch: 0 [10803712/18327966 (59%)] Loss: 0.81941 (0.8847) Data (t): 0.001 Batch (t): 0.906, 565.699/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,13:26:31 | INFO | Train Epoch: 0 [10854912/18327966 (59%)] Loss: 0.82825 (0.8845) Data (t): 0.001 Batch (t): 1.007, 565.012/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,13:28:02 | INFO | Train Epoch: 0 [10906112/18327966 (60%)] Loss: 0.80176 (0.8841) Data (t): 0.001 Batch (t): 0.905, 561.770/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,13:29:33 | INFO | Train Epoch: 0 [10957312/18327966 (60%)] Loss: 0.81927 (0.8838) Data (t): 0.001 Batch (t): 0.908, 566.957/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,13:31:03 | INFO | Train Epoch: 0 [11008512/18327966 (60%)] Loss: 0.71505 (0.8830) Data (t): 0.001 Batch (t): 0.907, 563.271/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,13:32:34 | INFO | Train Epoch: 0 [11059712/18327966 (60%)] Loss: 0.81200 (0.8827) Data (t): 0.001 Batch (t): 0.908, 564.971/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,13:34:11 | INFO | Train Epoch: 0 [11110912/18327966 (61%)] Loss: 0.65029 (0.8816) Data (t): 0.001 Batch (t): 0.965, 563.777/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,13:35:46 | INFO | Train Epoch: 0 [11162112/18327966 (61%)] Loss: 0.76168 (0.8811) Data (t): 0.001 Batch (t): 0.948, 565.447/s LR: 0.000004 Logit Scale: 99.999 - V4 -2024-11-27,13:37:16 | INFO | Train Epoch: 0 [11213312/18327966 (61%)] Loss: 0.69318 (0.8802) Data (t): 0.001 Batch (t): 0.907, 565.187/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,13:38:47 | INFO | Train Epoch: 0 [11264512/18327966 (61%)] Loss: 0.82342 (0.8800) Data (t): 0.001 Batch (t): 0.907, 565.414/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,13:40:18 | INFO | Train Epoch: 0 [11315712/18327966 (62%)] Loss: 0.84010 (0.8798) Data (t): 0.001 Batch (t): 0.907, 564.220/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,13:41:53 | INFO | Train Epoch: 0 [11366912/18327966 (62%)] Loss: 0.76597 (0.8793) Data (t): 0.001 Batch (t): 0.953, 249.928/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,13:43:29 | INFO | Train Epoch: 0 [11418112/18327966 (62%)] Loss: 0.76052 (0.8787) Data (t): 0.001 Batch (t): 0.959, 562.202/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,13:45:00 | INFO | Train Epoch: 0 [11469312/18327966 (63%)] Loss: 0.76107 (0.8782) Data (t): 0.001 Batch (t): 0.907, 564.240/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,13:46:30 | INFO | Train Epoch: 0 [11520512/18327966 (63%)] Loss: 0.76806 (0.8777) Data (t): 0.001 Batch (t): 0.907, 567.565/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,13:48:01 | INFO | Train Epoch: 0 [11571712/18327966 (63%)] Loss: 0.77586 (0.8773) Data (t): 0.001 Batch (t): 0.907, 564.032/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,13:49:33 | INFO | Train Epoch: 0 [11622912/18327966 (63%)] Loss: 0.79663 (0.8769) Data (t): 0.001 Batch (t): 0.919, 565.344/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,13:51:12 | INFO | Train Epoch: 0 [11674112/18327966 (64%)] Loss: 0.73784 (0.8763) Data (t): 0.001 Batch (t): 0.995, 560.480/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,13:52:43 | INFO | Train Epoch: 0 [11725312/18327966 (64%)] Loss: 0.71622 (0.8756) Data (t): 0.001 Batch (t): 0.908, 563.653/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,13:54:14 | INFO | Train Epoch: 0 [11776512/18327966 (64%)] Loss: 0.82424 (0.8754) Data (t): 0.001 Batch (t): 0.908, 564.106/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,13:55:45 | INFO | Train Epoch: 0 [11827712/18327966 (65%)] Loss: 0.69325 (0.8746) Data (t): 0.001 Batch (t): 0.908, 565.409/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,13:57:17 | INFO | Train Epoch: 0 [11878912/18327966 (65%)] Loss: 0.78789 (0.8742) Data (t): 0.001 Batch (t): 0.920, 566.096/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,13:58:57 | INFO | Train Epoch: 0 [11930112/18327966 (65%)] Loss: 0.67980 (0.8734) Data (t): 0.001 Batch (t): 1.005, 563.651/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,14:00:28 | INFO | Train Epoch: 0 [11981312/18327966 (65%)] Loss: 0.72717 (0.8728) Data (t): 0.001 Batch (t): 0.909, 561.227/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,14:01:59 | INFO | Train Epoch: 0 [12032512/18327966 (66%)] Loss: 0.78624 (0.8724) Data (t): 0.001 Batch (t): 0.908, 565.945/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,14:03:30 | INFO | Train Epoch: 0 [12083712/18327966 (66%)] Loss: 0.67912 (0.8716) Data (t): 0.001 Batch (t): 0.908, 563.439/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,14:05:00 | INFO | Train Epoch: 0 [12134912/18327966 (66%)] Loss: 0.73509 (0.8710) Data (t): 0.001 Batch (t): 0.908, 563.684/s LR: 0.000004 Logit Scale: 99.999 - V4 -2024-11-27,14:06:38 | INFO | Train Epoch: 0 [12186112/18327966 (66%)] Loss: 0.64333 (0.8701) Data (t): 0.001 Batch (t): 0.979, 563.572/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,14:08:12 | INFO | Train Epoch: 0 [12237312/18327966 (67%)] Loss: 0.75562 (0.8696) Data (t): 0.001 Batch (t): 0.937, 565.140/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,14:09:43 | INFO | Train Epoch: 0 [12288512/18327966 (67%)] Loss: 0.71567 (0.8690) Data (t): 0.001 Batch (t): 0.909, 567.709/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,14:11:14 | INFO | Train Epoch: 0 [12339712/18327966 (67%)] Loss: 0.74657 (0.8685) Data (t): 0.001 Batch (t): 0.909, 560.529/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,14:12:45 | INFO | Train Epoch: 0 [12390912/18327966 (68%)] Loss: 0.75179 (0.8680) Data (t): 0.001 Batch (t): 0.909, 566.140/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,14:14:20 | INFO | Train Epoch: 0 [12442112/18327966 (68%)] Loss: 0.84869 (0.8679) Data (t): 0.001 Batch (t): 0.956, 567.066/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,14:15:55 | INFO | Train Epoch: 0 [12493312/18327966 (68%)] Loss: 0.82701 (0.8677) Data (t): 0.001 Batch (t): 0.948, 563.001/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,14:17:26 | INFO | Train Epoch: 0 [12544512/18327966 (68%)] Loss: 0.75135 (0.8673) Data (t): 0.001 Batch (t): 0.908, 560.189/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,14:18:57 | INFO | Train Epoch: 0 [12595712/18327966 (69%)] Loss: 0.78579 (0.8669) Data (t): 0.001 Batch (t): 0.908, 563.959/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,14:20:28 | INFO | Train Epoch: 0 [12646912/18327966 (69%)] Loss: 0.79889 (0.8667) Data (t): 0.001 Batch (t): 0.909, 562.385/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,14:22:00 | INFO | Train Epoch: 0 [12698112/18327966 (69%)] Loss: 0.70984 (0.8660) Data (t): 0.001 Batch (t): 0.924, 544.814/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,14:23:40 | INFO | Train Epoch: 0 [12749312/18327966 (70%)] Loss: 0.73552 (0.8655) Data (t): 0.001 Batch (t): 0.996, 561.391/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,14:25:10 | INFO | Train Epoch: 0 [12800512/18327966 (70%)] Loss: 0.68945 (0.8648) Data (t): 0.001 Batch (t): 0.908, 561.193/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,14:26:41 | INFO | Train Epoch: 0 [12851712/18327966 (70%)] Loss: 0.77264 (0.8644) Data (t): 0.001 Batch (t): 0.908, 563.839/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,14:28:12 | INFO | Train Epoch: 0 [12902912/18327966 (70%)] Loss: 0.71833 (0.8639) Data (t): 0.001 Batch (t): 0.909, 559.950/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,14:29:44 | INFO | Train Epoch: 0 [12954112/18327966 (71%)] Loss: 0.79387 (0.8636) Data (t): 0.001 Batch (t): 0.920, 565.937/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,14:31:24 | INFO | Train Epoch: 0 [13005312/18327966 (71%)] Loss: 0.74577 (0.8631) Data (t): 0.001 Batch (t): 0.996, 565.700/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,14:32:55 | INFO | Train Epoch: 0 [13056512/18327966 (71%)] Loss: 0.74461 (0.8627) Data (t): 0.001 Batch (t): 0.908, 566.350/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,14:34:25 | INFO | Train Epoch: 0 [13107712/18327966 (72%)] Loss: 0.87151 (0.8627) Data (t): 0.001 Batch (t): 0.908, 562.005/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,14:35:56 | INFO | Train Epoch: 0 [13158912/18327966 (72%)] Loss: 0.65550 (0.8619) Data (t): 0.001 Batch (t): 0.908, 560.537/s LR: 0.000004 Logit Scale: 99.999 - V4 -2024-11-27,14:37:28 | INFO | Train Epoch: 0 [13210112/18327966 (72%)] Loss: 0.74779 (0.8614) Data (t): 0.001 Batch (t): 0.921, 561.552/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,14:39:06 | INFO | Train Epoch: 0 [13261312/18327966 (72%)] Loss: 0.62060 (0.8605) Data (t): 0.001 Batch (t): 0.980, 565.404/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,14:40:39 | INFO | Train Epoch: 0 [13312512/18327966 (73%)] Loss: 0.70347 (0.8599) Data (t): 0.001 Batch (t): 0.927, 562.718/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,14:42:10 | INFO | Train Epoch: 0 [13363712/18327966 (73%)] Loss: 0.69346 (0.8593) Data (t): 0.001 Batch (t): 0.909, 565.029/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,14:43:41 | INFO | Train Epoch: 0 [13414912/18327966 (73%)] Loss: 0.66711 (0.8586) Data (t): 0.001 Batch (t): 0.908, 563.280/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,14:45:12 | INFO | Train Epoch: 0 [13466112/18327966 (73%)] Loss: 0.71268 (0.8580) Data (t): 0.001 Batch (t): 0.908, 564.921/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,14:46:49 | INFO | Train Epoch: 0 [13517312/18327966 (74%)] Loss: 0.70971 (0.8574) Data (t): 0.001 Batch (t): 0.977, 566.114/s LR: 0.000004 Logit Scale: 100.000 - V4 -2024-11-27,14:48:24 | INFO | Train Epoch: 0 [13568512/18327966 (74%)] Loss: 0.77400 (0.8571) Data (t): 0.001 Batch (t): 0.949, 566.144/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,14:49:55 | INFO | Train Epoch: 0 [13619712/18327966 (74%)] Loss: 0.61349 (0.8562) Data (t): 0.001 Batch (t): 0.909, 563.773/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,14:51:26 | INFO | Train Epoch: 0 [13670912/18327966 (75%)] Loss: 0.73672 (0.8558) Data (t): 0.001 Batch (t): 0.909, 566.021/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,14:52:57 | INFO | Train Epoch: 0 [13722112/18327966 (75%)] Loss: 0.69560 (0.8552) Data (t): 0.001 Batch (t): 0.909, 564.582/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,14:54:31 | INFO | Train Epoch: 0 [13773312/18327966 (75%)] Loss: 0.62626 (0.8543) Data (t): 0.001 Batch (t): 0.945, 566.258/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,14:56:09 | INFO | Train Epoch: 0 [13824512/18327966 (75%)] Loss: 0.76814 (0.8540) Data (t): 0.001 Batch (t): 0.973, 563.978/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,14:57:39 | INFO | Train Epoch: 0 [13875712/18327966 (76%)] Loss: 0.72688 (0.8535) Data (t): 0.001 Batch (t): 0.908, 562.964/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,14:59:10 | INFO | Train Epoch: 0 [13926912/18327966 (76%)] Loss: 0.67735 (0.8529) Data (t): 0.001 Batch (t): 0.908, 565.753/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,15:00:41 | INFO | Train Epoch: 0 [13978112/18327966 (76%)] Loss: 0.74237 (0.8525) Data (t): 0.001 Batch (t): 0.908, 565.418/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,15:02:13 | INFO | Train Epoch: 0 [14029312/18327966 (77%)] Loss: 0.85835 (0.8525) Data (t): 0.001 Batch (t): 0.919, 563.593/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,15:03:53 | INFO | Train Epoch: 0 [14080512/18327966 (77%)] Loss: 0.70917 (0.8520) Data (t): 0.001 Batch (t): 0.996, 564.908/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,15:05:23 | INFO | Train Epoch: 0 [14131712/18327966 (77%)] Loss: 0.77782 (0.8517) Data (t): 0.001 Batch (t): 0.907, 564.231/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,15:06:54 | INFO | Train Epoch: 0 [14182912/18327966 (77%)] Loss: 0.71785 (0.8512) Data (t): 0.001 Batch (t): 0.907, 564.712/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,15:08:25 | INFO | Train Epoch: 0 [14234112/18327966 (78%)] Loss: 0.61752 (0.8504) Data (t): 0.001 Batch (t): 0.907, 568.065/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,15:09:56 | INFO | Train Epoch: 0 [14285312/18327966 (78%)] Loss: 0.73058 (0.8500) Data (t): 0.001 Batch (t): 0.917, 564.972/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,15:11:36 | INFO | Train Epoch: 0 [14336512/18327966 (78%)] Loss: 0.77938 (0.8497) Data (t): 0.001 Batch (t): 0.996, 565.464/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,15:13:07 | INFO | Train Epoch: 0 [14387712/18327966 (79%)] Loss: 0.78367 (0.8495) Data (t): 0.001 Batch (t): 0.907, 563.202/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,15:14:37 | INFO | Train Epoch: 0 [14438912/18327966 (79%)] Loss: 0.66703 (0.8488) Data (t): 0.001 Batch (t): 0.906, 564.447/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,15:16:08 | INFO | Train Epoch: 0 [14490112/18327966 (79%)] Loss: 0.67745 (0.8482) Data (t): 0.001 Batch (t): 0.907, 562.727/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,15:17:39 | INFO | Train Epoch: 0 [14541312/18327966 (79%)] Loss: 0.74453 (0.8479) Data (t): 0.001 Batch (t): 0.906, 563.617/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,15:19:17 | INFO | Train Epoch: 0 [14592512/18327966 (80%)] Loss: 0.82251 (0.8478) Data (t): 0.001 Batch (t): 0.979, 567.225/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,15:20:50 | INFO | Train Epoch: 0 [14643712/18327966 (80%)] Loss: 0.73617 (0.8474) Data (t): 0.001 Batch (t): 0.935, 564.225/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,15:22:21 | INFO | Train Epoch: 0 [14694912/18327966 (80%)] Loss: 0.76150 (0.8471) Data (t): 0.001 Batch (t): 0.906, 564.905/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,15:23:51 | INFO | Train Epoch: 0 [14746112/18327966 (80%)] Loss: 0.73078 (0.8467) Data (t): 0.001 Batch (t): 0.906, 564.150/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,15:25:22 | INFO | Train Epoch: 0 [14797312/18327966 (81%)] Loss: 0.75824 (0.8464) Data (t): 0.001 Batch (t): 0.906, 566.142/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,15:26:56 | INFO | Train Epoch: 0 [14848512/18327966 (81%)] Loss: 0.78597 (0.8462) Data (t): 0.001 Batch (t): 0.941, 565.824/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,15:28:32 | INFO | Train Epoch: 0 [14899712/18327966 (81%)] Loss: 0.61844 (0.8454) Data (t): 0.001 Batch (t): 0.959, 567.047/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,15:30:02 | INFO | Train Epoch: 0 [14950912/18327966 (82%)] Loss: 0.66893 (0.8448) Data (t): 0.001 Batch (t): 0.905, 564.701/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,15:31:33 | INFO | Train Epoch: 0 [15002112/18327966 (82%)] Loss: 0.73576 (0.8444) Data (t): 0.001 Batch (t): 0.906, 563.587/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,15:33:04 | INFO | Train Epoch: 0 [15053312/18327966 (82%)] Loss: 0.76006 (0.8442) Data (t): 0.001 Batch (t): 0.906, 561.226/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,15:34:37 | INFO | Train Epoch: 0 [15104512/18327966 (82%)] Loss: 0.74173 (0.8438) Data (t): 0.001 Batch (t): 0.931, 564.422/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,15:36:13 | INFO | Train Epoch: 0 [15155712/18327966 (83%)] Loss: 0.61322 (0.8430) Data (t): 0.001 Batch (t): 0.960, 565.514/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,15:37:43 | INFO | Train Epoch: 0 [15206912/18327966 (83%)] Loss: 0.58623 (0.8422) Data (t): 0.001 Batch (t): 0.906, 565.653/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,15:39:14 | INFO | Train Epoch: 0 [15258112/18327966 (83%)] Loss: 0.58312 (0.8413) Data (t): 0.001 Batch (t): 0.908, 563.957/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,15:40:45 | INFO | Train Epoch: 0 [15309312/18327966 (84%)] Loss: 0.70502 (0.8408) Data (t): 0.001 Batch (t): 0.906, 564.133/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,15:42:16 | INFO | Train Epoch: 0 [15360512/18327966 (84%)] Loss: 0.69962 (0.8404) Data (t): 0.001 Batch (t): 0.918, 567.080/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,15:43:56 | INFO | Train Epoch: 0 [15411712/18327966 (84%)] Loss: 0.73330 (0.8400) Data (t): 0.001 Batch (t): 0.995, 567.763/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,15:45:27 | INFO | Train Epoch: 0 [15462912/18327966 (84%)] Loss: 0.70898 (0.8396) Data (t): 0.001 Batch (t): 0.907, 567.683/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,15:46:57 | INFO | Train Epoch: 0 [15514112/18327966 (85%)] Loss: 0.67928 (0.8391) Data (t): 0.001 Batch (t): 0.907, 565.628/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,15:48:28 | INFO | Train Epoch: 0 [15565312/18327966 (85%)] Loss: 0.73855 (0.8387) Data (t): 0.001 Batch (t): 0.906, 563.744/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,15:50:00 | INFO | Train Epoch: 0 [15616512/18327966 (85%)] Loss: 0.72135 (0.8383) Data (t): 0.001 Batch (t): 0.918, 562.907/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,15:51:38 | INFO | Train Epoch: 0 [15667712/18327966 (85%)] Loss: 0.83191 (0.8383) Data (t): 0.001 Batch (t): 0.984, 565.696/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,15:53:10 | INFO | Train Epoch: 0 [15718912/18327966 (86%)] Loss: 0.67065 (0.8378) Data (t): 0.001 Batch (t): 0.919, 565.858/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,15:54:41 | INFO | Train Epoch: 0 [15770112/18327966 (86%)] Loss: 0.77696 (0.8376) Data (t): 0.001 Batch (t): 0.907, 564.669/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,15:56:11 | INFO | Train Epoch: 0 [15821312/18327966 (86%)] Loss: 0.78755 (0.8374) Data (t): 0.001 Batch (t): 0.908, 564.332/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,15:57:42 | INFO | Train Epoch: 0 [15872512/18327966 (87%)] Loss: 0.81282 (0.8373) Data (t): 0.001 Batch (t): 0.907, 565.896/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,15:59:18 | INFO | Train Epoch: 0 [15923712/18327966 (87%)] Loss: 0.67065 (0.8368) Data (t): 0.001 Batch (t): 0.957, 564.228/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,16:00:53 | INFO | Train Epoch: 0 [15974912/18327966 (87%)] Loss: 0.67965 (0.8363) Data (t): 0.001 Batch (t): 0.948, 562.925/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,16:02:23 | INFO | Train Epoch: 0 [16026112/18327966 (87%)] Loss: 0.70051 (0.8359) Data (t): 0.001 Batch (t): 0.907, 560.869/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,16:03:54 | INFO | Train Epoch: 0 [16077312/18327966 (88%)] Loss: 0.75999 (0.8356) Data (t): 0.001 Batch (t): 0.908, 563.497/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,16:05:25 | INFO | Train Epoch: 0 [16128512/18327966 (88%)] Loss: 0.69347 (0.8352) Data (t): 0.001 Batch (t): 0.907, 564.462/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,16:07:00 | INFO | Train Epoch: 0 [16179712/18327966 (88%)] Loss: 0.65132 (0.8346) Data (t): 0.001 Batch (t): 0.956, 565.718/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,16:08:37 | INFO | Train Epoch: 0 [16230912/18327966 (89%)] Loss: 0.63339 (0.8340) Data (t): 0.001 Batch (t): 0.962, 566.060/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,16:10:07 | INFO | Train Epoch: 0 [16282112/18327966 (89%)] Loss: 0.77048 (0.8338) Data (t): 0.001 Batch (t): 0.906, 565.526/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,16:11:38 | INFO | Train Epoch: 0 [16333312/18327966 (89%)] Loss: 0.74475 (0.8335) Data (t): 0.001 Batch (t): 0.907, 562.724/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,16:13:09 | INFO | Train Epoch: 0 [16384512/18327966 (89%)] Loss: 0.71864 (0.8331) Data (t): 0.001 Batch (t): 0.906, 564.897/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,16:14:43 | INFO | Train Epoch: 0 [16435712/18327966 (90%)] Loss: 0.75054 (0.8329) Data (t): 0.001 Batch (t): 0.944, 238.024/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,16:16:20 | INFO | Train Epoch: 0 [16486912/18327966 (90%)] Loss: 0.80317 (0.8328) Data (t): 0.001 Batch (t): 0.974, 567.245/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,16:17:51 | INFO | Train Epoch: 0 [16538112/18327966 (90%)] Loss: 0.70579 (0.8324) Data (t): 0.001 Batch (t): 0.906, 565.647/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,16:19:22 | INFO | Train Epoch: 0 [16589312/18327966 (91%)] Loss: 0.73270 (0.8321) Data (t): 0.001 Batch (t): 0.907, 561.160/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,16:20:52 | INFO | Train Epoch: 0 [16640512/18327966 (91%)] Loss: 0.70431 (0.8317) Data (t): 0.001 Batch (t): 0.905, 565.171/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,16:22:24 | INFO | Train Epoch: 0 [16691712/18327966 (91%)] Loss: 0.68303 (0.8312) Data (t): 0.001 Batch (t): 0.918, 566.605/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,16:24:04 | INFO | Train Epoch: 0 [16742912/18327966 (91%)] Loss: 0.70697 (0.8309) Data (t): 0.001 Batch (t): 0.998, 567.751/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,16:25:34 | INFO | Train Epoch: 0 [16794112/18327966 (92%)] Loss: 0.77079 (0.8307) Data (t): 0.001 Batch (t): 0.906, 563.955/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,16:27:05 | INFO | Train Epoch: 0 [16845312/18327966 (92%)] Loss: 0.78652 (0.8306) Data (t): 0.001 Batch (t): 0.906, 563.209/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,16:28:35 | INFO | Train Epoch: 0 [16896512/18327966 (92%)] Loss: 0.71046 (0.8302) Data (t): 0.001 Batch (t): 0.906, 564.602/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,16:30:07 | INFO | Train Epoch: 0 [16947712/18327966 (92%)] Loss: 0.61131 (0.8295) Data (t): 0.001 Batch (t): 0.918, 563.434/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,16:31:44 | INFO | Train Epoch: 0 [16998912/18327966 (93%)] Loss: 0.75447 (0.8293) Data (t): 0.001 Batch (t): 0.966, 565.680/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,16:33:17 | INFO | Train Epoch: 0 [17050112/18327966 (93%)] Loss: 0.62181 (0.8287) Data (t): 0.001 Batch (t): 0.935, 568.190/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,16:34:48 | INFO | Train Epoch: 0 [17101312/18327966 (93%)] Loss: 0.73505 (0.8284) Data (t): 0.001 Batch (t): 0.906, 566.720/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,16:36:18 | INFO | Train Epoch: 0 [17152512/18327966 (94%)] Loss: 0.56649 (0.8276) Data (t): 0.001 Batch (t): 0.905, 566.860/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,16:37:49 | INFO | Train Epoch: 0 [17203712/18327966 (94%)] Loss: 0.73337 (0.8273) Data (t): 0.001 Batch (t): 0.906, 566.918/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,16:39:26 | INFO | Train Epoch: 0 [17254912/18327966 (94%)] Loss: 0.64671 (0.8268) Data (t): 0.001 Batch (t): 0.967, 564.009/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,16:41:00 | INFO | Train Epoch: 0 [17306112/18327966 (94%)] Loss: 0.72252 (0.8265) Data (t): 0.001 Batch (t): 0.948, 563.669/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,16:42:31 | INFO | Train Epoch: 0 [17357312/18327966 (95%)] Loss: 0.63362 (0.8259) Data (t): 0.001 Batch (t): 0.905, 566.263/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,16:44:01 | INFO | Train Epoch: 0 [17408512/18327966 (95%)] Loss: 0.75592 (0.8257) Data (t): 0.001 Batch (t): 0.905, 563.053/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,16:45:32 | INFO | Train Epoch: 0 [17459712/18327966 (95%)] Loss: 0.66685 (0.8253) Data (t): 0.001 Batch (t): 0.906, 566.329/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,16:47:08 | INFO | Train Epoch: 0 [17510912/18327966 (96%)] Loss: 0.80104 (0.8252) Data (t): 0.001 Batch (t): 0.956, 567.933/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,16:48:44 | INFO | Train Epoch: 0 [17562112/18327966 (96%)] Loss: 0.70192 (0.8248) Data (t): 0.001 Batch (t): 0.961, 565.618/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,16:50:14 | INFO | Train Epoch: 0 [17613312/18327966 (96%)] Loss: 0.74147 (0.8246) Data (t): 0.001 Batch (t): 0.905, 566.163/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,16:51:45 | INFO | Train Epoch: 0 [17664512/18327966 (96%)] Loss: 0.84285 (0.8246) Data (t): 0.001 Batch (t): 0.907, 563.082/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,16:53:15 | INFO | Train Epoch: 0 [17715712/18327966 (97%)] Loss: 0.74607 (0.8244) Data (t): 0.001 Batch (t): 0.906, 562.637/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,16:54:49 | INFO | Train Epoch: 0 [17766912/18327966 (97%)] Loss: 0.67920 (0.8240) Data (t): 0.001 Batch (t): 0.930, 564.478/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,16:56:27 | INFO | Train Epoch: 0 [17818112/18327966 (97%)] Loss: 0.64175 (0.8235) Data (t): 0.001 Batch (t): 0.985, 564.077/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,16:57:58 | INFO | Train Epoch: 0 [17869312/18327966 (97%)] Loss: 0.74352 (0.8233) Data (t): 0.001 Batch (t): 0.906, 566.344/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,16:59:28 | INFO | Train Epoch: 0 [17920512/18327966 (98%)] Loss: 0.66121 (0.8228) Data (t): 0.001 Batch (t): 0.905, 565.226/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,17:00:59 | INFO | Train Epoch: 0 [17971712/18327966 (98%)] Loss: 0.65898 (0.8223) Data (t): 0.001 Batch (t): 0.906, 563.137/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,17:02:31 | INFO | Train Epoch: 0 [18022912/18327966 (98%)] Loss: 0.70338 (0.8220) Data (t): 0.001 Batch (t): 0.918, 561.867/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,17:04:09 | INFO | Train Epoch: 0 [18074112/18327966 (99%)] Loss: 0.67658 (0.8216) Data (t): 0.001 Batch (t): 0.987, 563.223/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,17:05:41 | INFO | Train Epoch: 0 [18125312/18327966 (99%)] Loss: 0.63734 (0.8211) Data (t): 0.001 Batch (t): 0.919, 566.215/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,17:07:12 | INFO | Train Epoch: 0 [18176512/18327966 (99%)] Loss: 0.62359 (0.8205) Data (t): 0.001 Batch (t): 0.907, 562.668/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,17:08:43 | INFO | Train Epoch: 0 [18227712/18327966 (99%)] Loss: 0.66212 (0.8201) Data (t): 0.001 Batch (t): 0.908, 567.202/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,17:10:14 | INFO | Train Epoch: 0 [18278912/18327966 (100%)] Loss: 0.63679 (0.8195) Data (t): 0.001 Batch (t): 0.919, 568.407/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,17:11:46 | INFO | Train Epoch: 0 [18327552/18327966 (100%)] Loss: 0.67906 (0.8192) Data (t): 0.002 Batch (t): 0.960, 568.829/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,17:11:54 | INFO | Start epoch 1 -2024-11-27,17:11:59 | INFO | Train Epoch: 1 [ 512/18327966 (0%)] Loss: 0.66913 (0.6691) Data (t): 3.483 Batch (t): 4.473, 114.475/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,17:13:33 | INFO | Train Epoch: 1 [ 51712/18327966 (0%)] Loss: 0.63908 (0.6541) Data (t): 0.001 Batch (t): 0.938, 562.825/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,17:15:03 | INFO | Train Epoch: 1 [ 102912/18327966 (1%)] Loss: 0.70077 (0.6697) Data (t): 0.001 Batch (t): 0.907, 563.533/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,17:16:34 | INFO | Train Epoch: 1 [ 154112/18327966 (1%)] Loss: 0.73076 (0.6849) Data (t): 0.001 Batch (t): 0.906, 560.670/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,17:18:04 | INFO | Train Epoch: 1 [ 205312/18327966 (1%)] Loss: 0.65314 (0.6786) Data (t): 0.001 Batch (t): 0.905, 567.818/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,17:19:41 | INFO | Train Epoch: 1 [ 256512/18327966 (1%)] Loss: 0.66934 (0.6770) Data (t): 0.001 Batch (t): 0.968, 566.102/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,17:21:16 | INFO | Train Epoch: 1 [ 307712/18327966 (2%)] Loss: 0.71767 (0.6828) Data (t): 0.001 Batch (t): 0.945, 565.219/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,17:22:46 | INFO | Train Epoch: 1 [ 358912/18327966 (2%)] Loss: 0.57649 (0.6695) Data (t): 0.001 Batch (t): 0.907, 564.312/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,17:24:17 | INFO | Train Epoch: 1 [ 410112/18327966 (2%)] Loss: 0.70354 (0.6733) Data (t): 0.001 Batch (t): 0.908, 563.350/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,17:25:48 | INFO | Train Epoch: 1 [ 461312/18327966 (3%)] Loss: 0.69035 (0.6750) Data (t): 0.001 Batch (t): 0.907, 563.008/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,17:27:21 | INFO | Train Epoch: 1 [ 512512/18327966 (3%)] Loss: 0.69304 (0.6767) Data (t): 0.001 Batch (t): 0.929, 566.702/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,17:28:58 | INFO | Train Epoch: 1 [ 563712/18327966 (3%)] Loss: 0.62971 (0.6728) Data (t): 0.001 Batch (t): 0.976, 564.669/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,17:30:29 | INFO | Train Epoch: 1 [ 614912/18327966 (3%)] Loss: 0.66175 (0.6719) Data (t): 0.001 Batch (t): 0.907, 564.262/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,17:32:00 | INFO | Train Epoch: 1 [ 666112/18327966 (4%)] Loss: 0.74430 (0.6771) Data (t): 0.001 Batch (t): 0.907, 566.835/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,17:33:30 | INFO | Train Epoch: 1 [ 717312/18327966 (4%)] Loss: 0.75167 (0.6820) Data (t): 0.001 Batch (t): 0.906, 567.995/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,17:35:03 | INFO | Train Epoch: 1 [ 768512/18327966 (4%)] Loss: 0.63260 (0.6790) Data (t): 0.001 Batch (t): 0.928, 565.533/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,17:36:40 | INFO | Train Epoch: 1 [ 819712/18327966 (4%)] Loss: 0.67778 (0.6789) Data (t): 0.001 Batch (t): 0.965, 566.131/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,17:38:11 | INFO | Train Epoch: 1 [ 870912/18327966 (5%)] Loss: 0.70370 (0.6803) Data (t): 0.001 Batch (t): 0.917, 563.589/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,17:39:42 | INFO | Train Epoch: 1 [ 922112/18327966 (5%)] Loss: 0.59004 (0.6755) Data (t): 0.001 Batch (t): 0.906, 564.049/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,17:41:13 | INFO | Train Epoch: 1 [ 973312/18327966 (5%)] Loss: 0.67312 (0.6754) Data (t): 0.001 Batch (t): 0.906, 564.743/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,17:42:44 | INFO | Train Epoch: 1 [ 1024512/18327966 (6%)] Loss: 0.67840 (0.6755) Data (t): 0.001 Batch (t): 0.918, 565.318/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,17:44:21 | INFO | Train Epoch: 1 [ 1075712/18327966 (6%)] Loss: 0.70906 (0.6771) Data (t): 0.001 Batch (t): 0.961, 570.208/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,17:45:54 | INFO | Train Epoch: 1 [ 1126912/18327966 (6%)] Loss: 0.65694 (0.6762) Data (t): 0.001 Batch (t): 0.933, 567.435/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,17:47:25 | INFO | Train Epoch: 1 [ 1178112/18327966 (6%)] Loss: 0.62062 (0.6739) Data (t): 0.001 Batch (t): 0.908, 563.273/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,17:48:56 | INFO | Train Epoch: 1 [ 1229312/18327966 (7%)] Loss: 0.81983 (0.6797) Data (t): 0.001 Batch (t): 0.908, 564.447/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,17:50:26 | INFO | Train Epoch: 1 [ 1280512/18327966 (7%)] Loss: 0.60785 (0.6769) Data (t): 0.001 Batch (t): 0.908, 562.500/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,17:52:02 | INFO | Train Epoch: 1 [ 1331712/18327966 (7%)] Loss: 0.73337 (0.6790) Data (t): 0.001 Batch (t): 0.962, 564.743/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,17:53:37 | INFO | Train Epoch: 1 [ 1382912/18327966 (8%)] Loss: 0.59648 (0.6761) Data (t): 0.001 Batch (t): 0.945, 563.327/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,17:55:08 | INFO | Train Epoch: 1 [ 1434112/18327966 (8%)] Loss: 0.64995 (0.6752) Data (t): 0.001 Batch (t): 0.906, 564.551/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,17:56:38 | INFO | Train Epoch: 1 [ 1485312/18327966 (8%)] Loss: 0.60291 (0.6728) Data (t): 0.001 Batch (t): 0.907, 564.474/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,17:58:09 | INFO | Train Epoch: 1 [ 1536512/18327966 (8%)] Loss: 0.62594 (0.6713) Data (t): 0.001 Batch (t): 0.907, 563.075/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,17:59:45 | INFO | Train Epoch: 1 [ 1587712/18327966 (9%)] Loss: 0.68646 (0.6717) Data (t): 0.001 Batch (t): 0.961, 567.056/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,18:01:19 | INFO | Train Epoch: 1 [ 1638912/18327966 (9%)] Loss: 0.59568 (0.6694) Data (t): 0.001 Batch (t): 0.944, 564.545/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,18:02:50 | INFO | Train Epoch: 1 [ 1690112/18327966 (9%)] Loss: 0.67253 (0.6695) Data (t): 0.001 Batch (t): 0.907, 567.567/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,18:04:21 | INFO | Train Epoch: 1 [ 1741312/18327966 (10%)] Loss: 0.63733 (0.6686) Data (t): 0.001 Batch (t): 0.906, 567.114/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,18:05:51 | INFO | Train Epoch: 1 [ 1792512/18327966 (10%)] Loss: 0.66682 (0.6686) Data (t): 0.001 Batch (t): 0.905, 565.835/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,18:07:24 | INFO | Train Epoch: 1 [ 1843712/18327966 (10%)] Loss: 0.59379 (0.6665) Data (t): 0.001 Batch (t): 0.928, 568.804/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,18:09:00 | INFO | Train Epoch: 1 [ 1894912/18327966 (10%)] Loss: 0.69137 (0.6672) Data (t): 0.001 Batch (t): 0.966, 563.533/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,18:10:32 | INFO | Train Epoch: 1 [ 1946112/18327966 (11%)] Loss: 0.73578 (0.6690) Data (t): 0.001 Batch (t): 0.917, 564.422/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,18:12:03 | INFO | Train Epoch: 1 [ 1997312/18327966 (11%)] Loss: 0.67545 (0.6691) Data (t): 0.001 Batch (t): 0.905, 565.071/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,18:13:33 | INFO | Train Epoch: 1 [ 2048512/18327966 (11%)] Loss: 0.61016 (0.6677) Data (t): 0.001 Batch (t): 0.905, 565.471/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,18:15:06 | INFO | Train Epoch: 1 [ 2099712/18327966 (11%)] Loss: 0.53652 (0.6646) Data (t): 0.001 Batch (t): 0.928, 254.925/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,18:16:41 | INFO | Train Epoch: 1 [ 2150912/18327966 (12%)] Loss: 0.74064 (0.6663) Data (t): 0.001 Batch (t): 0.955, 567.967/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,18:18:13 | INFO | Train Epoch: 1 [ 2202112/18327966 (12%)] Loss: 0.75510 (0.6683) Data (t): 0.001 Batch (t): 0.917, 563.309/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,18:19:44 | INFO | Train Epoch: 1 [ 2253312/18327966 (12%)] Loss: 0.74071 (0.6699) Data (t): 0.001 Batch (t): 0.906, 561.852/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,18:21:14 | INFO | Train Epoch: 1 [ 2304512/18327966 (13%)] Loss: 0.74792 (0.6716) Data (t): 0.001 Batch (t): 0.907, 566.627/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,18:22:46 | INFO | Train Epoch: 1 [ 2355712/18327966 (13%)] Loss: 0.65758 (0.6713) Data (t): 0.001 Batch (t): 0.918, 557.020/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,18:24:21 | INFO | Train Epoch: 1 [ 2406912/18327966 (13%)] Loss: 0.62461 (0.6704) Data (t): 0.001 Batch (t): 0.951, 562.503/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,18:25:56 | INFO | Train Epoch: 1 [ 2458112/18327966 (13%)] Loss: 0.70728 (0.6711) Data (t): 0.001 Batch (t): 0.945, 563.689/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,18:27:26 | INFO | Train Epoch: 1 [ 2509312/18327966 (14%)] Loss: 0.62899 (0.6703) Data (t): 0.001 Batch (t): 0.907, 564.291/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,18:28:57 | INFO | Train Epoch: 1 [ 2560512/18327966 (14%)] Loss: 0.76091 (0.6721) Data (t): 0.001 Batch (t): 0.908, 565.948/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,18:30:28 | INFO | Train Epoch: 1 [ 2611712/18327966 (14%)] Loss: 0.69776 (0.6726) Data (t): 0.001 Batch (t): 0.905, 563.340/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,18:32:04 | INFO | Train Epoch: 1 [ 2662912/18327966 (15%)] Loss: 0.62824 (0.6717) Data (t): 0.001 Batch (t): 0.960, 563.191/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,18:33:38 | INFO | Train Epoch: 1 [ 2714112/18327966 (15%)] Loss: 0.73761 (0.6729) Data (t): 0.001 Batch (t): 0.944, 562.579/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,18:35:09 | INFO | Train Epoch: 1 [ 2765312/18327966 (15%)] Loss: 0.71559 (0.6737) Data (t): 0.001 Batch (t): 0.907, 566.928/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,18:36:40 | INFO | Train Epoch: 1 [ 2816512/18327966 (15%)] Loss: 0.59135 (0.6722) Data (t): 0.001 Batch (t): 0.907, 561.997/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,18:38:10 | INFO | Train Epoch: 1 [ 2867712/18327966 (16%)] Loss: 0.67001 (0.6722) Data (t): 0.001 Batch (t): 0.906, 564.822/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,18:39:43 | INFO | Train Epoch: 1 [ 2918912/18327966 (16%)] Loss: 0.74335 (0.6734) Data (t): 0.001 Batch (t): 0.928, 564.728/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,18:41:20 | INFO | Train Epoch: 1 [ 2970112/18327966 (16%)] Loss: 0.70002 (0.6739) Data (t): 0.001 Batch (t): 0.966, 566.810/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,18:42:50 | INFO | Train Epoch: 1 [ 3021312/18327966 (16%)] Loss: 0.56721 (0.6721) Data (t): 0.001 Batch (t): 0.906, 565.845/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,18:44:21 | INFO | Train Epoch: 1 [ 3072512/18327966 (17%)] Loss: 0.58193 (0.6706) Data (t): 0.001 Batch (t): 0.906, 564.045/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,18:45:51 | INFO | Train Epoch: 1 [ 3123712/18327966 (17%)] Loss: 0.54468 (0.6686) Data (t): 0.001 Batch (t): 0.905, 564.849/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,18:47:24 | INFO | Train Epoch: 1 [ 3174912/18327966 (17%)] Loss: 0.53598 (0.6665) Data (t): 0.001 Batch (t): 0.928, 564.438/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,18:49:01 | INFO | Train Epoch: 1 [ 3226112/18327966 (18%)] Loss: 0.66998 (0.6665) Data (t): 0.001 Batch (t): 0.966, 561.995/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,18:50:32 | INFO | Train Epoch: 1 [ 3277312/18327966 (18%)] Loss: 0.66796 (0.6666) Data (t): 0.001 Batch (t): 0.917, 567.303/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,18:52:03 | INFO | Train Epoch: 1 [ 3328512/18327966 (18%)] Loss: 0.65948 (0.6665) Data (t): 0.001 Batch (t): 0.906, 562.575/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,18:53:34 | INFO | Train Epoch: 1 [ 3379712/18327966 (18%)] Loss: 0.81103 (0.6686) Data (t): 0.001 Batch (t): 0.906, 564.834/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,18:55:05 | INFO | Train Epoch: 1 [ 3430912/18327966 (19%)] Loss: 0.66788 (0.6686) Data (t): 0.001 Batch (t): 0.917, 566.000/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,18:56:40 | INFO | Train Epoch: 1 [ 3482112/18327966 (19%)] Loss: 0.66819 (0.6686) Data (t): 0.001 Batch (t): 0.950, 566.318/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,18:58:15 | INFO | Train Epoch: 1 [ 3533312/18327966 (19%)] Loss: 0.66294 (0.6685) Data (t): 0.001 Batch (t): 0.943, 557.182/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,18:59:45 | INFO | Train Epoch: 1 [ 3584512/18327966 (20%)] Loss: 0.59270 (0.6674) Data (t): 0.001 Batch (t): 0.907, 564.956/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,19:01:16 | INFO | Train Epoch: 1 [ 3635712/18327966 (20%)] Loss: 0.59516 (0.6664) Data (t): 0.001 Batch (t): 0.907, 563.869/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,19:02:48 | INFO | Train Epoch: 1 [ 3686912/18327966 (20%)] Loss: 0.75808 (0.6677) Data (t): 0.001 Batch (t): 0.918, 561.923/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,19:04:24 | INFO | Train Epoch: 1 [ 3738112/18327966 (20%)] Loss: 0.59416 (0.6667) Data (t): 0.001 Batch (t): 0.961, 558.011/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,19:05:58 | INFO | Train Epoch: 1 [ 3789312/18327966 (21%)] Loss: 0.65969 (0.6666) Data (t): 0.001 Batch (t): 0.945, 565.548/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,19:07:29 | INFO | Train Epoch: 1 [ 3840512/18327966 (21%)] Loss: 0.62674 (0.6661) Data (t): 0.001 Batch (t): 0.907, 564.029/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,19:09:00 | INFO | Train Epoch: 1 [ 3891712/18327966 (21%)] Loss: 0.66089 (0.6660) Data (t): 0.001 Batch (t): 0.908, 565.720/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,19:10:30 | INFO | Train Epoch: 1 [ 3942912/18327966 (22%)] Loss: 0.59393 (0.6651) Data (t): 0.001 Batch (t): 0.906, 559.248/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,19:12:07 | INFO | Train Epoch: 1 [ 3994112/18327966 (22%)] Loss: 0.73094 (0.6659) Data (t): 0.001 Batch (t): 0.962, 567.980/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,19:13:41 | INFO | Train Epoch: 1 [ 4045312/18327966 (22%)] Loss: 0.59307 (0.6650) Data (t): 0.001 Batch (t): 0.944, 566.306/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,19:15:12 | INFO | Train Epoch: 1 [ 4096512/18327966 (22%)] Loss: 0.56491 (0.6638) Data (t): 0.001 Batch (t): 0.907, 563.109/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,19:16:42 | INFO | Train Epoch: 1 [ 4147712/18327966 (23%)] Loss: 0.62175 (0.6633) Data (t): 0.001 Batch (t): 0.907, 568.173/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,19:18:13 | INFO | Train Epoch: 1 [ 4198912/18327966 (23%)] Loss: 0.66539 (0.6633) Data (t): 0.001 Batch (t): 0.906, 566.628/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,19:19:46 | INFO | Train Epoch: 1 [ 4250112/18327966 (23%)] Loss: 0.72566 (0.6640) Data (t): 0.001 Batch (t): 0.929, 565.063/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,19:21:21 | INFO | Train Epoch: 1 [ 4301312/18327966 (23%)] Loss: 0.76734 (0.6653) Data (t): 0.001 Batch (t): 0.956, 565.125/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,19:22:53 | INFO | Train Epoch: 1 [ 4352512/18327966 (24%)] Loss: 0.55168 (0.6639) Data (t): 0.001 Batch (t): 0.918, 565.930/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,19:24:24 | INFO | Train Epoch: 1 [ 4403712/18327966 (24%)] Loss: 0.66188 (0.6639) Data (t): 0.001 Batch (t): 0.906, 563.510/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,19:25:55 | INFO | Train Epoch: 1 [ 4454912/18327966 (24%)] Loss: 0.81308 (0.6656) Data (t): 0.001 Batch (t): 0.907, 564.819/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,19:27:27 | INFO | Train Epoch: 1 [ 4506112/18327966 (25%)] Loss: 0.60485 (0.6649) Data (t): 0.001 Batch (t): 0.928, 567.825/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,19:29:03 | INFO | Train Epoch: 1 [ 4557312/18327966 (25%)] Loss: 0.57098 (0.6639) Data (t): 0.001 Batch (t): 0.954, 568.494/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,19:30:35 | INFO | Train Epoch: 1 [ 4608512/18327966 (25%)] Loss: 0.63252 (0.6635) Data (t): 0.001 Batch (t): 0.927, 564.581/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,19:32:06 | INFO | Train Epoch: 1 [ 4659712/18327966 (25%)] Loss: 0.63090 (0.6632) Data (t): 0.001 Batch (t): 0.905, 565.250/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,19:33:37 | INFO | Train Epoch: 1 [ 4710912/18327966 (26%)] Loss: 0.66676 (0.6632) Data (t): 0.001 Batch (t): 0.907, 564.346/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,19:35:08 | INFO | Train Epoch: 1 [ 4762112/18327966 (26%)] Loss: 0.61326 (0.6627) Data (t): 0.001 Batch (t): 0.917, 564.713/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,19:36:43 | INFO | Train Epoch: 1 [ 4813312/18327966 (26%)] Loss: 0.60432 (0.6621) Data (t): 0.001 Batch (t): 0.950, 566.420/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,19:38:18 | INFO | Train Epoch: 1 [ 4864512/18327966 (27%)] Loss: 0.56939 (0.6611) Data (t): 0.001 Batch (t): 0.945, 565.207/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,19:39:48 | INFO | Train Epoch: 1 [ 4915712/18327966 (27%)] Loss: 0.70345 (0.6615) Data (t): 0.001 Batch (t): 0.907, 565.100/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,19:41:19 | INFO | Train Epoch: 1 [ 4966912/18327966 (27%)] Loss: 0.67382 (0.6617) Data (t): 0.001 Batch (t): 0.906, 563.208/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,19:42:51 | INFO | Train Epoch: 1 [ 5018112/18327966 (27%)] Loss: 0.73476 (0.6624) Data (t): 0.001 Batch (t): 0.918, 564.332/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,19:44:26 | INFO | Train Epoch: 1 [ 5069312/18327966 (28%)] Loss: 0.76519 (0.6634) Data (t): 0.001 Batch (t): 0.951, 565.258/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,19:46:00 | INFO | Train Epoch: 1 [ 5120512/18327966 (28%)] Loss: 0.60902 (0.6629) Data (t): 0.001 Batch (t): 0.944, 567.596/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,19:47:31 | INFO | Train Epoch: 1 [ 5171712/18327966 (28%)] Loss: 0.72222 (0.6635) Data (t): 0.001 Batch (t): 0.907, 563.080/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,19:49:02 | INFO | Train Epoch: 1 [ 5222912/18327966 (28%)] Loss: 0.73441 (0.6642) Data (t): 0.001 Batch (t): 0.906, 561.920/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,19:50:32 | INFO | Train Epoch: 1 [ 5274112/18327966 (29%)] Loss: 0.56582 (0.6632) Data (t): 0.001 Batch (t): 0.906, 568.338/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,19:52:07 | INFO | Train Epoch: 1 [ 5325312/18327966 (29%)] Loss: 0.67192 (0.6633) Data (t): 0.001 Batch (t): 0.949, 564.687/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,19:53:41 | INFO | Train Epoch: 1 [ 5376512/18327966 (29%)] Loss: 0.54932 (0.6622) Data (t): 0.001 Batch (t): 0.933, 567.483/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,19:55:13 | INFO | Train Epoch: 1 [ 5427712/18327966 (30%)] Loss: 0.58280 (0.6615) Data (t): 0.001 Batch (t): 0.928, 568.340/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,19:56:44 | INFO | Train Epoch: 1 [ 5478912/18327966 (30%)] Loss: 0.66057 (0.6615) Data (t): 0.001 Batch (t): 0.906, 564.205/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,19:58:14 | INFO | Train Epoch: 1 [ 5530112/18327966 (30%)] Loss: 0.71486 (0.6620) Data (t): 0.001 Batch (t): 0.906, 561.642/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,19:59:47 | INFO | Train Epoch: 1 [ 5581312/18327966 (30%)] Loss: 0.73335 (0.6626) Data (t): 0.001 Batch (t): 0.928, 564.421/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,20:01:23 | INFO | Train Epoch: 1 [ 5632512/18327966 (31%)] Loss: 0.70193 (0.6630) Data (t): 0.001 Batch (t): 0.956, 567.844/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,20:02:56 | INFO | Train Epoch: 1 [ 5683712/18327966 (31%)] Loss: 0.52204 (0.6617) Data (t): 0.001 Batch (t): 0.928, 567.598/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,20:04:26 | INFO | Train Epoch: 1 [ 5734912/18327966 (31%)] Loss: 0.69420 (0.6620) Data (t): 0.001 Batch (t): 0.905, 566.033/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,20:05:57 | INFO | Train Epoch: 1 [ 5786112/18327966 (32%)] Loss: 0.71503 (0.6625) Data (t): 0.001 Batch (t): 0.905, 566.020/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,20:07:29 | INFO | Train Epoch: 1 [ 5837312/18327966 (32%)] Loss: 0.69995 (0.6628) Data (t): 0.001 Batch (t): 0.927, 567.775/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,20:09:05 | INFO | Train Epoch: 1 [ 5888512/18327966 (32%)] Loss: 0.89915 (0.6648) Data (t): 0.001 Batch (t): 0.953, 564.504/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,20:10:38 | INFO | Train Epoch: 1 [ 5939712/18327966 (32%)] Loss: 0.67155 (0.6649) Data (t): 0.001 Batch (t): 0.928, 562.787/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,20:12:08 | INFO | Train Epoch: 1 [ 5990912/18327966 (33%)] Loss: 0.66748 (0.6649) Data (t): 0.001 Batch (t): 0.905, 564.888/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,20:13:39 | INFO | Train Epoch: 1 [ 6042112/18327966 (33%)] Loss: 0.64394 (0.6647) Data (t): 0.001 Batch (t): 0.905, 564.389/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,20:15:10 | INFO | Train Epoch: 1 [ 6093312/18327966 (33%)] Loss: 0.62967 (0.6644) Data (t): 0.001 Batch (t): 0.916, 565.028/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,20:16:45 | INFO | Train Epoch: 1 [ 6144512/18327966 (34%)] Loss: 0.63318 (0.6642) Data (t): 0.001 Batch (t): 0.948, 566.230/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,20:18:19 | INFO | Train Epoch: 1 [ 6195712/18327966 (34%)] Loss: 0.56638 (0.6634) Data (t): 0.001 Batch (t): 0.943, 569.104/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,20:19:50 | INFO | Train Epoch: 1 [ 6246912/18327966 (34%)] Loss: 0.65090 (0.6633) Data (t): 0.001 Batch (t): 0.904, 566.904/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,20:21:20 | INFO | Train Epoch: 1 [ 6298112/18327966 (34%)] Loss: 0.49055 (0.6619) Data (t): 0.001 Batch (t): 0.904, 566.834/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,20:22:52 | INFO | Train Epoch: 1 [ 6349312/18327966 (35%)] Loss: 0.55734 (0.6610) Data (t): 0.001 Batch (t): 0.915, 567.431/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,20:24:26 | INFO | Train Epoch: 1 [ 6400512/18327966 (35%)] Loss: 0.62030 (0.6607) Data (t): 0.001 Batch (t): 0.949, 568.121/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,20:25:59 | INFO | Train Epoch: 1 [ 6451712/18327966 (35%)] Loss: 0.57143 (0.6600) Data (t): 0.001 Batch (t): 0.928, 566.480/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,20:27:31 | INFO | Train Epoch: 1 [ 6502912/18327966 (35%)] Loss: 0.62456 (0.6597) Data (t): 0.001 Batch (t): 0.915, 565.591/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,20:29:01 | INFO | Train Epoch: 1 [ 6554112/18327966 (36%)] Loss: 0.68711 (0.6600) Data (t): 0.001 Batch (t): 0.904, 569.037/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,20:30:32 | INFO | Train Epoch: 1 [ 6605312/18327966 (36%)] Loss: 0.67919 (0.6601) Data (t): 0.001 Batch (t): 0.904, 566.271/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,20:32:06 | INFO | Train Epoch: 1 [ 6656512/18327966 (36%)] Loss: 0.65404 (0.6601) Data (t): 0.001 Batch (t): 0.948, 565.295/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,20:33:40 | INFO | Train Epoch: 1 [ 6707712/18327966 (37%)] Loss: 0.60432 (0.6596) Data (t): 0.001 Batch (t): 0.932, 565.477/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,20:35:12 | INFO | Train Epoch: 1 [ 6758912/18327966 (37%)] Loss: 0.65546 (0.6596) Data (t): 0.001 Batch (t): 0.927, 563.883/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,20:36:43 | INFO | Train Epoch: 1 [ 6810112/18327966 (37%)] Loss: 0.70056 (0.6599) Data (t): 0.001 Batch (t): 0.903, 565.637/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,20:38:13 | INFO | Train Epoch: 1 [ 6861312/18327966 (37%)] Loss: 0.52679 (0.6589) Data (t): 0.001 Batch (t): 0.905, 566.382/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,20:39:46 | INFO | Train Epoch: 1 [ 6912512/18327966 (38%)] Loss: 0.49114 (0.6577) Data (t): 0.001 Batch (t): 0.927, 561.563/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,20:41:21 | INFO | Train Epoch: 1 [ 6963712/18327966 (38%)] Loss: 0.62414 (0.6574) Data (t): 0.001 Batch (t): 0.954, 565.961/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,20:42:54 | INFO | Train Epoch: 1 [ 7014912/18327966 (38%)] Loss: 0.60583 (0.6571) Data (t): 0.001 Batch (t): 0.924, 570.330/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,20:44:24 | INFO | Train Epoch: 1 [ 7066112/18327966 (39%)] Loss: 0.61837 (0.6568) Data (t): 0.001 Batch (t): 0.902, 567.658/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,20:45:54 | INFO | Train Epoch: 1 [ 7117312/18327966 (39%)] Loss: 0.63419 (0.6566) Data (t): 0.001 Batch (t): 0.902, 567.254/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,20:47:25 | INFO | Train Epoch: 1 [ 7168512/18327966 (39%)] Loss: 0.44951 (0.6552) Data (t): 0.001 Batch (t): 0.913, 568.533/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,20:49:00 | INFO | Train Epoch: 1 [ 7219712/18327966 (39%)] Loss: 0.63526 (0.6550) Data (t): 0.001 Batch (t): 0.946, 567.238/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,20:50:34 | INFO | Train Epoch: 1 [ 7270912/18327966 (40%)] Loss: 0.55227 (0.6543) Data (t): 0.001 Batch (t): 0.939, 569.681/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,20:52:04 | INFO | Train Epoch: 1 [ 7322112/18327966 (40%)] Loss: 0.63829 (0.6542) Data (t): 0.001 Batch (t): 0.901, 569.133/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,20:53:34 | INFO | Train Epoch: 1 [ 7373312/18327966 (40%)] Loss: 0.80754 (0.6553) Data (t): 0.001 Batch (t): 0.900, 566.450/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,20:55:05 | INFO | Train Epoch: 1 [ 7424512/18327966 (41%)] Loss: 0.52088 (0.6543) Data (t): 0.001 Batch (t): 0.913, 568.066/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,20:56:40 | INFO | Train Epoch: 1 [ 7475712/18327966 (41%)] Loss: 0.57125 (0.6538) Data (t): 0.001 Batch (t): 0.947, 571.264/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,20:58:13 | INFO | Train Epoch: 1 [ 7526912/18327966 (41%)] Loss: 0.64101 (0.6537) Data (t): 0.001 Batch (t): 0.929, 564.243/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,20:59:44 | INFO | Train Epoch: 1 [ 7578112/18327966 (41%)] Loss: 0.62347 (0.6535) Data (t): 0.001 Batch (t): 0.914, 565.130/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,21:01:15 | INFO | Train Epoch: 1 [ 7629312/18327966 (42%)] Loss: 0.74408 (0.6541) Data (t): 0.001 Batch (t): 0.902, 567.172/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,21:02:46 | INFO | Train Epoch: 1 [ 7680512/18327966 (42%)] Loss: 0.64518 (0.6540) Data (t): 0.001 Batch (t): 0.915, 566.109/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,21:04:20 | INFO | Train Epoch: 1 [ 7731712/18327966 (42%)] Loss: 0.64481 (0.6540) Data (t): 0.001 Batch (t): 0.937, 565.942/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,21:05:53 | INFO | Train Epoch: 1 [ 7782912/18327966 (42%)] Loss: 0.56547 (0.6534) Data (t): 0.001 Batch (t): 0.930, 566.493/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,21:07:25 | INFO | Train Epoch: 1 [ 7834112/18327966 (43%)] Loss: 0.58891 (0.6530) Data (t): 0.001 Batch (t): 0.924, 566.614/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,21:08:56 | INFO | Train Epoch: 1 [ 7885312/18327966 (43%)] Loss: 0.72028 (0.6534) Data (t): 0.001 Batch (t): 0.903, 568.279/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,21:10:26 | INFO | Train Epoch: 1 [ 7936512/18327966 (43%)] Loss: 0.50032 (0.6524) Data (t): 0.001 Batch (t): 0.903, 568.971/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,21:12:00 | INFO | Train Epoch: 1 [ 7987712/18327966 (44%)] Loss: 0.61503 (0.6522) Data (t): 0.001 Batch (t): 0.937, 248.987/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,21:13:34 | INFO | Train Epoch: 1 [ 8038912/18327966 (44%)] Loss: 0.63741 (0.6521) Data (t): 0.001 Batch (t): 0.943, 567.049/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,21:15:07 | INFO | Train Epoch: 1 [ 8090112/18327966 (44%)] Loss: 0.61425 (0.6518) Data (t): 0.001 Batch (t): 0.927, 565.820/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,21:16:37 | INFO | Train Epoch: 1 [ 8141312/18327966 (44%)] Loss: 0.58557 (0.6514) Data (t): 0.001 Batch (t): 0.905, 565.568/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,21:18:08 | INFO | Train Epoch: 1 [ 8192512/18327966 (45%)] Loss: 0.60411 (0.6511) Data (t): 0.001 Batch (t): 0.904, 567.246/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,21:19:40 | INFO | Train Epoch: 1 [ 8243712/18327966 (45%)] Loss: 0.67015 (0.6513) Data (t): 0.001 Batch (t): 0.927, 565.368/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,21:21:16 | INFO | Train Epoch: 1 [ 8294912/18327966 (45%)] Loss: 0.67500 (0.6514) Data (t): 0.001 Batch (t): 0.957, 564.104/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,21:22:49 | INFO | Train Epoch: 1 [ 8346112/18327966 (46%)] Loss: 0.60725 (0.6511) Data (t): 0.001 Batch (t): 0.928, 567.442/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,21:24:19 | INFO | Train Epoch: 1 [ 8397312/18327966 (46%)] Loss: 0.71678 (0.6515) Data (t): 0.001 Batch (t): 0.905, 565.449/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,21:25:50 | INFO | Train Epoch: 1 [ 8448512/18327966 (46%)] Loss: 0.61427 (0.6513) Data (t): 0.001 Batch (t): 0.904, 566.249/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,21:27:21 | INFO | Train Epoch: 1 [ 8499712/18327966 (46%)] Loss: 0.65773 (0.6513) Data (t): 0.001 Batch (t): 0.916, 567.155/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,21:28:56 | INFO | Train Epoch: 1 [ 8550912/18327966 (47%)] Loss: 0.64058 (0.6513) Data (t): 0.001 Batch (t): 0.948, 567.923/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,21:30:29 | INFO | Train Epoch: 1 [ 8602112/18327966 (47%)] Loss: 0.71853 (0.6517) Data (t): 0.001 Batch (t): 0.931, 565.562/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,21:32:01 | INFO | Train Epoch: 1 [ 8653312/18327966 (47%)] Loss: 0.61592 (0.6515) Data (t): 0.001 Batch (t): 0.914, 566.014/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,21:33:31 | INFO | Train Epoch: 1 [ 8704512/18327966 (47%)] Loss: 0.57701 (0.6510) Data (t): 0.001 Batch (t): 0.904, 567.387/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,21:35:02 | INFO | Train Epoch: 1 [ 8755712/18327966 (48%)] Loss: 0.55777 (0.6505) Data (t): 0.001 Batch (t): 0.915, 562.942/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,21:36:36 | INFO | Train Epoch: 1 [ 8806912/18327966 (48%)] Loss: 0.64339 (0.6504) Data (t): 0.001 Batch (t): 0.938, 567.300/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,21:38:09 | INFO | Train Epoch: 1 [ 8858112/18327966 (48%)] Loss: 0.68654 (0.6507) Data (t): 0.001 Batch (t): 0.931, 565.311/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,21:39:42 | INFO | Train Epoch: 1 [ 8909312/18327966 (49%)] Loss: 0.55804 (0.6501) Data (t): 0.001 Batch (t): 0.927, 570.551/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,21:41:13 | INFO | Train Epoch: 1 [ 8960512/18327966 (49%)] Loss: 0.63887 (0.6501) Data (t): 0.001 Batch (t): 0.904, 563.520/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,21:42:43 | INFO | Train Epoch: 1 [ 9011712/18327966 (49%)] Loss: 0.53081 (0.6494) Data (t): 0.001 Batch (t): 0.905, 563.936/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,21:44:18 | INFO | Train Epoch: 1 [ 9062912/18327966 (49%)] Loss: 0.57937 (0.6490) Data (t): 0.001 Batch (t): 0.951, 566.313/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,21:45:51 | INFO | Train Epoch: 1 [ 9114112/18327966 (50%)] Loss: 0.71471 (0.6494) Data (t): 0.001 Batch (t): 0.932, 567.052/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,21:47:24 | INFO | Train Epoch: 1 [ 9165312/18327966 (50%)] Loss: 0.80059 (0.6502) Data (t): 0.001 Batch (t): 0.927, 565.579/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,21:48:55 | INFO | Train Epoch: 1 [ 9216512/18327966 (50%)] Loss: 0.66906 (0.6503) Data (t): 0.001 Batch (t): 0.905, 567.125/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,21:50:25 | INFO | Train Epoch: 1 [ 9267712/18327966 (51%)] Loss: 0.54386 (0.6497) Data (t): 0.001 Batch (t): 0.906, 564.674/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,21:51:58 | INFO | Train Epoch: 1 [ 9318912/18327966 (51%)] Loss: 0.62225 (0.6496) Data (t): 0.001 Batch (t): 0.928, 566.886/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,21:53:34 | INFO | Train Epoch: 1 [ 9370112/18327966 (51%)] Loss: 0.69356 (0.6498) Data (t): 0.001 Batch (t): 0.956, 565.930/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,21:55:06 | INFO | Train Epoch: 1 [ 9421312/18327966 (51%)] Loss: 0.58183 (0.6494) Data (t): 0.001 Batch (t): 0.929, 565.091/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,21:56:37 | INFO | Train Epoch: 1 [ 9472512/18327966 (52%)] Loss: 0.63602 (0.6494) Data (t): 0.001 Batch (t): 0.906, 563.911/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,21:58:08 | INFO | Train Epoch: 1 [ 9523712/18327966 (52%)] Loss: 0.77515 (0.6500) Data (t): 0.001 Batch (t): 0.906, 567.175/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,21:59:40 | INFO | Train Epoch: 1 [ 9574912/18327966 (52%)] Loss: 0.50690 (0.6493) Data (t): 0.001 Batch (t): 0.927, 566.719/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,22:01:14 | INFO | Train Epoch: 1 [ 9626112/18327966 (53%)] Loss: 0.63243 (0.6492) Data (t): 0.001 Batch (t): 0.940, 569.155/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,22:02:48 | INFO | Train Epoch: 1 [ 9677312/18327966 (53%)] Loss: 0.52737 (0.6486) Data (t): 0.001 Batch (t): 0.932, 564.761/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,22:04:19 | INFO | Train Epoch: 1 [ 9728512/18327966 (53%)] Loss: 0.77670 (0.6492) Data (t): 0.001 Batch (t): 0.915, 563.307/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,22:05:50 | INFO | Train Epoch: 1 [ 9779712/18327966 (53%)] Loss: 0.59840 (0.6490) Data (t): 0.001 Batch (t): 0.905, 566.331/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,22:07:21 | INFO | Train Epoch: 1 [ 9830912/18327966 (54%)] Loss: 0.64758 (0.6490) Data (t): 0.001 Batch (t): 0.914, 567.248/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,22:08:56 | INFO | Train Epoch: 1 [ 9882112/18327966 (54%)] Loss: 0.55875 (0.6485) Data (t): 0.001 Batch (t): 0.950, 570.034/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,22:10:29 | INFO | Train Epoch: 1 [ 9933312/18327966 (54%)] Loss: 0.70621 (0.6488) Data (t): 0.001 Batch (t): 0.933, 566.320/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,22:12:01 | INFO | Train Epoch: 1 [ 9984512/18327966 (54%)] Loss: 0.64708 (0.6488) Data (t): 0.001 Batch (t): 0.917, 564.132/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,22:13:32 | INFO | Train Epoch: 1 [10035712/18327966 (55%)] Loss: 0.63447 (0.6487) Data (t): 0.001 Batch (t): 0.905, 566.058/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,22:15:03 | INFO | Train Epoch: 1 [10086912/18327966 (55%)] Loss: 0.56636 (0.6483) Data (t): 0.001 Batch (t): 0.917, 566.528/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,22:16:36 | INFO | Train Epoch: 1 [10138112/18327966 (55%)] Loss: 0.48074 (0.6474) Data (t): 0.001 Batch (t): 0.929, 566.239/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,22:18:09 | INFO | Train Epoch: 1 [10189312/18327966 (56%)] Loss: 0.63467 (0.6474) Data (t): 0.001 Batch (t): 0.933, 563.741/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,22:19:42 | INFO | Train Epoch: 1 [10240512/18327966 (56%)] Loss: 0.66656 (0.6475) Data (t): 0.001 Batch (t): 0.927, 565.435/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,22:21:13 | INFO | Train Epoch: 1 [10291712/18327966 (56%)] Loss: 0.76620 (0.6481) Data (t): 0.001 Batch (t): 0.904, 564.781/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,22:22:43 | INFO | Train Epoch: 1 [10342912/18327966 (56%)] Loss: 0.55704 (0.6476) Data (t): 0.001 Batch (t): 0.905, 566.767/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,22:24:18 | INFO | Train Epoch: 1 [10394112/18327966 (57%)] Loss: 0.53938 (0.6471) Data (t): 0.001 Batch (t): 0.953, 567.029/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,22:25:52 | INFO | Train Epoch: 1 [10445312/18327966 (57%)] Loss: 0.60566 (0.6469) Data (t): 0.001 Batch (t): 0.935, 565.506/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,22:27:25 | INFO | Train Epoch: 1 [10496512/18327966 (57%)] Loss: 0.54707 (0.6464) Data (t): 0.001 Batch (t): 0.929, 565.963/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,22:28:55 | INFO | Train Epoch: 1 [10547712/18327966 (58%)] Loss: 0.57140 (0.6460) Data (t): 0.001 Batch (t): 0.907, 568.584/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,22:30:26 | INFO | Train Epoch: 1 [10598912/18327966 (58%)] Loss: 0.62857 (0.6460) Data (t): 0.001 Batch (t): 0.907, 563.687/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,22:31:59 | INFO | Train Epoch: 1 [10650112/18327966 (58%)] Loss: 0.64187 (0.6459) Data (t): 0.001 Batch (t): 0.931, 560.980/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,22:33:35 | INFO | Train Epoch: 1 [10701312/18327966 (58%)] Loss: 0.62641 (0.6458) Data (t): 0.001 Batch (t): 0.958, 565.186/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,22:35:08 | INFO | Train Epoch: 1 [10752512/18327966 (59%)] Loss: 0.62527 (0.6457) Data (t): 0.001 Batch (t): 0.929, 566.442/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,22:36:39 | INFO | Train Epoch: 1 [10803712/18327966 (59%)] Loss: 0.70934 (0.6460) Data (t): 0.001 Batch (t): 0.906, 563.479/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,22:38:09 | INFO | Train Epoch: 1 [10854912/18327966 (59%)] Loss: 0.61044 (0.6459) Data (t): 0.001 Batch (t): 0.906, 564.800/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,22:39:42 | INFO | Train Epoch: 1 [10906112/18327966 (60%)] Loss: 0.56456 (0.6455) Data (t): 0.001 Batch (t): 0.929, 565.366/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,22:41:16 | INFO | Train Epoch: 1 [10957312/18327966 (60%)] Loss: 0.56551 (0.6451) Data (t): 0.001 Batch (t): 0.941, 566.055/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,22:42:50 | INFO | Train Epoch: 1 [11008512/18327966 (60%)] Loss: 0.64141 (0.6451) Data (t): 0.001 Batch (t): 0.934, 566.741/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,22:44:21 | INFO | Train Epoch: 1 [11059712/18327966 (60%)] Loss: 0.52401 (0.6445) Data (t): 0.001 Batch (t): 0.918, 566.360/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,22:45:52 | INFO | Train Epoch: 1 [11110912/18327966 (61%)] Loss: 0.62651 (0.6445) Data (t): 0.001 Batch (t): 0.908, 566.489/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,22:47:24 | INFO | Train Epoch: 1 [11162112/18327966 (61%)] Loss: 0.58360 (0.6442) Data (t): 0.001 Batch (t): 0.920, 555.977/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,22:49:00 | INFO | Train Epoch: 1 [11213312/18327966 (61%)] Loss: 0.52143 (0.6436) Data (t): 0.001 Batch (t): 0.955, 567.329/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,22:50:33 | INFO | Train Epoch: 1 [11264512/18327966 (61%)] Loss: 0.59501 (0.6434) Data (t): 0.001 Batch (t): 0.935, 564.526/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,22:52:05 | INFO | Train Epoch: 1 [11315712/18327966 (62%)] Loss: 0.52995 (0.6429) Data (t): 0.001 Batch (t): 0.918, 567.731/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,22:53:36 | INFO | Train Epoch: 1 [11366912/18327966 (62%)] Loss: 0.53918 (0.6424) Data (t): 0.001 Batch (t): 0.908, 566.000/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,22:55:07 | INFO | Train Epoch: 1 [11418112/18327966 (62%)] Loss: 0.47604 (0.6417) Data (t): 0.001 Batch (t): 0.918, 567.074/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,22:56:41 | INFO | Train Epoch: 1 [11469312/18327966 (63%)] Loss: 0.45253 (0.6408) Data (t): 0.001 Batch (t): 0.940, 566.359/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,22:58:15 | INFO | Train Epoch: 1 [11520512/18327966 (63%)] Loss: 0.63707 (0.6408) Data (t): 0.001 Batch (t): 0.934, 564.964/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,22:59:48 | INFO | Train Epoch: 1 [11571712/18327966 (63%)] Loss: 0.71698 (0.6412) Data (t): 0.001 Batch (t): 0.930, 564.711/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,23:01:18 | INFO | Train Epoch: 1 [11622912/18327966 (63%)] Loss: 0.55920 (0.6408) Data (t): 0.001 Batch (t): 0.906, 563.277/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,23:02:50 | INFO | Train Epoch: 1 [11674112/18327966 (64%)] Loss: 0.59742 (0.6406) Data (t): 0.001 Batch (t): 0.918, 562.501/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,23:04:22 | INFO | Train Epoch: 1 [11725312/18327966 (64%)] Loss: 0.59644 (0.6404) Data (t): 0.001 Batch (t): 0.918, 563.517/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,23:05:58 | INFO | Train Epoch: 1 [11776512/18327966 (64%)] Loss: 0.66476 (0.6405) Data (t): 0.001 Batch (t): 0.958, 564.284/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,23:07:31 | INFO | Train Epoch: 1 [11827712/18327966 (65%)] Loss: 0.57817 (0.6403) Data (t): 0.001 Batch (t): 0.930, 569.308/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,23:09:02 | INFO | Train Epoch: 1 [11878912/18327966 (65%)] Loss: 0.57918 (0.6400) Data (t): 0.001 Batch (t): 0.907, 562.665/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,23:10:32 | INFO | Train Epoch: 1 [11930112/18327966 (65%)] Loss: 0.59766 (0.6398) Data (t): 0.001 Batch (t): 0.907, 564.726/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,23:12:05 | INFO | Train Epoch: 1 [11981312/18327966 (65%)] Loss: 0.58981 (0.6396) Data (t): 0.001 Batch (t): 0.931, 566.545/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,23:13:40 | INFO | Train Epoch: 1 [12032512/18327966 (66%)] Loss: 0.57325 (0.6393) Data (t): 0.001 Batch (t): 0.942, 563.796/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,23:15:13 | INFO | Train Epoch: 1 [12083712/18327966 (66%)] Loss: 0.54406 (0.6389) Data (t): 0.001 Batch (t): 0.935, 563.575/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,23:16:45 | INFO | Train Epoch: 1 [12134912/18327966 (66%)] Loss: 0.59485 (0.6387) Data (t): 0.001 Batch (t): 0.918, 565.211/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,23:18:16 | INFO | Train Epoch: 1 [12186112/18327966 (66%)] Loss: 0.55818 (0.6384) Data (t): 0.001 Batch (t): 0.908, 563.580/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,23:19:49 | INFO | Train Epoch: 1 [12237312/18327966 (67%)] Loss: 0.55091 (0.6380) Data (t): 0.001 Batch (t): 0.930, 563.744/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,23:21:23 | INFO | Train Epoch: 1 [12288512/18327966 (67%)] Loss: 0.49206 (0.6374) Data (t): 0.001 Batch (t): 0.941, 562.601/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,23:22:56 | INFO | Train Epoch: 1 [12339712/18327966 (67%)] Loss: 0.64453 (0.6375) Data (t): 0.001 Batch (t): 0.935, 563.958/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,23:24:28 | INFO | Train Epoch: 1 [12390912/18327966 (68%)] Loss: 0.67251 (0.6376) Data (t): 0.001 Batch (t): 0.918, 563.695/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,23:25:59 | INFO | Train Epoch: 1 [12442112/18327966 (68%)] Loss: 0.64847 (0.6376) Data (t): 0.001 Batch (t): 0.907, 563.726/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,23:27:31 | INFO | Train Epoch: 1 [12493312/18327966 (68%)] Loss: 0.55202 (0.6373) Data (t): 0.001 Batch (t): 0.919, 567.563/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,23:29:07 | INFO | Train Epoch: 1 [12544512/18327966 (68%)] Loss: 0.60519 (0.6372) Data (t): 0.001 Batch (t): 0.958, 566.667/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,23:30:40 | INFO | Train Epoch: 1 [12595712/18327966 (69%)] Loss: 0.60583 (0.6370) Data (t): 0.001 Batch (t): 0.933, 565.921/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,23:32:12 | INFO | Train Epoch: 1 [12646912/18327966 (69%)] Loss: 0.66310 (0.6371) Data (t): 0.001 Batch (t): 0.918, 565.215/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,23:33:42 | INFO | Train Epoch: 1 [12698112/18327966 (69%)] Loss: 0.68874 (0.6374) Data (t): 0.001 Batch (t): 0.905, 566.434/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,23:35:14 | INFO | Train Epoch: 1 [12749312/18327966 (70%)] Loss: 0.72270 (0.6377) Data (t): 0.001 Batch (t): 0.917, 564.601/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,23:36:48 | INFO | Train Epoch: 1 [12800512/18327966 (70%)] Loss: 0.73927 (0.6381) Data (t): 0.001 Batch (t): 0.942, 567.127/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,23:38:21 | INFO | Train Epoch: 1 [12851712/18327966 (70%)] Loss: 0.58253 (0.6379) Data (t): 0.001 Batch (t): 0.933, 565.233/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,23:39:54 | INFO | Train Epoch: 1 [12902912/18327966 (70%)] Loss: 0.50000 (0.6373) Data (t): 0.001 Batch (t): 0.928, 566.030/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,23:41:25 | INFO | Train Epoch: 1 [12954112/18327966 (71%)] Loss: 0.60744 (0.6372) Data (t): 0.001 Batch (t): 0.905, 566.555/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,23:42:55 | INFO | Train Epoch: 1 [13005312/18327966 (71%)] Loss: 0.48756 (0.6366) Data (t): 0.001 Batch (t): 0.906, 568.215/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,23:44:29 | INFO | Train Epoch: 1 [13056512/18327966 (71%)] Loss: 0.68641 (0.6368) Data (t): 0.001 Batch (t): 0.941, 241.061/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,23:46:04 | INFO | Train Epoch: 1 [13107712/18327966 (72%)] Loss: 0.66208 (0.6369) Data (t): 0.001 Batch (t): 0.946, 566.328/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,23:47:36 | INFO | Train Epoch: 1 [13158912/18327966 (72%)] Loss: 0.60111 (0.6368) Data (t): 0.001 Batch (t): 0.918, 564.147/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,23:49:08 | INFO | Train Epoch: 1 [13210112/18327966 (72%)] Loss: 0.66186 (0.6369) Data (t): 0.001 Batch (t): 0.918, 564.999/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,23:50:38 | INFO | Train Epoch: 1 [13261312/18327966 (72%)] Loss: 0.56596 (0.6366) Data (t): 0.001 Batch (t): 0.907, 568.483/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,23:52:11 | INFO | Train Epoch: 1 [13312512/18327966 (73%)] Loss: 0.55206 (0.6363) Data (t): 0.001 Batch (t): 0.929, 567.722/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,23:53:45 | INFO | Train Epoch: 1 [13363712/18327966 (73%)] Loss: 0.61122 (0.6362) Data (t): 0.001 Batch (t): 0.943, 563.517/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,23:55:19 | INFO | Train Epoch: 1 [13414912/18327966 (73%)] Loss: 0.66331 (0.6363) Data (t): 0.001 Batch (t): 0.935, 566.513/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,23:56:51 | INFO | Train Epoch: 1 [13466112/18327966 (73%)] Loss: 0.60067 (0.6362) Data (t): 0.001 Batch (t): 0.918, 566.643/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,23:58:22 | INFO | Train Epoch: 1 [13517312/18327966 (74%)] Loss: 0.72174 (0.6365) Data (t): 0.001 Batch (t): 0.907, 562.943/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,23:59:55 | INFO | Train Epoch: 1 [13568512/18327966 (74%)] Loss: 0.60255 (0.6364) Data (t): 0.001 Batch (t): 0.931, 564.574/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,00:01:30 | INFO | Train Epoch: 1 [13619712/18327966 (74%)] Loss: 0.47904 (0.6358) Data (t): 0.001 Batch (t): 0.950, 565.078/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,00:03:03 | INFO | Train Epoch: 1 [13670912/18327966 (75%)] Loss: 0.58561 (0.6356) Data (t): 0.001 Batch (t): 0.935, 566.414/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,00:04:35 | INFO | Train Epoch: 1 [13722112/18327966 (75%)] Loss: 0.62522 (0.6355) Data (t): 0.001 Batch (t): 0.918, 566.341/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,00:06:06 | INFO | Train Epoch: 1 [13773312/18327966 (75%)] Loss: 0.52981 (0.6351) Data (t): 0.001 Batch (t): 0.907, 563.919/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,00:07:39 | INFO | Train Epoch: 1 [13824512/18327966 (75%)] Loss: 0.49145 (0.6346) Data (t): 0.001 Batch (t): 0.932, 238.319/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,00:09:13 | INFO | Train Epoch: 1 [13875712/18327966 (76%)] Loss: 0.73330 (0.6350) Data (t): 0.001 Batch (t): 0.943, 565.803/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,00:10:45 | INFO | Train Epoch: 1 [13926912/18327966 (76%)] Loss: 0.65518 (0.6351) Data (t): 0.001 Batch (t): 0.924, 564.238/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,00:12:19 | INFO | Train Epoch: 1 [13978112/18327966 (76%)] Loss: 0.71273 (0.6353) Data (t): 0.001 Batch (t): 0.932, 567.331/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,00:13:49 | INFO | Train Epoch: 1 [14029312/18327966 (77%)] Loss: 0.56057 (0.6351) Data (t): 0.001 Batch (t): 0.907, 563.982/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,00:15:21 | INFO | Train Epoch: 1 [14080512/18327966 (77%)] Loss: 0.45363 (0.6344) Data (t): 0.001 Batch (t): 0.919, 564.139/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,00:16:56 | INFO | Train Epoch: 1 [14131712/18327966 (77%)] Loss: 0.68529 (0.6346) Data (t): 0.001 Batch (t): 0.943, 562.574/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,00:18:29 | INFO | Train Epoch: 1 [14182912/18327966 (77%)] Loss: 0.63252 (0.6346) Data (t): 0.001 Batch (t): 0.936, 566.938/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,00:20:01 | INFO | Train Epoch: 1 [14234112/18327966 (78%)] Loss: 0.49294 (0.6341) Data (t): 0.001 Batch (t): 0.919, 564.205/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,00:21:33 | INFO | Train Epoch: 1 [14285312/18327966 (78%)] Loss: 0.60964 (0.6340) Data (t): 0.001 Batch (t): 0.920, 561.868/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,00:23:04 | INFO | Train Epoch: 1 [14336512/18327966 (78%)] Loss: 0.68276 (0.6342) Data (t): 0.001 Batch (t): 0.906, 561.991/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,00:24:37 | INFO | Train Epoch: 1 [14387712/18327966 (79%)] Loss: 0.59664 (0.6340) Data (t): 0.001 Batch (t): 0.931, 565.503/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,00:26:11 | INFO | Train Epoch: 1 [14438912/18327966 (79%)] Loss: 0.67370 (0.6342) Data (t): 0.001 Batch (t): 0.943, 561.359/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,00:27:45 | INFO | Train Epoch: 1 [14490112/18327966 (79%)] Loss: 0.55850 (0.6339) Data (t): 0.001 Batch (t): 0.937, 564.478/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,00:29:16 | INFO | Train Epoch: 1 [14541312/18327966 (79%)] Loss: 0.59934 (0.6338) Data (t): 0.001 Batch (t): 0.917, 565.602/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,00:30:47 | INFO | Train Epoch: 1 [14592512/18327966 (80%)] Loss: 0.56242 (0.6335) Data (t): 0.001 Batch (t): 0.907, 565.457/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,00:32:20 | INFO | Train Epoch: 1 [14643712/18327966 (80%)] Loss: 0.69004 (0.6337) Data (t): 0.001 Batch (t): 0.931, 565.746/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,00:33:56 | INFO | Train Epoch: 1 [14694912/18327966 (80%)] Loss: 0.56424 (0.6335) Data (t): 0.001 Batch (t): 0.955, 565.084/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,00:35:29 | INFO | Train Epoch: 1 [14746112/18327966 (80%)] Loss: 0.58378 (0.6333) Data (t): 0.001 Batch (t): 0.937, 564.110/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,00:37:01 | INFO | Train Epoch: 1 [14797312/18327966 (81%)] Loss: 0.63065 (0.6333) Data (t): 0.001 Batch (t): 0.919, 564.657/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,00:38:32 | INFO | Train Epoch: 1 [14848512/18327966 (81%)] Loss: 0.75931 (0.6337) Data (t): 0.001 Batch (t): 0.906, 566.739/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,00:40:05 | INFO | Train Epoch: 1 [14899712/18327966 (81%)] Loss: 0.61691 (0.6337) Data (t): 0.001 Batch (t): 0.932, 563.727/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,00:41:39 | INFO | Train Epoch: 1 [14950912/18327966 (82%)] Loss: 0.65024 (0.6337) Data (t): 0.001 Batch (t): 0.944, 565.728/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,00:43:12 | INFO | Train Epoch: 1 [15002112/18327966 (82%)] Loss: 0.55830 (0.6335) Data (t): 0.001 Batch (t): 0.924, 564.808/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,00:44:45 | INFO | Train Epoch: 1 [15053312/18327966 (82%)] Loss: 0.76840 (0.6339) Data (t): 0.001 Batch (t): 0.932, 563.664/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,00:46:16 | INFO | Train Epoch: 1 [15104512/18327966 (82%)] Loss: 0.55041 (0.6337) Data (t): 0.001 Batch (t): 0.907, 565.560/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,00:47:48 | INFO | Train Epoch: 1 [15155712/18327966 (83%)] Loss: 0.64533 (0.6337) Data (t): 0.001 Batch (t): 0.918, 566.285/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,00:49:22 | INFO | Train Epoch: 1 [15206912/18327966 (83%)] Loss: 0.53453 (0.6334) Data (t): 0.001 Batch (t): 0.945, 560.221/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,00:50:56 | INFO | Train Epoch: 1 [15258112/18327966 (83%)] Loss: 0.61992 (0.6333) Data (t): 0.001 Batch (t): 0.936, 566.184/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,00:52:27 | INFO | Train Epoch: 1 [15309312/18327966 (84%)] Loss: 0.62095 (0.6333) Data (t): 0.001 Batch (t): 0.918, 563.231/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,00:53:59 | INFO | Train Epoch: 1 [15360512/18327966 (84%)] Loss: 0.52236 (0.6329) Data (t): 0.001 Batch (t): 0.920, 568.405/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,00:55:31 | INFO | Train Epoch: 1 [15411712/18327966 (84%)] Loss: 0.70047 (0.6331) Data (t): 0.001 Batch (t): 0.918, 567.257/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,00:57:04 | INFO | Train Epoch: 1 [15462912/18327966 (84%)] Loss: 0.51668 (0.6327) Data (t): 0.001 Batch (t): 0.931, 559.693/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,00:58:39 | INFO | Train Epoch: 1 [15514112/18327966 (85%)] Loss: 0.59458 (0.6326) Data (t): 0.001 Batch (t): 0.947, 567.338/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,01:00:11 | INFO | Train Epoch: 1 [15565312/18327966 (85%)] Loss: 0.67139 (0.6327) Data (t): 0.001 Batch (t): 0.917, 564.134/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,01:01:42 | INFO | Train Epoch: 1 [15616512/18327966 (85%)] Loss: 0.58265 (0.6326) Data (t): 0.001 Batch (t): 0.917, 565.786/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,01:03:14 | INFO | Train Epoch: 1 [15667712/18327966 (85%)] Loss: 0.58033 (0.6324) Data (t): 0.001 Batch (t): 0.917, 563.813/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,01:04:46 | INFO | Train Epoch: 1 [15718912/18327966 (86%)] Loss: 0.51994 (0.6321) Data (t): 0.001 Batch (t): 0.918, 567.993/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,01:06:20 | INFO | Train Epoch: 1 [15770112/18327966 (86%)] Loss: 0.76517 (0.6325) Data (t): 0.001 Batch (t): 0.944, 568.264/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,01:07:54 | INFO | Train Epoch: 1 [15821312/18327966 (86%)] Loss: 0.49818 (0.6320) Data (t): 0.001 Batch (t): 0.936, 566.252/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,01:09:26 | INFO | Train Epoch: 1 [15872512/18327966 (87%)] Loss: 0.52016 (0.6317) Data (t): 0.001 Batch (t): 0.920, 566.940/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,01:10:57 | INFO | Train Epoch: 1 [15923712/18327966 (87%)] Loss: 0.63283 (0.6317) Data (t): 0.001 Batch (t): 0.908, 566.270/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,01:12:30 | INFO | Train Epoch: 1 [15974912/18327966 (87%)] Loss: 0.54762 (0.6314) Data (t): 0.001 Batch (t): 0.931, 566.184/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,01:14:04 | INFO | Train Epoch: 1 [16026112/18327966 (87%)] Loss: 0.58539 (0.6313) Data (t): 0.001 Batch (t): 0.943, 564.741/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,01:15:38 | INFO | Train Epoch: 1 [16077312/18327966 (88%)] Loss: 0.66682 (0.6314) Data (t): 0.001 Batch (t): 0.936, 562.807/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,01:17:10 | INFO | Train Epoch: 1 [16128512/18327966 (88%)] Loss: 0.62758 (0.6314) Data (t): 0.001 Batch (t): 0.918, 565.468/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,01:18:40 | INFO | Train Epoch: 1 [16179712/18327966 (88%)] Loss: 0.60154 (0.6313) Data (t): 0.001 Batch (t): 0.906, 568.635/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,01:20:13 | INFO | Train Epoch: 1 [16230912/18327966 (89%)] Loss: 0.56968 (0.6311) Data (t): 0.001 Batch (t): 0.931, 564.613/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,01:21:49 | INFO | Train Epoch: 1 [16282112/18327966 (89%)] Loss: 0.69086 (0.6313) Data (t): 0.001 Batch (t): 0.954, 562.640/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,01:23:21 | INFO | Train Epoch: 1 [16333312/18327966 (89%)] Loss: 0.65991 (0.6314) Data (t): 0.001 Batch (t): 0.924, 564.676/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,01:24:53 | INFO | Train Epoch: 1 [16384512/18327966 (89%)] Loss: 0.70238 (0.6316) Data (t): 0.001 Batch (t): 0.918, 562.123/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,01:26:25 | INFO | Train Epoch: 1 [16435712/18327966 (90%)] Loss: 0.69493 (0.6318) Data (t): 0.001 Batch (t): 0.920, 562.655/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,01:27:57 | INFO | Train Epoch: 1 [16486912/18327966 (90%)] Loss: 0.61897 (0.6317) Data (t): 0.001 Batch (t): 0.920, 562.413/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,01:29:31 | INFO | Train Epoch: 1 [16538112/18327966 (90%)] Loss: 0.51919 (0.6314) Data (t): 0.001 Batch (t): 0.945, 565.981/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,01:31:05 | INFO | Train Epoch: 1 [16589312/18327966 (91%)] Loss: 0.64378 (0.6314) Data (t): 0.001 Batch (t): 0.937, 564.099/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,01:32:37 | INFO | Train Epoch: 1 [16640512/18327966 (91%)] Loss: 0.57328 (0.6313) Data (t): 0.001 Batch (t): 0.920, 565.710/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,01:34:09 | INFO | Train Epoch: 1 [16691712/18327966 (91%)] Loss: 0.54121 (0.6310) Data (t): 0.001 Batch (t): 0.920, 565.665/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,01:35:41 | INFO | Train Epoch: 1 [16742912/18327966 (91%)] Loss: 0.68124 (0.6311) Data (t): 0.001 Batch (t): 0.918, 564.128/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,01:37:14 | INFO | Train Epoch: 1 [16794112/18327966 (92%)] Loss: 0.63612 (0.6312) Data (t): 0.001 Batch (t): 0.931, 567.523/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,01:38:47 | INFO | Train Epoch: 1 [16845312/18327966 (92%)] Loss: 0.59370 (0.6310) Data (t): 0.001 Batch (t): 0.931, 565.128/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,01:40:21 | INFO | Train Epoch: 1 [16896512/18327966 (92%)] Loss: 0.64706 (0.6311) Data (t): 0.001 Batch (t): 0.938, 565.001/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,01:41:53 | INFO | Train Epoch: 1 [16947712/18327966 (92%)] Loss: 0.68760 (0.6313) Data (t): 0.001 Batch (t): 0.920, 564.095/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,01:43:25 | INFO | Train Epoch: 1 [16998912/18327966 (93%)] Loss: 0.69510 (0.6314) Data (t): 0.001 Batch (t): 0.919, 564.252/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,01:44:57 | INFO | Train Epoch: 1 [17050112/18327966 (93%)] Loss: 0.64377 (0.6315) Data (t): 0.001 Batch (t): 0.920, 562.766/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,01:46:31 | INFO | Train Epoch: 1 [17101312/18327966 (93%)] Loss: 0.60436 (0.6314) Data (t): 0.001 Batch (t): 0.945, 564.852/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,01:48:05 | INFO | Train Epoch: 1 [17152512/18327966 (94%)] Loss: 0.56926 (0.6312) Data (t): 0.001 Batch (t): 0.937, 566.780/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,01:49:37 | INFO | Train Epoch: 1 [17203712/18327966 (94%)] Loss: 0.55406 (0.6310) Data (t): 0.001 Batch (t): 0.920, 564.817/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,01:51:08 | INFO | Train Epoch: 1 [17254912/18327966 (94%)] Loss: 0.56896 (0.6308) Data (t): 0.001 Batch (t): 0.907, 565.649/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,01:52:41 | INFO | Train Epoch: 1 [17306112/18327966 (94%)] Loss: 0.58121 (0.6307) Data (t): 0.001 Batch (t): 0.932, 564.905/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,01:54:17 | INFO | Train Epoch: 1 [17357312/18327966 (95%)] Loss: 0.49593 (0.6303) Data (t): 0.001 Batch (t): 0.956, 568.497/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,01:55:49 | INFO | Train Epoch: 1 [17408512/18327966 (95%)] Loss: 0.57704 (0.6301) Data (t): 0.001 Batch (t): 0.922, 562.922/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,01:57:21 | INFO | Train Epoch: 1 [17459712/18327966 (95%)] Loss: 0.55594 (0.6299) Data (t): 0.001 Batch (t): 0.918, 564.685/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,01:58:53 | INFO | Train Epoch: 1 [17510912/18327966 (96%)] Loss: 0.66007 (0.6300) Data (t): 0.001 Batch (t): 0.918, 568.542/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,02:00:26 | INFO | Train Epoch: 1 [17562112/18327966 (96%)] Loss: 0.64127 (0.6300) Data (t): 0.001 Batch (t): 0.931, 561.582/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,02:01:59 | INFO | Train Epoch: 1 [17613312/18327966 (96%)] Loss: 0.62350 (0.6300) Data (t): 0.001 Batch (t): 0.931, 566.550/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,02:03:32 | INFO | Train Epoch: 1 [17664512/18327966 (96%)] Loss: 0.67588 (0.6301) Data (t): 0.001 Batch (t): 0.935, 566.689/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,02:05:04 | INFO | Train Epoch: 1 [17715712/18327966 (97%)] Loss: 0.61091 (0.6301) Data (t): 0.001 Batch (t): 0.918, 567.508/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,02:06:36 | INFO | Train Epoch: 1 [17766912/18327966 (97%)] Loss: 0.47224 (0.6296) Data (t): 0.001 Batch (t): 0.919, 564.245/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,02:08:08 | INFO | Train Epoch: 1 [17818112/18327966 (97%)] Loss: 0.57546 (0.6295) Data (t): 0.001 Batch (t): 0.918, 563.940/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,02:09:42 | INFO | Train Epoch: 1 [17869312/18327966 (97%)] Loss: 0.53677 (0.6292) Data (t): 0.001 Batch (t): 0.948, 564.113/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,02:11:16 | INFO | Train Epoch: 1 [17920512/18327966 (98%)] Loss: 0.57408 (0.6290) Data (t): 0.001 Batch (t): 0.936, 564.835/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,02:12:48 | INFO | Train Epoch: 1 [17971712/18327966 (98%)] Loss: 0.53781 (0.6288) Data (t): 0.001 Batch (t): 0.916, 566.088/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,02:14:19 | INFO | Train Epoch: 1 [18022912/18327966 (98%)] Loss: 0.68609 (0.6289) Data (t): 0.001 Batch (t): 0.916, 565.192/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,02:15:51 | INFO | Train Epoch: 1 [18074112/18327966 (99%)] Loss: 0.58021 (0.6288) Data (t): 0.001 Batch (t): 0.916, 567.324/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,02:17:23 | INFO | Train Epoch: 1 [18125312/18327966 (99%)] Loss: 0.57150 (0.6286) Data (t): 0.001 Batch (t): 0.916, 567.704/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,02:18:57 | INFO | Train Epoch: 1 [18176512/18327966 (99%)] Loss: 0.69910 (0.6288) Data (t): 0.001 Batch (t): 0.942, 564.296/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,02:20:30 | INFO | Train Epoch: 1 [18227712/18327966 (99%)] Loss: 0.56923 (0.6287) Data (t): 0.001 Batch (t): 0.935, 568.901/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,02:22:02 | INFO | Train Epoch: 1 [18278912/18327966 (100%)] Loss: 0.55995 (0.6285) Data (t): 0.001 Batch (t): 0.915, 567.338/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-28,02:23:29 | INFO | Train Epoch: 1 [18327552/18327966 (100%)] Loss: 0.59918 (0.6284) Data (t): 0.002 Batch (t): 0.916, 568.908/s LR: 0.000000 Logit Scale: 100.000 - V4 diff --git a/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten_decimal2/2024_11_27-07_57_39-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/params.txt b/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten_decimal2/2024_11_27-07_57_39-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/params.txt deleted file mode 100644 index 0629546c5061712f770c39a7589ea11d0f5a5667..0000000000000000000000000000000000000000 --- a/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten_decimal2/2024_11_27-07_57_39-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/params.txt +++ /dev/null @@ -1,67 +0,0 @@ -batch_size: 64 -beta1: 0.9 -beta2: 0.98 -checkpoint_path: data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten_decimal2/2024_11_27-07_57_39-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/checkpoints -copy_codebase: False -csv_caption_key: caption -csv_hard_captions_key: neg_caption -csv_img_key: img_path -csv_separator: , -dataset_resampled: False -dataset_type: csv -ddp_static_graph: False -debug: False -device: cuda:0 -dist_backend: nccl -dist_url: env:// -distributed: True -epochs: 2 -eps: 1e-06 -force_quick_gelu: True -gather_with_grad: False -grad_checkpointing: False -horovod: False -imagenet_v2: None -imagenet_val: None -local_loss: False -local_rank: 0 -lock_image: False -lock_image_freeze_bn_stats: False -lock_image_unlocked_groups: 0 -log_level: 20 -log_local: False -log_path: data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten_decimal2/2024_11_27-07_57_39-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/out.log -logs: data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten_decimal2 -lr: 5e-06 -model: ViT-L-14-336 -name: 2024_11_27-07_57_39-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp -no_set_device_rank: False -norm_gradient_clip: None -precision: amp -pretrained: data/openclip-vit-14-336/openclip_model.pt -pretrained_image: False -rank: 0 -report_to: wandb -resume: None -save_frequency: 1 -save_most_recent: False -seed: 0 -skip_scheduler: False -tensorboard: False -tensorboard_path: -torchscript: False -trace: False -train_data: csv_data/plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten_decimal2.csv -train_num_samples: None -use_bn_sync: False -val_data: None -val_frequency: 1 -val_num_samples: None -wandb: True -wandb_notes: -wandb_project: neg-clip-plotqa_train_only_qa_v2_10false_formated_sampled_fixed_flaten_decimal2 -warmup: 0 -wd: 0.1 -workers: 4 -world_size: 8 -zeroshot_frequency: 2 diff --git a/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten/2024_11_26-13_26_16-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/checkpoints/epoch_1.pt b/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten/2024_11_26-13_26_16-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/checkpoints/epoch_1.pt deleted file mode 100644 index a2f0ef9d10ee17dd288ba83a11ac4721a04f5bc1..0000000000000000000000000000000000000000 --- a/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten/2024_11_26-13_26_16-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/checkpoints/epoch_1.pt +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:9e22bb5909ea41fc358ba46f8e7f9363a9aec6735fd566d004146c22ac1e3a9f -size 5135890710 diff --git a/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten/2024_11_26-13_26_16-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/checkpoints/epoch_2.pt b/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten/2024_11_26-13_26_16-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/checkpoints/epoch_2.pt deleted file mode 100644 index 97e58130f65549ba84da76d91e9e38f4161ee047..0000000000000000000000000000000000000000 --- a/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten/2024_11_26-13_26_16-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/checkpoints/epoch_2.pt +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:b7300879c8e9fba78862ad210f65a25569c07e4cf54ef486b2a8f03f51239b15 -size 5135890710 diff --git a/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten/2024_11_26-13_26_16-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/out.log b/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten/2024_11_26-13_26_16-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/out.log deleted file mode 100644 index f9c51e5708c614de453846543c98336aae1c6caa..0000000000000000000000000000000000000000 --- a/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten/2024_11_26-13_26_16-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/out.log +++ /dev/null @@ -1,534 +0,0 @@ -2024-11-26,13:26:17 | INFO | Running in distributed mode with multiple processes. Device: cuda:0.Process (global: 0, local 0), total 8. -2024-11-26,13:26:17 | INFO | Loading ViT-L-14-336 model config. -2024-11-26,13:26:20 | INFO | Loading pretrained ViT-L-14-336 weights (data/openclip-vit-14-336/openclip_model.pt). -2024-11-26,13:26:28 | INFO | Model: -2024-11-26,13:26:28 | INFO | CLIP( - (visual): VisualTransformer( - (conv1): Conv2d(3, 1024, kernel_size=(14, 14), stride=(14, 14), bias=False) - (ln_pre): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) - (transformer): Transformer( - (resblocks): ModuleList( - (0-23): 24 x ResidualAttentionBlock( - (attn): MultiheadAttention( - (out_proj): NonDynamicallyQuantizableLinear(in_features=1024, out_features=1024, bias=True) - ) - (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) - (mlp): Sequential( - (c_fc): Linear(in_features=1024, out_features=4096, bias=True) - (gelu): QuickGELU() - (c_proj): Linear(in_features=4096, out_features=1024, bias=True) - ) - (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) - ) - ) - ) - (ln_post): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) - ) - (transformer): Transformer( - (resblocks): ModuleList( - (0-11): 12 x ResidualAttentionBlock( - (attn): MultiheadAttention( - (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) - ) - (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) - (mlp): Sequential( - (c_fc): Linear(in_features=768, out_features=3072, bias=True) - (gelu): QuickGELU() - (c_proj): Linear(in_features=3072, out_features=768, bias=True) - ) - (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) - ) - ) - ) - (token_embedding): Embedding(49408, 768) - (ln_final): LayerNorm((768,), eps=1e-05, elementwise_affine=True) -) -2024-11-26,13:26:28 | INFO | Params: -2024-11-26,13:26:28 | INFO | batch_size: 64 -2024-11-26,13:26:28 | INFO | beta1: 0.9 -2024-11-26,13:26:28 | INFO | beta2: 0.98 -2024-11-26,13:26:28 | INFO | checkpoint_path: data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten/2024_11_26-13_26_16-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/checkpoints -2024-11-26,13:26:28 | INFO | copy_codebase: False -2024-11-26,13:26:28 | INFO | csv_caption_key: caption -2024-11-26,13:26:28 | INFO | csv_hard_captions_key: neg_caption -2024-11-26,13:26:28 | INFO | csv_img_key: img_path -2024-11-26,13:26:28 | INFO | csv_separator: , -2024-11-26,13:26:28 | INFO | dataset_resampled: False -2024-11-26,13:26:28 | INFO | dataset_type: csv -2024-11-26,13:26:28 | INFO | ddp_static_graph: False -2024-11-26,13:26:28 | INFO | debug: False -2024-11-26,13:26:28 | INFO | device: cuda:0 -2024-11-26,13:26:28 | INFO | dist_backend: nccl -2024-11-26,13:26:28 | INFO | dist_url: env:// -2024-11-26,13:26:28 | INFO | distributed: True -2024-11-26,13:26:28 | INFO | epochs: 2 -2024-11-26,13:26:28 | INFO | eps: 1e-06 -2024-11-26,13:26:28 | INFO | force_quick_gelu: True -2024-11-26,13:26:28 | INFO | gather_with_grad: False -2024-11-26,13:26:28 | INFO | grad_checkpointing: False -2024-11-26,13:26:28 | INFO | horovod: False -2024-11-26,13:26:28 | INFO | imagenet_v2: None -2024-11-26,13:26:28 | INFO | imagenet_val: None -2024-11-26,13:26:28 | INFO | local_loss: False -2024-11-26,13:26:28 | INFO | local_rank: 0 -2024-11-26,13:26:28 | INFO | lock_image: False -2024-11-26,13:26:28 | INFO | lock_image_freeze_bn_stats: False -2024-11-26,13:26:28 | INFO | lock_image_unlocked_groups: 0 -2024-11-26,13:26:28 | INFO | log_level: 20 -2024-11-26,13:26:28 | INFO | log_local: False -2024-11-26,13:26:28 | INFO | log_path: data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten/2024_11_26-13_26_16-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/out.log -2024-11-26,13:26:28 | INFO | logs: data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten -2024-11-26,13:26:28 | INFO | lr: 1e-06 -2024-11-26,13:26:28 | INFO | model: ViT-L-14-336 -2024-11-26,13:26:28 | INFO | name: 2024_11_26-13_26_16-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp -2024-11-26,13:26:28 | INFO | no_set_device_rank: False -2024-11-26,13:26:28 | INFO | norm_gradient_clip: None -2024-11-26,13:26:28 | INFO | precision: amp -2024-11-26,13:26:28 | INFO | pretrained: data/openclip-vit-14-336/openclip_model.pt -2024-11-26,13:26:28 | INFO | pretrained_image: False -2024-11-26,13:26:28 | INFO | rank: 0 -2024-11-26,13:26:28 | INFO | report_to: wandb -2024-11-26,13:26:28 | INFO | resume: None -2024-11-26,13:26:28 | INFO | save_frequency: 1 -2024-11-26,13:26:28 | INFO | save_most_recent: False -2024-11-26,13:26:28 | INFO | seed: 0 -2024-11-26,13:26:28 | INFO | skip_scheduler: False -2024-11-26,13:26:28 | INFO | tensorboard: False -2024-11-26,13:26:28 | INFO | tensorboard_path: -2024-11-26,13:26:28 | INFO | torchscript: False -2024-11-26,13:26:28 | INFO | trace: False -2024-11-26,13:26:28 | INFO | train_data: csv_data/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten.csv -2024-11-26,13:26:28 | INFO | train_num_samples: None -2024-11-26,13:26:28 | INFO | use_bn_sync: False -2024-11-26,13:26:28 | INFO | val_data: None -2024-11-26,13:26:28 | INFO | val_frequency: 1 -2024-11-26,13:26:28 | INFO | val_num_samples: None -2024-11-26,13:26:28 | INFO | wandb: True -2024-11-26,13:26:28 | INFO | wandb_notes: -2024-11-26,13:26:28 | INFO | wandb_project: neg-clip-plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten -2024-11-26,13:26:28 | INFO | warmup: 0 -2024-11-26,13:26:28 | INFO | wd: 0.1 -2024-11-26,13:26:28 | INFO | workers: 4 -2024-11-26,13:26:28 | INFO | world_size: 8 -2024-11-26,13:26:28 | INFO | zeroshot_frequency: 2 -2024-11-26,13:27:23 | INFO | Init a wandb project! -2024-11-26,13:27:29 | INFO | Start epoch 0 -2024-11-26,13:27:36 | INFO | Train Epoch: 0 [ 512/10637090 (0%)] Loss: 5.5496 (5.550) Data (t): 2.793 Batch (t): 6.830, 74.9673/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,13:29:08 | INFO | Train Epoch: 0 [ 51712/10637090 (0%)] Loss: 2.6938 (4.122) Data (t): 0.001 Batch (t): 0.913, 561.888/s LR: 0.000001 Logit Scale: 99.998 - V4 -2024-11-26,13:30:38 | INFO | Train Epoch: 0 [ 102912/10637090 (1%)] Loss: 2.2569 (3.500) Data (t): 0.001 Batch (t): 0.909, 562.911/s LR: 0.000001 Logit Scale: 99.998 - V4 -2024-11-26,13:32:10 | INFO | Train Epoch: 0 [ 154112/10637090 (1%)] Loss: 2.0755 (3.144) Data (t): 0.001 Batch (t): 0.915, 563.167/s LR: 0.000001 Logit Scale: 99.998 - V4 -2024-11-26,13:33:45 | INFO | Train Epoch: 0 [ 205312/10637090 (2%)] Loss: 2.0623 (2.928) Data (t): 0.001 Batch (t): 0.948, 562.861/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,13:35:16 | INFO | Train Epoch: 0 [ 256512/10637090 (2%)] Loss: 1.8247 (2.744) Data (t): 0.001 Batch (t): 0.908, 564.203/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,13:36:46 | INFO | Train Epoch: 0 [ 307712/10637090 (3%)] Loss: 1.7445 (2.601) Data (t): 0.001 Batch (t): 0.908, 566.755/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,13:38:17 | INFO | Train Epoch: 0 [ 358912/10637090 (3%)] Loss: 1.8082 (2.502) Data (t): 0.001 Batch (t): 0.908, 565.794/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,13:39:48 | INFO | Train Epoch: 0 [ 410112/10637090 (4%)] Loss: 1.7109 (2.414) Data (t): 0.001 Batch (t): 0.913, 566.573/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,13:41:24 | INFO | Train Epoch: 0 [ 461312/10637090 (4%)] Loss: 1.5298 (2.326) Data (t): 0.001 Batch (t): 0.955, 567.027/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,13:42:55 | INFO | Train Epoch: 0 [ 512512/10637090 (5%)] Loss: 1.6165 (2.261) Data (t): 0.001 Batch (t): 0.906, 563.278/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,13:44:25 | INFO | Train Epoch: 0 [ 563712/10637090 (5%)] Loss: 1.5626 (2.203) Data (t): 0.001 Batch (t): 0.907, 565.448/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,13:45:56 | INFO | Train Epoch: 0 [ 614912/10637090 (6%)] Loss: 1.6279 (2.159) Data (t): 0.001 Batch (t): 0.906, 566.904/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,13:47:26 | INFO | Train Epoch: 0 [ 666112/10637090 (6%)] Loss: 1.5003 (2.112) Data (t): 0.001 Batch (t): 0.907, 565.359/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,13:49:01 | INFO | Train Epoch: 0 [ 717312/10637090 (7%)] Loss: 1.4429 (2.067) Data (t): 0.001 Batch (t): 0.946, 567.412/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,13:50:32 | INFO | Train Epoch: 0 [ 768512/10637090 (7%)] Loss: 1.6723 (2.042) Data (t): 0.001 Batch (t): 0.906, 562.600/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,13:52:02 | INFO | Train Epoch: 0 [ 819712/10637090 (8%)] Loss: 1.5646 (2.014) Data (t): 0.001 Batch (t): 0.906, 566.678/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,13:53:33 | INFO | Train Epoch: 0 [ 870912/10637090 (8%)] Loss: 1.4842 (1.985) Data (t): 0.001 Batch (t): 0.906, 563.976/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,13:55:03 | INFO | Train Epoch: 0 [ 922112/10637090 (9%)] Loss: 1.6805 (1.969) Data (t): 0.001 Batch (t): 0.906, 564.824/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,13:56:40 | INFO | Train Epoch: 0 [ 973312/10637090 (9%)] Loss: 1.5527 (1.948) Data (t): 0.001 Batch (t): 0.965, 563.200/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,13:58:11 | INFO | Train Epoch: 0 [ 1024512/10637090 (10%)] Loss: 1.4393 (1.924) Data (t): 0.001 Batch (t): 0.906, 563.626/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,13:59:41 | INFO | Train Epoch: 0 [ 1075712/10637090 (10%)] Loss: 1.5999 (1.909) Data (t): 0.001 Batch (t): 0.906, 564.455/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:01:12 | INFO | Train Epoch: 0 [ 1126912/10637090 (11%)] Loss: 1.4600 (1.890) Data (t): 0.001 Batch (t): 0.906, 566.176/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:02:42 | INFO | Train Epoch: 0 [ 1178112/10637090 (11%)] Loss: 1.4896 (1.873) Data (t): 0.001 Batch (t): 0.906, 565.815/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:04:17 | INFO | Train Epoch: 0 [ 1229312/10637090 (12%)] Loss: 1.5681 (1.861) Data (t): 0.001 Batch (t): 0.946, 565.761/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:05:50 | INFO | Train Epoch: 0 [ 1280512/10637090 (12%)] Loss: 1.4004 (1.843) Data (t): 0.001 Batch (t): 0.926, 565.760/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:07:20 | INFO | Train Epoch: 0 [ 1331712/10637090 (13%)] Loss: 1.4362 (1.828) Data (t): 0.001 Batch (t): 0.906, 562.928/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:08:51 | INFO | Train Epoch: 0 [ 1382912/10637090 (13%)] Loss: 1.4948 (1.816) Data (t): 0.001 Batch (t): 0.906, 565.679/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:10:22 | INFO | Train Epoch: 0 [ 1434112/10637090 (13%)] Loss: 1.5365 (1.806) Data (t): 0.001 Batch (t): 0.907, 565.107/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:11:53 | INFO | Train Epoch: 0 [ 1485312/10637090 (14%)] Loss: 1.5180 (1.797) Data (t): 0.001 Batch (t): 0.912, 565.333/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:13:29 | INFO | Train Epoch: 0 [ 1536512/10637090 (14%)] Loss: 1.6162 (1.791) Data (t): 0.001 Batch (t): 0.961, 565.295/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:15:00 | INFO | Train Epoch: 0 [ 1587712/10637090 (15%)] Loss: 1.4370 (1.780) Data (t): 0.001 Batch (t): 0.907, 565.087/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:16:30 | INFO | Train Epoch: 0 [ 1638912/10637090 (15%)] Loss: 1.4059 (1.769) Data (t): 0.001 Batch (t): 0.906, 565.510/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:18:01 | INFO | Train Epoch: 0 [ 1690112/10637090 (16%)] Loss: 1.5552 (1.762) Data (t): 0.001 Batch (t): 0.906, 564.604/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:19:32 | INFO | Train Epoch: 0 [ 1741312/10637090 (16%)] Loss: 1.3878 (1.752) Data (t): 0.001 Batch (t): 0.912, 565.169/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:21:08 | INFO | Train Epoch: 0 [ 1792512/10637090 (17%)] Loss: 1.3488 (1.740) Data (t): 0.001 Batch (t): 0.965, 563.128/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:22:39 | INFO | Train Epoch: 0 [ 1843712/10637090 (17%)] Loss: 1.5331 (1.735) Data (t): 0.001 Batch (t): 0.907, 563.040/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:24:10 | INFO | Train Epoch: 0 [ 1894912/10637090 (18%)] Loss: 1.4770 (1.728) Data (t): 0.001 Batch (t): 0.907, 564.176/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:25:40 | INFO | Train Epoch: 0 [ 1946112/10637090 (18%)] Loss: 1.4729 (1.721) Data (t): 0.001 Batch (t): 0.906, 565.956/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:27:11 | INFO | Train Epoch: 0 [ 1997312/10637090 (19%)] Loss: 1.2389 (1.709) Data (t): 0.001 Batch (t): 0.906, 565.150/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:28:48 | INFO | Train Epoch: 0 [ 2048512/10637090 (19%)] Loss: 1.4484 (1.703) Data (t): 0.001 Batch (t): 0.970, 565.558/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:30:19 | INFO | Train Epoch: 0 [ 2099712/10637090 (20%)] Loss: 1.4061 (1.696) Data (t): 0.001 Batch (t): 0.905, 565.588/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:31:49 | INFO | Train Epoch: 0 [ 2150912/10637090 (20%)] Loss: 1.1981 (1.684) Data (t): 0.001 Batch (t): 0.905, 565.478/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:33:20 | INFO | Train Epoch: 0 [ 2202112/10637090 (21%)] Loss: 1.4048 (1.678) Data (t): 0.001 Batch (t): 0.906, 565.190/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:34:50 | INFO | Train Epoch: 0 [ 2253312/10637090 (21%)] Loss: 1.2888 (1.669) Data (t): 0.001 Batch (t): 0.906, 566.264/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:36:26 | INFO | Train Epoch: 0 [ 2304512/10637090 (22%)] Loss: 1.3869 (1.663) Data (t): 0.001 Batch (t): 0.960, 563.551/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:37:58 | INFO | Train Epoch: 0 [ 2355712/10637090 (22%)] Loss: 1.3551 (1.657) Data (t): 0.001 Batch (t): 0.916, 557.168/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:39:28 | INFO | Train Epoch: 0 [ 2406912/10637090 (23%)] Loss: 1.4228 (1.652) Data (t): 0.001 Batch (t): 0.906, 564.947/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:40:59 | INFO | Train Epoch: 0 [ 2458112/10637090 (23%)] Loss: 1.3846 (1.646) Data (t): 0.001 Batch (t): 0.907, 564.711/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:42:30 | INFO | Train Epoch: 0 [ 2509312/10637090 (24%)] Loss: 1.3890 (1.641) Data (t): 0.001 Batch (t): 0.906, 564.274/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:44:04 | INFO | Train Epoch: 0 [ 2560512/10637090 (24%)] Loss: 1.4708 (1.638) Data (t): 0.001 Batch (t): 0.939, 564.029/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:45:37 | INFO | Train Epoch: 0 [ 2611712/10637090 (25%)] Loss: 1.4605 (1.634) Data (t): 0.001 Batch (t): 0.935, 566.684/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:47:08 | INFO | Train Epoch: 0 [ 2662912/10637090 (25%)] Loss: 1.2774 (1.628) Data (t): 0.001 Batch (t): 0.905, 562.427/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:48:38 | INFO | Train Epoch: 0 [ 2714112/10637090 (26%)] Loss: 1.3679 (1.623) Data (t): 0.001 Batch (t): 0.906, 563.969/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:50:09 | INFO | Train Epoch: 0 [ 2765312/10637090 (26%)] Loss: 1.3890 (1.619) Data (t): 0.001 Batch (t): 0.905, 562.776/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:51:41 | INFO | Train Epoch: 0 [ 2816512/10637090 (26%)] Loss: 1.1875 (1.611) Data (t): 0.001 Batch (t): 0.919, 561.616/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:53:16 | INFO | Train Epoch: 0 [ 2867712/10637090 (27%)] Loss: 1.3898 (1.607) Data (t): 0.001 Batch (t): 0.957, 564.746/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:54:47 | INFO | Train Epoch: 0 [ 2918912/10637090 (27%)] Loss: 1.3824 (1.603) Data (t): 0.001 Batch (t): 0.905, 567.549/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:56:17 | INFO | Train Epoch: 0 [ 2970112/10637090 (28%)] Loss: 1.3579 (1.599) Data (t): 0.001 Batch (t): 0.905, 564.705/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:57:48 | INFO | Train Epoch: 0 [ 3021312/10637090 (28%)] Loss: 1.2568 (1.593) Data (t): 0.001 Batch (t): 0.905, 566.225/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:59:19 | INFO | Train Epoch: 0 [ 3072512/10637090 (29%)] Loss: 1.3294 (1.589) Data (t): 0.001 Batch (t): 0.913, 566.800/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:00:54 | INFO | Train Epoch: 0 [ 3123712/10637090 (29%)] Loss: 1.2655 (1.584) Data (t): 0.001 Batch (t): 0.953, 564.999/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:02:26 | INFO | Train Epoch: 0 [ 3174912/10637090 (30%)] Loss: 1.1694 (1.577) Data (t): 0.001 Batch (t): 0.916, 565.150/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:03:56 | INFO | Train Epoch: 0 [ 3226112/10637090 (30%)] Loss: 1.3314 (1.573) Data (t): 0.001 Batch (t): 0.906, 565.527/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:05:27 | INFO | Train Epoch: 0 [ 3277312/10637090 (31%)] Loss: 1.3500 (1.570) Data (t): 0.001 Batch (t): 0.907, 562.142/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:06:58 | INFO | Train Epoch: 0 [ 3328512/10637090 (31%)] Loss: 1.3836 (1.567) Data (t): 0.001 Batch (t): 0.906, 563.983/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:08:34 | INFO | Train Epoch: 0 [ 3379712/10637090 (32%)] Loss: 1.3994 (1.565) Data (t): 0.001 Batch (t): 0.960, 564.818/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:10:05 | INFO | Train Epoch: 0 [ 3430912/10637090 (32%)] Loss: 1.3057 (1.561) Data (t): 0.001 Batch (t): 0.916, 565.366/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:11:36 | INFO | Train Epoch: 0 [ 3482112/10637090 (33%)] Loss: 1.4654 (1.559) Data (t): 0.001 Batch (t): 0.906, 564.994/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:13:07 | INFO | Train Epoch: 0 [ 3533312/10637090 (33%)] Loss: 1.1907 (1.554) Data (t): 0.001 Batch (t): 0.906, 566.278/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:14:37 | INFO | Train Epoch: 0 [ 3584512/10637090 (34%)] Loss: 1.1220 (1.548) Data (t): 0.001 Batch (t): 0.905, 566.043/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:16:11 | INFO | Train Epoch: 0 [ 3635712/10637090 (34%)] Loss: 1.3814 (1.546) Data (t): 0.001 Batch (t): 0.939, 564.484/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:17:45 | INFO | Train Epoch: 0 [ 3686912/10637090 (35%)] Loss: 1.3244 (1.543) Data (t): 0.001 Batch (t): 0.937, 566.922/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:19:15 | INFO | Train Epoch: 0 [ 3738112/10637090 (35%)] Loss: 1.4181 (1.541) Data (t): 0.001 Batch (t): 0.905, 566.004/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:20:46 | INFO | Train Epoch: 0 [ 3789312/10637090 (36%)] Loss: 1.1651 (1.536) Data (t): 0.001 Batch (t): 0.906, 565.101/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:22:16 | INFO | Train Epoch: 0 [ 3840512/10637090 (36%)] Loss: 1.3660 (1.534) Data (t): 0.001 Batch (t): 0.905, 566.687/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:23:49 | INFO | Train Epoch: 0 [ 3891712/10637090 (37%)] Loss: 1.4300 (1.532) Data (t): 0.001 Batch (t): 0.925, 564.653/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:25:24 | INFO | Train Epoch: 0 [ 3942912/10637090 (37%)] Loss: 1.3340 (1.530) Data (t): 0.001 Batch (t): 0.950, 566.433/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:26:54 | INFO | Train Epoch: 0 [ 3994112/10637090 (38%)] Loss: 1.3072 (1.527) Data (t): 0.001 Batch (t): 0.905, 565.921/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:28:25 | INFO | Train Epoch: 0 [ 4045312/10637090 (38%)] Loss: 1.2631 (1.524) Data (t): 0.001 Batch (t): 0.906, 563.596/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:29:55 | INFO | Train Epoch: 0 [ 4096512/10637090 (39%)] Loss: 1.2469 (1.520) Data (t): 0.001 Batch (t): 0.906, 567.034/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:31:27 | INFO | Train Epoch: 0 [ 4147712/10637090 (39%)] Loss: 1.1652 (1.516) Data (t): 0.001 Batch (t): 0.920, 564.787/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:33:02 | INFO | Train Epoch: 0 [ 4198912/10637090 (39%)] Loss: 1.3874 (1.514) Data (t): 0.001 Batch (t): 0.946, 566.911/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:34:34 | INFO | Train Epoch: 0 [ 4250112/10637090 (40%)] Loss: 1.3164 (1.512) Data (t): 0.001 Batch (t): 0.915, 566.698/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:36:04 | INFO | Train Epoch: 0 [ 4301312/10637090 (40%)] Loss: 1.3739 (1.511) Data (t): 0.001 Batch (t): 0.905, 566.523/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:37:35 | INFO | Train Epoch: 0 [ 4352512/10637090 (41%)] Loss: 1.1820 (1.507) Data (t): 0.001 Batch (t): 0.906, 564.028/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:39:05 | INFO | Train Epoch: 0 [ 4403712/10637090 (41%)] Loss: 1.2819 (1.504) Data (t): 0.001 Batch (t): 0.906, 562.150/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:40:41 | INFO | Train Epoch: 0 [ 4454912/10637090 (42%)] Loss: 1.4259 (1.503) Data (t): 0.001 Batch (t): 0.960, 264.610/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:42:13 | INFO | Train Epoch: 0 [ 4506112/10637090 (42%)] Loss: 1.2922 (1.501) Data (t): 0.001 Batch (t): 0.916, 566.770/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:43:43 | INFO | Train Epoch: 0 [ 4557312/10637090 (43%)] Loss: 1.2810 (1.498) Data (t): 0.001 Batch (t): 0.905, 566.095/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:45:14 | INFO | Train Epoch: 0 [ 4608512/10637090 (43%)] Loss: 1.3400 (1.497) Data (t): 0.001 Batch (t): 0.905, 564.121/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:46:44 | INFO | Train Epoch: 0 [ 4659712/10637090 (44%)] Loss: 1.3546 (1.495) Data (t): 0.001 Batch (t): 0.905, 565.485/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:48:18 | INFO | Train Epoch: 0 [ 4710912/10637090 (44%)] Loss: 1.2837 (1.493) Data (t): 0.001 Batch (t): 0.939, 567.428/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:49:52 | INFO | Train Epoch: 0 [ 4762112/10637090 (45%)] Loss: 1.3378 (1.491) Data (t): 0.001 Batch (t): 0.937, 563.893/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:51:23 | INFO | Train Epoch: 0 [ 4813312/10637090 (45%)] Loss: 1.2499 (1.489) Data (t): 0.001 Batch (t): 0.906, 562.105/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:52:53 | INFO | Train Epoch: 0 [ 4864512/10637090 (46%)] Loss: 1.3169 (1.487) Data (t): 0.001 Batch (t): 0.906, 562.888/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:54:24 | INFO | Train Epoch: 0 [ 4915712/10637090 (46%)] Loss: 1.3130 (1.485) Data (t): 0.001 Batch (t): 0.906, 558.000/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:55:58 | INFO | Train Epoch: 0 [ 4966912/10637090 (47%)] Loss: 1.2658 (1.483) Data (t): 0.001 Batch (t): 0.940, 564.414/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:57:32 | INFO | Train Epoch: 0 [ 5018112/10637090 (47%)] Loss: 1.3192 (1.481) Data (t): 0.001 Batch (t): 0.936, 565.290/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:59:02 | INFO | Train Epoch: 0 [ 5069312/10637090 (48%)] Loss: 1.1244 (1.478) Data (t): 0.001 Batch (t): 0.906, 565.728/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:00:33 | INFO | Train Epoch: 0 [ 5120512/10637090 (48%)] Loss: 1.1769 (1.475) Data (t): 0.001 Batch (t): 0.906, 566.048/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:02:03 | INFO | Train Epoch: 0 [ 5171712/10637090 (49%)] Loss: 1.2274 (1.472) Data (t): 0.001 Batch (t): 0.906, 563.096/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:03:36 | INFO | Train Epoch: 0 [ 5222912/10637090 (49%)] Loss: 1.2307 (1.470) Data (t): 0.001 Batch (t): 0.926, 567.223/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:05:10 | INFO | Train Epoch: 0 [ 5274112/10637090 (50%)] Loss: 1.1837 (1.467) Data (t): 0.001 Batch (t): 0.940, 566.891/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:06:42 | INFO | Train Epoch: 0 [ 5325312/10637090 (50%)] Loss: 1.3002 (1.466) Data (t): 0.001 Batch (t): 0.916, 565.741/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:08:12 | INFO | Train Epoch: 0 [ 5376512/10637090 (51%)] Loss: 1.1461 (1.463) Data (t): 0.001 Batch (t): 0.905, 565.395/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:09:43 | INFO | Train Epoch: 0 [ 5427712/10637090 (51%)] Loss: 1.3009 (1.461) Data (t): 0.001 Batch (t): 0.905, 565.003/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:11:14 | INFO | Train Epoch: 0 [ 5478912/10637090 (52%)] Loss: 1.3348 (1.460) Data (t): 0.001 Batch (t): 0.912, 562.009/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:12:49 | INFO | Train Epoch: 0 [ 5530112/10637090 (52%)] Loss: 1.3173 (1.459) Data (t): 0.001 Batch (t): 0.954, 566.691/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:14:21 | INFO | Train Epoch: 0 [ 5581312/10637090 (52%)] Loss: 1.1887 (1.456) Data (t): 0.001 Batch (t): 0.916, 566.148/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:15:51 | INFO | Train Epoch: 0 [ 5632512/10637090 (53%)] Loss: 1.2399 (1.454) Data (t): 0.001 Batch (t): 0.905, 564.712/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:17:22 | INFO | Train Epoch: 0 [ 5683712/10637090 (53%)] Loss: 1.1904 (1.452) Data (t): 0.001 Batch (t): 0.906, 565.968/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:18:52 | INFO | Train Epoch: 0 [ 5734912/10637090 (54%)] Loss: 1.1559 (1.449) Data (t): 0.001 Batch (t): 0.905, 566.546/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:20:26 | INFO | Train Epoch: 0 [ 5786112/10637090 (54%)] Loss: 1.2268 (1.447) Data (t): 0.001 Batch (t): 0.940, 563.824/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:22:00 | INFO | Train Epoch: 0 [ 5837312/10637090 (55%)] Loss: 1.1154 (1.444) Data (t): 0.001 Batch (t): 0.937, 565.785/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:23:31 | INFO | Train Epoch: 0 [ 5888512/10637090 (55%)] Loss: 1.2780 (1.443) Data (t): 0.001 Batch (t): 0.904, 563.530/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:25:01 | INFO | Train Epoch: 0 [ 5939712/10637090 (56%)] Loss: 1.2995 (1.442) Data (t): 0.001 Batch (t): 0.906, 564.827/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:26:32 | INFO | Train Epoch: 0 [ 5990912/10637090 (56%)] Loss: 1.2820 (1.440) Data (t): 0.001 Batch (t): 0.906, 566.366/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:28:06 | INFO | Train Epoch: 0 [ 6042112/10637090 (57%)] Loss: 1.0357 (1.437) Data (t): 0.001 Batch (t): 0.939, 568.102/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:29:39 | INFO | Train Epoch: 0 [ 6093312/10637090 (57%)] Loss: 1.1916 (1.435) Data (t): 0.001 Batch (t): 0.936, 566.852/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:31:10 | INFO | Train Epoch: 0 [ 6144512/10637090 (58%)] Loss: 1.3087 (1.434) Data (t): 0.001 Batch (t): 0.905, 565.900/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:32:40 | INFO | Train Epoch: 0 [ 6195712/10637090 (58%)] Loss: 1.2553 (1.432) Data (t): 0.001 Batch (t): 0.905, 564.135/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:34:11 | INFO | Train Epoch: 0 [ 6246912/10637090 (59%)] Loss: 1.1910 (1.430) Data (t): 0.001 Batch (t): 0.905, 568.550/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:35:45 | INFO | Train Epoch: 0 [ 6298112/10637090 (59%)] Loss: 1.2113 (1.429) Data (t): 0.001 Batch (t): 0.941, 565.892/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:37:18 | INFO | Train Epoch: 0 [ 6349312/10637090 (60%)] Loss: 1.2448 (1.427) Data (t): 0.001 Batch (t): 0.928, 566.225/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:38:49 | INFO | Train Epoch: 0 [ 6400512/10637090 (60%)] Loss: 1.3494 (1.427) Data (t): 0.001 Batch (t): 0.916, 564.945/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:40:20 | INFO | Train Epoch: 0 [ 6451712/10637090 (61%)] Loss: 1.2914 (1.425) Data (t): 0.001 Batch (t): 0.904, 565.027/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:41:50 | INFO | Train Epoch: 0 [ 6502912/10637090 (61%)] Loss: 1.2394 (1.424) Data (t): 0.001 Batch (t): 0.905, 564.575/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:43:23 | INFO | Train Epoch: 0 [ 6554112/10637090 (62%)] Loss: 1.1204 (1.422) Data (t): 0.001 Batch (t): 0.926, 564.557/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:44:57 | INFO | Train Epoch: 0 [ 6605312/10637090 (62%)] Loss: 1.1377 (1.419) Data (t): 0.001 Batch (t): 0.940, 566.221/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:46:28 | INFO | Train Epoch: 0 [ 6656512/10637090 (63%)] Loss: 1.1403 (1.417) Data (t): 0.001 Batch (t): 0.915, 566.595/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:47:59 | INFO | Train Epoch: 0 [ 6707712/10637090 (63%)] Loss: 1.1797 (1.416) Data (t): 0.001 Batch (t): 0.905, 566.042/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:49:29 | INFO | Train Epoch: 0 [ 6758912/10637090 (64%)] Loss: 1.1877 (1.414) Data (t): 0.001 Batch (t): 0.904, 564.831/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:51:00 | INFO | Train Epoch: 0 [ 6810112/10637090 (64%)] Loss: 1.2662 (1.413) Data (t): 0.001 Batch (t): 0.912, 566.063/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:52:33 | INFO | Train Epoch: 0 [ 6861312/10637090 (65%)] Loss: 1.3159 (1.412) Data (t): 0.001 Batch (t): 0.932, 565.862/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:54:07 | INFO | Train Epoch: 0 [ 6912512/10637090 (65%)] Loss: 1.2148 (1.411) Data (t): 0.001 Batch (t): 0.937, 565.621/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:55:38 | INFO | Train Epoch: 0 [ 6963712/10637090 (65%)] Loss: 1.2995 (1.410) Data (t): 0.001 Batch (t): 0.906, 563.823/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:57:08 | INFO | Train Epoch: 0 [ 7014912/10637090 (66%)] Loss: 1.1218 (1.408) Data (t): 0.001 Batch (t): 0.906, 565.513/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:58:39 | INFO | Train Epoch: 0 [ 7066112/10637090 (66%)] Loss: 1.3295 (1.407) Data (t): 0.001 Batch (t): 0.906, 563.177/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:00:13 | INFO | Train Epoch: 0 [ 7117312/10637090 (67%)] Loss: 1.1448 (1.405) Data (t): 0.001 Batch (t): 0.941, 563.472/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:01:47 | INFO | Train Epoch: 0 [ 7168512/10637090 (67%)] Loss: 1.1801 (1.404) Data (t): 0.001 Batch (t): 0.938, 566.473/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:03:18 | INFO | Train Epoch: 0 [ 7219712/10637090 (68%)] Loss: 1.1822 (1.402) Data (t): 0.001 Batch (t): 0.906, 565.609/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:04:48 | INFO | Train Epoch: 0 [ 7270912/10637090 (68%)] Loss: 1.3013 (1.401) Data (t): 0.001 Batch (t): 0.906, 566.407/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:06:19 | INFO | Train Epoch: 0 [ 7322112/10637090 (69%)] Loss: 1.2890 (1.401) Data (t): 0.001 Batch (t): 0.906, 565.871/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:07:53 | INFO | Train Epoch: 0 [ 7373312/10637090 (69%)] Loss: 1.2193 (1.399) Data (t): 0.001 Batch (t): 0.942, 564.987/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:09:26 | INFO | Train Epoch: 0 [ 7424512/10637090 (70%)] Loss: 1.2400 (1.398) Data (t): 0.001 Batch (t): 0.927, 565.552/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:10:57 | INFO | Train Epoch: 0 [ 7475712/10637090 (70%)] Loss: 1.2489 (1.397) Data (t): 0.001 Batch (t): 0.917, 566.312/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:12:28 | INFO | Train Epoch: 0 [ 7526912/10637090 (71%)] Loss: 1.3111 (1.397) Data (t): 0.001 Batch (t): 0.906, 566.103/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:13:59 | INFO | Train Epoch: 0 [ 7578112/10637090 (71%)] Loss: 1.1942 (1.395) Data (t): 0.001 Batch (t): 0.906, 566.083/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:15:33 | INFO | Train Epoch: 0 [ 7629312/10637090 (72%)] Loss: 1.2478 (1.394) Data (t): 0.001 Batch (t): 0.941, 568.291/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:17:05 | INFO | Train Epoch: 0 [ 7680512/10637090 (72%)] Loss: 1.1523 (1.393) Data (t): 0.001 Batch (t): 0.926, 566.661/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:18:37 | INFO | Train Epoch: 0 [ 7731712/10637090 (73%)] Loss: 1.1149 (1.391) Data (t): 0.001 Batch (t): 0.916, 580.253/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:20:07 | INFO | Train Epoch: 0 [ 7782912/10637090 (73%)] Loss: 1.3505 (1.391) Data (t): 0.001 Batch (t): 0.906, 566.431/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:21:38 | INFO | Train Epoch: 0 [ 7834112/10637090 (74%)] Loss: 1.1597 (1.389) Data (t): 0.001 Batch (t): 0.904, 565.911/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:23:10 | INFO | Train Epoch: 0 [ 7885312/10637090 (74%)] Loss: 1.1724 (1.388) Data (t): 0.001 Batch (t): 0.927, 565.110/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:24:42 | INFO | Train Epoch: 0 [ 7936512/10637090 (75%)] Loss: 1.2565 (1.387) Data (t): 0.001 Batch (t): 0.919, 564.593/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:26:16 | INFO | Train Epoch: 0 [ 7987712/10637090 (75%)] Loss: 1.1642 (1.385) Data (t): 0.001 Batch (t): 0.937, 566.546/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:27:47 | INFO | Train Epoch: 0 [ 8038912/10637090 (76%)] Loss: 1.2992 (1.385) Data (t): 0.001 Batch (t): 0.905, 565.761/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:29:17 | INFO | Train Epoch: 0 [ 8090112/10637090 (76%)] Loss: 1.3238 (1.385) Data (t): 0.001 Batch (t): 0.905, 563.700/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:30:49 | INFO | Train Epoch: 0 [ 8141312/10637090 (77%)] Loss: 1.2713 (1.384) Data (t): 0.001 Batch (t): 0.919, 568.559/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:32:22 | INFO | Train Epoch: 0 [ 8192512/10637090 (77%)] Loss: 1.1442 (1.382) Data (t): 0.001 Batch (t): 0.927, 566.255/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:33:55 | INFO | Train Epoch: 0 [ 8243712/10637090 (78%)] Loss: 1.0954 (1.381) Data (t): 0.001 Batch (t): 0.937, 565.181/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:35:26 | INFO | Train Epoch: 0 [ 8294912/10637090 (78%)] Loss: 1.3564 (1.380) Data (t): 0.001 Batch (t): 0.905, 566.639/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:36:56 | INFO | Train Epoch: 0 [ 8346112/10637090 (78%)] Loss: 1.2590 (1.380) Data (t): 0.001 Batch (t): 0.904, 564.117/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:38:27 | INFO | Train Epoch: 0 [ 8397312/10637090 (79%)] Loss: 1.1770 (1.378) Data (t): 0.001 Batch (t): 0.905, 564.134/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:40:01 | INFO | Train Epoch: 0 [ 8448512/10637090 (79%)] Loss: 1.1348 (1.377) Data (t): 0.001 Batch (t): 0.941, 563.761/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:41:33 | INFO | Train Epoch: 0 [ 8499712/10637090 (80%)] Loss: 1.1695 (1.376) Data (t): 0.001 Batch (t): 0.926, 568.251/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:43:05 | INFO | Train Epoch: 0 [ 8550912/10637090 (80%)] Loss: 1.1918 (1.375) Data (t): 0.001 Batch (t): 0.916, 564.239/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:44:36 | INFO | Train Epoch: 0 [ 8602112/10637090 (81%)] Loss: 1.3536 (1.375) Data (t): 0.001 Batch (t): 0.905, 565.739/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:46:06 | INFO | Train Epoch: 0 [ 8653312/10637090 (81%)] Loss: 1.1845 (1.373) Data (t): 0.001 Batch (t): 0.905, 565.182/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:47:40 | INFO | Train Epoch: 0 [ 8704512/10637090 (82%)] Loss: 1.1522 (1.372) Data (t): 0.001 Batch (t): 0.941, 567.618/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:49:12 | INFO | Train Epoch: 0 [ 8755712/10637090 (82%)] Loss: 1.2960 (1.372) Data (t): 0.001 Batch (t): 0.915, 564.128/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:50:44 | INFO | Train Epoch: 0 [ 8806912/10637090 (83%)] Loss: 1.1738 (1.371) Data (t): 0.001 Batch (t): 0.927, 563.781/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:52:15 | INFO | Train Epoch: 0 [ 8858112/10637090 (83%)] Loss: 1.2121 (1.370) Data (t): 0.001 Batch (t): 0.905, 566.281/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:53:45 | INFO | Train Epoch: 0 [ 8909312/10637090 (84%)] Loss: 1.2586 (1.369) Data (t): 0.001 Batch (t): 0.906, 566.052/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:55:19 | INFO | Train Epoch: 0 [ 8960512/10637090 (84%)] Loss: 1.3684 (1.369) Data (t): 0.001 Batch (t): 0.934, 567.096/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:56:50 | INFO | Train Epoch: 0 [ 9011712/10637090 (85%)] Loss: 1.1778 (1.368) Data (t): 0.001 Batch (t): 0.913, 565.718/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:58:24 | INFO | Train Epoch: 0 [ 9062912/10637090 (85%)] Loss: 1.1981 (1.367) Data (t): 0.001 Batch (t): 0.938, 566.629/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:59:55 | INFO | Train Epoch: 0 [ 9114112/10637090 (86%)] Loss: 1.2079 (1.366) Data (t): 0.001 Batch (t): 0.906, 564.911/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:01:25 | INFO | Train Epoch: 0 [ 9165312/10637090 (86%)] Loss: 1.3082 (1.366) Data (t): 0.001 Batch (t): 0.905, 565.323/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:02:58 | INFO | Train Epoch: 0 [ 9216512/10637090 (87%)] Loss: 1.1537 (1.365) Data (t): 0.001 Batch (t): 0.927, 566.460/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:04:30 | INFO | Train Epoch: 0 [ 9267712/10637090 (87%)] Loss: 1.2238 (1.364) Data (t): 0.001 Batch (t): 0.919, 566.179/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:06:02 | INFO | Train Epoch: 0 [ 9318912/10637090 (88%)] Loss: 1.1675 (1.363) Data (t): 0.001 Batch (t): 0.926, 564.695/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:07:34 | INFO | Train Epoch: 0 [ 9370112/10637090 (88%)] Loss: 1.1506 (1.362) Data (t): 0.001 Batch (t): 0.916, 565.429/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:09:04 | INFO | Train Epoch: 0 [ 9421312/10637090 (89%)] Loss: 1.2045 (1.361) Data (t): 0.001 Batch (t): 0.905, 565.161/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:10:36 | INFO | Train Epoch: 0 [ 9472512/10637090 (89%)] Loss: 1.0813 (1.359) Data (t): 0.001 Batch (t): 0.912, 566.481/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:12:09 | INFO | Train Epoch: 0 [ 9523712/10637090 (90%)] Loss: 1.2197 (1.358) Data (t): 0.001 Batch (t): 0.936, 565.641/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:13:42 | INFO | Train Epoch: 0 [ 9574912/10637090 (90%)] Loss: 1.1496 (1.357) Data (t): 0.001 Batch (t): 0.927, 566.480/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:15:14 | INFO | Train Epoch: 0 [ 9626112/10637090 (90%)] Loss: 1.1646 (1.356) Data (t): 0.001 Batch (t): 0.916, 565.670/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:16:44 | INFO | Train Epoch: 0 [ 9677312/10637090 (91%)] Loss: 1.1273 (1.355) Data (t): 0.001 Batch (t): 0.906, 564.308/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:18:15 | INFO | Train Epoch: 0 [ 9728512/10637090 (91%)] Loss: 1.3357 (1.355) Data (t): 0.001 Batch (t): 0.905, 565.224/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:19:49 | INFO | Train Epoch: 0 [ 9779712/10637090 (92%)] Loss: 1.0409 (1.353) Data (t): 0.001 Batch (t): 0.942, 567.549/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:21:21 | INFO | Train Epoch: 0 [ 9830912/10637090 (92%)] Loss: 1.2838 (1.353) Data (t): 0.001 Batch (t): 0.926, 565.977/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:22:53 | INFO | Train Epoch: 0 [ 9882112/10637090 (93%)] Loss: 1.3626 (1.353) Data (t): 0.001 Batch (t): 0.916, 563.409/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:24:24 | INFO | Train Epoch: 0 [ 9933312/10637090 (93%)] Loss: 1.1998 (1.352) Data (t): 0.001 Batch (t): 0.906, 564.122/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:25:54 | INFO | Train Epoch: 0 [ 9984512/10637090 (94%)] Loss: 1.2297 (1.352) Data (t): 0.001 Batch (t): 0.906, 565.543/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:27:29 | INFO | Train Epoch: 0 [10035712/10637090 (94%)] Loss: 1.2715 (1.351) Data (t): 0.001 Batch (t): 0.943, 566.838/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:28:59 | INFO | Train Epoch: 0 [10086912/10637090 (95%)] Loss: 1.2865 (1.351) Data (t): 0.001 Batch (t): 0.906, 563.765/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:30:32 | INFO | Train Epoch: 0 [10138112/10637090 (95%)] Loss: 1.2209 (1.350) Data (t): 0.001 Batch (t): 0.928, 561.596/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:32:03 | INFO | Train Epoch: 0 [10189312/10637090 (96%)] Loss: 1.3355 (1.350) Data (t): 0.001 Batch (t): 0.907, 558.925/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:33:33 | INFO | Train Epoch: 0 [10240512/10637090 (96%)] Loss: 1.1038 (1.349) Data (t): 0.001 Batch (t): 0.906, 564.300/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:35:06 | INFO | Train Epoch: 0 [10291712/10637090 (97%)] Loss: 1.2405 (1.348) Data (t): 0.001 Batch (t): 0.928, 567.856/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:36:38 | INFO | Train Epoch: 0 [10342912/10637090 (97%)] Loss: 1.0198 (1.347) Data (t): 0.001 Batch (t): 0.919, 563.372/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:38:12 | INFO | Train Epoch: 0 [10394112/10637090 (98%)] Loss: 1.1710 (1.346) Data (t): 0.001 Batch (t): 0.938, 249.357/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:39:42 | INFO | Train Epoch: 0 [10445312/10637090 (98%)] Loss: 1.3286 (1.346) Data (t): 0.001 Batch (t): 0.905, 565.746/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:41:13 | INFO | Train Epoch: 0 [10496512/10637090 (99%)] Loss: 1.0912 (1.345) Data (t): 0.001 Batch (t): 0.904, 567.087/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:42:45 | INFO | Train Epoch: 0 [10547712/10637090 (99%)] Loss: 1.4040 (1.345) Data (t): 0.001 Batch (t): 0.928, 567.309/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:44:17 | INFO | Train Epoch: 0 [10598912/10637090 (100%)] Loss: 1.2265 (1.344) Data (t): 0.001 Batch (t): 0.920, 565.180/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:45:26 | INFO | Train Epoch: 0 [10636800/10637090 (100%)] Loss: 1.3101 (1.344) Data (t): 0.001 Batch (t): 0.933, 568.099/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:45:33 | INFO | Start epoch 1 -2024-11-26,18:45:36 | INFO | Train Epoch: 1 [ 512/10637090 (0%)] Loss: 1.1113 (1.111) Data (t): 2.678 Batch (t): 3.614, 141.674/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,18:47:08 | INFO | Train Epoch: 1 [ 51712/10637090 (0%)] Loss: 1.1790 (1.145) Data (t): 0.001 Batch (t): 0.918, 565.967/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,18:48:39 | INFO | Train Epoch: 1 [ 102912/10637090 (1%)] Loss: 1.2082 (1.166) Data (t): 0.001 Batch (t): 0.906, 567.243/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,18:50:10 | INFO | Train Epoch: 1 [ 154112/10637090 (1%)] Loss: 1.2178 (1.179) Data (t): 0.001 Batch (t): 0.913, 564.863/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,18:51:44 | INFO | Train Epoch: 1 [ 205312/10637090 (2%)] Loss: 1.0870 (1.161) Data (t): 0.001 Batch (t): 0.935, 566.280/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,18:53:14 | INFO | Train Epoch: 1 [ 256512/10637090 (2%)] Loss: 1.2527 (1.176) Data (t): 0.001 Batch (t): 0.903, 564.633/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,18:54:47 | INFO | Train Epoch: 1 [ 307712/10637090 (3%)] Loss: 1.1543 (1.173) Data (t): 0.001 Batch (t): 0.935, 566.305/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,18:56:18 | INFO | Train Epoch: 1 [ 358912/10637090 (3%)] Loss: 1.2308 (1.180) Data (t): 0.001 Batch (t): 0.903, 568.845/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,18:57:49 | INFO | Train Epoch: 1 [ 410112/10637090 (4%)] Loss: 1.2369 (1.186) Data (t): 0.001 Batch (t): 0.911, 567.065/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,18:59:22 | INFO | Train Epoch: 1 [ 461312/10637090 (4%)] Loss: 1.0303 (1.171) Data (t): 0.001 Batch (t): 0.930, 567.518/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:00:52 | INFO | Train Epoch: 1 [ 512512/10637090 (5%)] Loss: 1.2102 (1.174) Data (t): 0.001 Batch (t): 0.903, 564.848/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:02:25 | INFO | Train Epoch: 1 [ 563712/10637090 (5%)] Loss: 1.2996 (1.185) Data (t): 0.001 Batch (t): 0.934, 566.655/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:03:56 | INFO | Train Epoch: 1 [ 614912/10637090 (6%)] Loss: 1.1657 (1.183) Data (t): 0.001 Batch (t): 0.905, 568.032/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:05:27 | INFO | Train Epoch: 1 [ 666112/10637090 (6%)] Loss: 1.1367 (1.180) Data (t): 0.001 Batch (t): 0.912, 567.773/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:06:59 | INFO | Train Epoch: 1 [ 717312/10637090 (7%)] Loss: 1.2879 (1.187) Data (t): 0.001 Batch (t): 0.918, 567.224/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:08:31 | INFO | Train Epoch: 1 [ 768512/10637090 (7%)] Loss: 1.2183 (1.189) Data (t): 0.001 Batch (t): 0.918, 567.219/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:10:03 | INFO | Train Epoch: 1 [ 819712/10637090 (8%)] Loss: 1.2487 (1.193) Data (t): 0.001 Batch (t): 0.923, 564.209/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:11:34 | INFO | Train Epoch: 1 [ 870912/10637090 (8%)] Loss: 1.0493 (1.185) Data (t): 0.001 Batch (t): 0.913, 566.582/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:13:05 | INFO | Train Epoch: 1 [ 922112/10637090 (9%)] Loss: 1.1146 (1.181) Data (t): 0.001 Batch (t): 0.904, 566.208/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:14:37 | INFO | Train Epoch: 1 [ 973312/10637090 (9%)] Loss: 1.2195 (1.183) Data (t): 0.001 Batch (t): 0.918, 563.963/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:16:09 | INFO | Train Epoch: 1 [ 1024512/10637090 (10%)] Loss: 1.1162 (1.180) Data (t): 0.001 Batch (t): 0.925, 566.122/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:17:41 | INFO | Train Epoch: 1 [ 1075712/10637090 (10%)] Loss: 1.1723 (1.179) Data (t): 0.001 Batch (t): 0.923, 566.487/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:19:13 | INFO | Train Epoch: 1 [ 1126912/10637090 (11%)] Loss: 1.0903 (1.176) Data (t): 0.001 Batch (t): 0.913, 569.544/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:20:43 | INFO | Train Epoch: 1 [ 1178112/10637090 (11%)] Loss: 1.1557 (1.175) Data (t): 0.001 Batch (t): 0.904, 564.434/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:22:14 | INFO | Train Epoch: 1 [ 1229312/10637090 (12%)] Loss: 1.2936 (1.179) Data (t): 0.001 Batch (t): 0.910, 568.474/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:23:47 | INFO | Train Epoch: 1 [ 1280512/10637090 (12%)] Loss: 1.0049 (1.173) Data (t): 0.001 Batch (t): 0.930, 566.808/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:25:18 | INFO | Train Epoch: 1 [ 1331712/10637090 (13%)] Loss: 1.1353 (1.171) Data (t): 0.001 Batch (t): 0.913, 566.849/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:26:51 | INFO | Train Epoch: 1 [ 1382912/10637090 (13%)] Loss: 1.1447 (1.170) Data (t): 0.001 Batch (t): 0.922, 564.822/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:28:21 | INFO | Train Epoch: 1 [ 1434112/10637090 (13%)] Loss: 1.0842 (1.167) Data (t): 0.001 Batch (t): 0.904, 564.034/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:29:52 | INFO | Train Epoch: 1 [ 1485312/10637090 (14%)] Loss: 1.1166 (1.166) Data (t): 0.001 Batch (t): 0.910, 568.063/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:31:25 | INFO | Train Epoch: 1 [ 1536512/10637090 (14%)] Loss: 1.0902 (1.163) Data (t): 0.001 Batch (t): 0.930, 565.891/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:32:55 | INFO | Train Epoch: 1 [ 1587712/10637090 (15%)] Loss: 1.1390 (1.163) Data (t): 0.001 Batch (t): 0.903, 565.759/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:34:28 | INFO | Train Epoch: 1 [ 1638912/10637090 (15%)] Loss: 1.0111 (1.158) Data (t): 0.001 Batch (t): 0.931, 567.499/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:35:59 | INFO | Train Epoch: 1 [ 1690112/10637090 (16%)] Loss: 1.1096 (1.157) Data (t): 0.001 Batch (t): 0.904, 566.242/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:37:30 | INFO | Train Epoch: 1 [ 1741312/10637090 (16%)] Loss: 1.0811 (1.154) Data (t): 0.001 Batch (t): 0.911, 563.914/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:39:03 | INFO | Train Epoch: 1 [ 1792512/10637090 (17%)] Loss: 1.2335 (1.157) Data (t): 0.001 Batch (t): 0.931, 567.587/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:40:33 | INFO | Train Epoch: 1 [ 1843712/10637090 (17%)] Loss: 0.96379 (1.151) Data (t): 0.001 Batch (t): 0.904, 568.363/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:42:07 | INFO | Train Epoch: 1 [ 1894912/10637090 (18%)] Loss: 1.2238 (1.153) Data (t): 0.001 Batch (t): 0.934, 565.869/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:43:37 | INFO | Train Epoch: 1 [ 1946112/10637090 (18%)] Loss: 1.1333 (1.153) Data (t): 0.001 Batch (t): 0.905, 563.048/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:45:08 | INFO | Train Epoch: 1 [ 1997312/10637090 (19%)] Loss: 1.1600 (1.153) Data (t): 0.001 Batch (t): 0.904, 565.614/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:46:40 | INFO | Train Epoch: 1 [ 2048512/10637090 (19%)] Loss: 1.0534 (1.151) Data (t): 0.001 Batch (t): 0.924, 568.257/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:48:12 | INFO | Train Epoch: 1 [ 2099712/10637090 (20%)] Loss: 1.1201 (1.150) Data (t): 0.001 Batch (t): 0.917, 566.229/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:49:44 | INFO | Train Epoch: 1 [ 2150912/10637090 (20%)] Loss: 1.1335 (1.149) Data (t): 0.001 Batch (t): 0.924, 565.974/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:51:16 | INFO | Train Epoch: 1 [ 2202112/10637090 (21%)] Loss: 1.1657 (1.150) Data (t): 0.001 Batch (t): 0.915, 561.098/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:52:46 | INFO | Train Epoch: 1 [ 2253312/10637090 (21%)] Loss: 1.1044 (1.149) Data (t): 0.001 Batch (t): 0.906, 565.411/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:54:18 | INFO | Train Epoch: 1 [ 2304512/10637090 (22%)] Loss: 1.2146 (1.150) Data (t): 0.001 Batch (t): 0.918, 568.226/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:55:51 | INFO | Train Epoch: 1 [ 2355712/10637090 (22%)] Loss: 1.2726 (1.153) Data (t): 0.001 Batch (t): 0.925, 566.263/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:57:23 | INFO | Train Epoch: 1 [ 2406912/10637090 (23%)] Loss: 1.1442 (1.153) Data (t): 0.001 Batch (t): 0.923, 564.662/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:58:54 | INFO | Train Epoch: 1 [ 2458112/10637090 (23%)] Loss: 1.2504 (1.155) Data (t): 0.001 Batch (t): 0.914, 565.119/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:00:25 | INFO | Train Epoch: 1 [ 2509312/10637090 (24%)] Loss: 1.1312 (1.154) Data (t): 0.001 Batch (t): 0.904, 565.673/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:01:56 | INFO | Train Epoch: 1 [ 2560512/10637090 (24%)] Loss: 1.1920 (1.155) Data (t): 0.001 Batch (t): 0.911, 567.681/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:03:29 | INFO | Train Epoch: 1 [ 2611712/10637090 (25%)] Loss: 1.2368 (1.156) Data (t): 0.001 Batch (t): 0.931, 567.142/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:05:00 | INFO | Train Epoch: 1 [ 2662912/10637090 (25%)] Loss: 1.1776 (1.157) Data (t): 0.001 Batch (t): 0.914, 564.779/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:06:33 | INFO | Train Epoch: 1 [ 2714112/10637090 (26%)] Loss: 1.1162 (1.156) Data (t): 0.001 Batch (t): 0.924, 565.048/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:08:03 | INFO | Train Epoch: 1 [ 2765312/10637090 (26%)] Loss: 1.1965 (1.157) Data (t): 0.001 Batch (t): 0.905, 565.182/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:09:34 | INFO | Train Epoch: 1 [ 2816512/10637090 (26%)] Loss: 1.0867 (1.156) Data (t): 0.001 Batch (t): 0.912, 569.781/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:11:07 | INFO | Train Epoch: 1 [ 2867712/10637090 (27%)] Loss: 1.2475 (1.157) Data (t): 0.001 Batch (t): 0.931, 568.291/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:12:38 | INFO | Train Epoch: 1 [ 2918912/10637090 (27%)] Loss: 1.1480 (1.157) Data (t): 0.001 Batch (t): 0.904, 568.279/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:14:11 | INFO | Train Epoch: 1 [ 2970112/10637090 (28%)] Loss: 1.1874 (1.158) Data (t): 0.001 Batch (t): 0.935, 567.359/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:15:42 | INFO | Train Epoch: 1 [ 3021312/10637090 (28%)] Loss: 1.2032 (1.158) Data (t): 0.001 Batch (t): 0.904, 567.935/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:17:12 | INFO | Train Epoch: 1 [ 3072512/10637090 (29%)] Loss: 1.1499 (1.158) Data (t): 0.001 Batch (t): 0.904, 567.167/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:18:45 | INFO | Train Epoch: 1 [ 3123712/10637090 (29%)] Loss: 1.2424 (1.160) Data (t): 0.001 Batch (t): 0.924, 564.097/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:20:16 | INFO | Train Epoch: 1 [ 3174912/10637090 (30%)] Loss: 1.0890 (1.158) Data (t): 0.001 Batch (t): 0.917, 567.259/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:21:50 | INFO | Train Epoch: 1 [ 3226112/10637090 (30%)] Loss: 1.2888 (1.160) Data (t): 0.001 Batch (t): 0.934, 565.948/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:23:20 | INFO | Train Epoch: 1 [ 3277312/10637090 (31%)] Loss: 1.1547 (1.160) Data (t): 0.001 Batch (t): 0.905, 565.787/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:24:51 | INFO | Train Epoch: 1 [ 3328512/10637090 (31%)] Loss: 1.3574 (1.163) Data (t): 0.001 Batch (t): 0.904, 565.443/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:26:23 | INFO | Train Epoch: 1 [ 3379712/10637090 (32%)] Loss: 1.1291 (1.163) Data (t): 0.001 Batch (t): 0.924, 566.729/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:27:55 | INFO | Train Epoch: 1 [ 3430912/10637090 (32%)] Loss: 1.2364 (1.164) Data (t): 0.001 Batch (t): 0.918, 566.960/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:29:27 | INFO | Train Epoch: 1 [ 3482112/10637090 (33%)] Loss: 1.0870 (1.163) Data (t): 0.001 Batch (t): 0.925, 564.544/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:30:59 | INFO | Train Epoch: 1 [ 3533312/10637090 (33%)] Loss: 1.1682 (1.163) Data (t): 0.001 Batch (t): 0.915, 567.330/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:32:29 | INFO | Train Epoch: 1 [ 3584512/10637090 (34%)] Loss: 1.0792 (1.162) Data (t): 0.001 Batch (t): 0.905, 564.027/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:34:01 | INFO | Train Epoch: 1 [ 3635712/10637090 (34%)] Loss: 1.1628 (1.162) Data (t): 0.001 Batch (t): 0.919, 565.897/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:35:34 | INFO | Train Epoch: 1 [ 3686912/10637090 (35%)] Loss: 0.98935 (1.159) Data (t): 0.001 Batch (t): 0.924, 565.175/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:37:05 | INFO | Train Epoch: 1 [ 3738112/10637090 (35%)] Loss: 1.2257 (1.160) Data (t): 0.001 Batch (t): 0.913, 566.154/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:38:37 | INFO | Train Epoch: 1 [ 3789312/10637090 (36%)] Loss: 1.2173 (1.161) Data (t): 0.001 Batch (t): 0.924, 566.209/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:40:08 | INFO | Train Epoch: 1 [ 3840512/10637090 (36%)] Loss: 1.2186 (1.162) Data (t): 0.001 Batch (t): 0.904, 567.116/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:41:38 | INFO | Train Epoch: 1 [ 3891712/10637090 (37%)] Loss: 1.0532 (1.160) Data (t): 0.001 Batch (t): 0.905, 566.398/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:43:12 | INFO | Train Epoch: 1 [ 3942912/10637090 (37%)] Loss: 1.0454 (1.159) Data (t): 0.001 Batch (t): 0.939, 564.783/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:44:44 | INFO | Train Epoch: 1 [ 3994112/10637090 (38%)] Loss: 1.0210 (1.157) Data (t): 0.001 Batch (t): 0.914, 566.413/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:46:16 | INFO | Train Epoch: 1 [ 4045312/10637090 (38%)] Loss: 1.1267 (1.157) Data (t): 0.001 Batch (t): 0.922, 567.655/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:47:46 | INFO | Train Epoch: 1 [ 4096512/10637090 (39%)] Loss: 1.1625 (1.157) Data (t): 0.001 Batch (t): 0.904, 567.239/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:49:17 | INFO | Train Epoch: 1 [ 4147712/10637090 (39%)] Loss: 1.0676 (1.156) Data (t): 0.001 Batch (t): 0.905, 569.392/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:50:51 | INFO | Train Epoch: 1 [ 4198912/10637090 (39%)] Loss: 1.1740 (1.156) Data (t): 0.001 Batch (t): 0.937, 567.198/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:52:21 | INFO | Train Epoch: 1 [ 4250112/10637090 (40%)] Loss: 1.2480 (1.157) Data (t): 0.001 Batch (t): 0.904, 566.632/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:53:54 | INFO | Train Epoch: 1 [ 4301312/10637090 (40%)] Loss: 1.0699 (1.156) Data (t): 0.001 Batch (t): 0.933, 565.796/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:55:25 | INFO | Train Epoch: 1 [ 4352512/10637090 (41%)] Loss: 1.1192 (1.156) Data (t): 0.001 Batch (t): 0.905, 562.706/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:56:55 | INFO | Train Epoch: 1 [ 4403712/10637090 (41%)] Loss: 1.1440 (1.155) Data (t): 0.001 Batch (t): 0.904, 565.289/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:58:28 | INFO | Train Epoch: 1 [ 4454912/10637090 (42%)] Loss: 1.2507 (1.157) Data (t): 0.001 Batch (t): 0.925, 566.284/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:59:59 | INFO | Train Epoch: 1 [ 4506112/10637090 (42%)] Loss: 1.0559 (1.155) Data (t): 0.001 Batch (t): 0.917, 567.174/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:01:32 | INFO | Train Epoch: 1 [ 4557312/10637090 (43%)] Loss: 1.1118 (1.155) Data (t): 0.001 Batch (t): 0.924, 566.448/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:03:03 | INFO | Train Epoch: 1 [ 4608512/10637090 (43%)] Loss: 1.1348 (1.155) Data (t): 0.001 Batch (t): 0.914, 565.992/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:04:34 | INFO | Train Epoch: 1 [ 4659712/10637090 (44%)] Loss: 1.1899 (1.155) Data (t): 0.001 Batch (t): 0.905, 563.363/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:06:06 | INFO | Train Epoch: 1 [ 4710912/10637090 (44%)] Loss: 1.2856 (1.157) Data (t): 0.001 Batch (t): 0.925, 567.289/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:07:38 | INFO | Train Epoch: 1 [ 4762112/10637090 (45%)] Loss: 1.1253 (1.156) Data (t): 0.001 Batch (t): 0.918, 564.719/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:09:09 | INFO | Train Epoch: 1 [ 4813312/10637090 (45%)] Loss: 1.0894 (1.155) Data (t): 0.001 Batch (t): 0.915, 565.447/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:10:42 | INFO | Train Epoch: 1 [ 4864512/10637090 (46%)] Loss: 1.1255 (1.155) Data (t): 0.001 Batch (t): 0.925, 566.633/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:12:12 | INFO | Train Epoch: 1 [ 4915712/10637090 (46%)] Loss: 1.0128 (1.154) Data (t): 0.001 Batch (t): 0.905, 566.988/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:13:43 | INFO | Train Epoch: 1 [ 4966912/10637090 (47%)] Loss: 1.2716 (1.155) Data (t): 0.001 Batch (t): 0.905, 568.637/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:15:17 | INFO | Train Epoch: 1 [ 5018112/10637090 (47%)] Loss: 1.1035 (1.154) Data (t): 0.001 Batch (t): 0.939, 566.968/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:16:48 | INFO | Train Epoch: 1 [ 5069312/10637090 (48%)] Loss: 1.0628 (1.153) Data (t): 0.001 Batch (t): 0.915, 566.736/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:18:21 | INFO | Train Epoch: 1 [ 5120512/10637090 (48%)] Loss: 1.1938 (1.154) Data (t): 0.001 Batch (t): 0.925, 564.839/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:19:51 | INFO | Train Epoch: 1 [ 5171712/10637090 (49%)] Loss: 1.1683 (1.154) Data (t): 0.001 Batch (t): 0.905, 566.970/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:21:22 | INFO | Train Epoch: 1 [ 5222912/10637090 (49%)] Loss: 1.1101 (1.154) Data (t): 0.001 Batch (t): 0.904, 564.382/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:22:56 | INFO | Train Epoch: 1 [ 5274112/10637090 (50%)] Loss: 1.2139 (1.154) Data (t): 0.001 Batch (t): 0.939, 565.488/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:24:26 | INFO | Train Epoch: 1 [ 5325312/10637090 (50%)] Loss: 1.0669 (1.153) Data (t): 0.001 Batch (t): 0.905, 567.869/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:26:00 | INFO | Train Epoch: 1 [ 5376512/10637090 (51%)] Loss: 1.1615 (1.153) Data (t): 0.001 Batch (t): 0.935, 568.790/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:27:30 | INFO | Train Epoch: 1 [ 5427712/10637090 (51%)] Loss: 1.1216 (1.153) Data (t): 0.001 Batch (t): 0.905, 569.802/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:29:01 | INFO | Train Epoch: 1 [ 5478912/10637090 (52%)] Loss: 1.2386 (1.154) Data (t): 0.001 Batch (t): 0.905, 565.213/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:30:34 | INFO | Train Epoch: 1 [ 5530112/10637090 (52%)] Loss: 1.0780 (1.153) Data (t): 0.001 Batch (t): 0.939, 566.992/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:32:05 | INFO | Train Epoch: 1 [ 5581312/10637090 (52%)] Loss: 1.1594 (1.153) Data (t): 0.001 Batch (t): 0.904, 568.733/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:33:38 | INFO | Train Epoch: 1 [ 5632512/10637090 (53%)] Loss: 1.0383 (1.152) Data (t): 0.001 Batch (t): 0.934, 566.493/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:35:09 | INFO | Train Epoch: 1 [ 5683712/10637090 (53%)] Loss: 1.2032 (1.153) Data (t): 0.001 Batch (t): 0.905, 564.756/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:36:39 | INFO | Train Epoch: 1 [ 5734912/10637090 (54%)] Loss: 1.1998 (1.153) Data (t): 0.001 Batch (t): 0.905, 562.056/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:38:12 | INFO | Train Epoch: 1 [ 5786112/10637090 (54%)] Loss: 1.0639 (1.152) Data (t): 0.001 Batch (t): 0.926, 566.306/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:39:44 | INFO | Train Epoch: 1 [ 5837312/10637090 (55%)] Loss: 1.2116 (1.153) Data (t): 0.001 Batch (t): 0.919, 566.372/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:41:15 | INFO | Train Epoch: 1 [ 5888512/10637090 (55%)] Loss: 1.0662 (1.152) Data (t): 0.001 Batch (t): 0.915, 565.906/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:42:48 | INFO | Train Epoch: 1 [ 5939712/10637090 (56%)] Loss: 1.2228 (1.153) Data (t): 0.001 Batch (t): 0.925, 565.118/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:44:18 | INFO | Train Epoch: 1 [ 5990912/10637090 (56%)] Loss: 1.0105 (1.151) Data (t): 0.001 Batch (t): 0.905, 566.870/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:45:50 | INFO | Train Epoch: 1 [ 6042112/10637090 (57%)] Loss: 1.1212 (1.151) Data (t): 0.001 Batch (t): 0.919, 567.042/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:47:22 | INFO | Train Epoch: 1 [ 6093312/10637090 (57%)] Loss: 1.2714 (1.152) Data (t): 0.001 Batch (t): 0.919, 563.793/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:48:53 | INFO | Train Epoch: 1 [ 6144512/10637090 (58%)] Loss: 1.0927 (1.152) Data (t): 0.001 Batch (t): 0.915, 565.260/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:50:26 | INFO | Train Epoch: 1 [ 6195712/10637090 (58%)] Loss: 1.2607 (1.153) Data (t): 0.001 Batch (t): 0.924, 564.631/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:51:56 | INFO | Train Epoch: 1 [ 6246912/10637090 (59%)] Loss: 1.1110 (1.152) Data (t): 0.001 Batch (t): 0.905, 564.706/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:53:28 | INFO | Train Epoch: 1 [ 6298112/10637090 (59%)] Loss: 1.0302 (1.151) Data (t): 0.001 Batch (t): 0.913, 565.761/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:55:00 | INFO | Train Epoch: 1 [ 6349312/10637090 (60%)] Loss: 1.0109 (1.150) Data (t): 0.001 Batch (t): 0.927, 566.584/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:56:32 | INFO | Train Epoch: 1 [ 6400512/10637090 (60%)] Loss: 1.1750 (1.150) Data (t): 0.001 Batch (t): 0.916, 564.912/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:58:05 | INFO | Train Epoch: 1 [ 6451712/10637090 (61%)] Loss: 1.2053 (1.151) Data (t): 0.001 Batch (t): 0.926, 562.800/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:59:35 | INFO | Train Epoch: 1 [ 6502912/10637090 (61%)] Loss: 1.2756 (1.152) Data (t): 0.001 Batch (t): 0.906, 565.667/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:01:06 | INFO | Train Epoch: 1 [ 6554112/10637090 (62%)] Loss: 1.2139 (1.152) Data (t): 0.001 Batch (t): 0.906, 566.985/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:02:39 | INFO | Train Epoch: 1 [ 6605312/10637090 (62%)] Loss: 1.1667 (1.152) Data (t): 0.001 Batch (t): 0.935, 564.301/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:04:10 | INFO | Train Epoch: 1 [ 6656512/10637090 (63%)] Loss: 0.99248 (1.151) Data (t): 0.001 Batch (t): 0.906, 566.735/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:05:43 | INFO | Train Epoch: 1 [ 6707712/10637090 (63%)] Loss: 1.0848 (1.151) Data (t): 0.001 Batch (t): 0.936, 567.274/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:07:14 | INFO | Train Epoch: 1 [ 6758912/10637090 (64%)] Loss: 1.2516 (1.151) Data (t): 0.001 Batch (t): 0.906, 565.804/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:08:45 | INFO | Train Epoch: 1 [ 6810112/10637090 (64%)] Loss: 1.1139 (1.151) Data (t): 0.001 Batch (t): 0.905, 564.323/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:10:17 | INFO | Train Epoch: 1 [ 6861312/10637090 (65%)] Loss: 0.98787 (1.150) Data (t): 0.001 Batch (t): 0.927, 563.882/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:11:49 | INFO | Train Epoch: 1 [ 6912512/10637090 (65%)] Loss: 1.1831 (1.150) Data (t): 0.001 Batch (t): 0.920, 565.372/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:13:22 | INFO | Train Epoch: 1 [ 6963712/10637090 (65%)] Loss: 1.2515 (1.151) Data (t): 0.001 Batch (t): 0.925, 565.697/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:14:53 | INFO | Train Epoch: 1 [ 7014912/10637090 (66%)] Loss: 1.1531 (1.151) Data (t): 0.001 Batch (t): 0.915, 562.846/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:16:24 | INFO | Train Epoch: 1 [ 7066112/10637090 (66%)] Loss: 1.1604 (1.151) Data (t): 0.001 Batch (t): 0.904, 565.719/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:17:56 | INFO | Train Epoch: 1 [ 7117312/10637090 (67%)] Loss: 1.1141 (1.151) Data (t): 0.001 Batch (t): 0.919, 563.724/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:19:27 | INFO | Train Epoch: 1 [ 7168512/10637090 (67%)] Loss: 1.0183 (1.150) Data (t): 0.001 Batch (t): 0.918, 567.763/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:20:59 | INFO | Train Epoch: 1 [ 7219712/10637090 (68%)] Loss: 1.2316 (1.150) Data (t): 0.001 Batch (t): 0.914, 565.280/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:22:31 | INFO | Train Epoch: 1 [ 7270912/10637090 (68%)] Loss: 1.1976 (1.151) Data (t): 0.001 Batch (t): 0.925, 565.785/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:24:02 | INFO | Train Epoch: 1 [ 7322112/10637090 (69%)] Loss: 1.1537 (1.151) Data (t): 0.001 Batch (t): 0.905, 567.193/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:25:34 | INFO | Train Epoch: 1 [ 7373312/10637090 (69%)] Loss: 1.1750 (1.151) Data (t): 0.001 Batch (t): 0.920, 565.102/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:27:06 | INFO | Train Epoch: 1 [ 7424512/10637090 (70%)] Loss: 1.1223 (1.151) Data (t): 0.001 Batch (t): 0.920, 567.425/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:28:37 | INFO | Train Epoch: 1 [ 7475712/10637090 (70%)] Loss: 1.2066 (1.151) Data (t): 0.001 Batch (t): 0.915, 565.040/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:30:10 | INFO | Train Epoch: 1 [ 7526912/10637090 (71%)] Loss: 1.0742 (1.151) Data (t): 0.001 Batch (t): 0.925, 564.063/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:31:40 | INFO | Train Epoch: 1 [ 7578112/10637090 (71%)] Loss: 1.2356 (1.151) Data (t): 0.001 Batch (t): 0.905, 565.331/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:33:11 | INFO | Train Epoch: 1 [ 7629312/10637090 (72%)] Loss: 1.0804 (1.151) Data (t): 0.001 Batch (t): 0.912, 564.789/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:34:44 | INFO | Train Epoch: 1 [ 7680512/10637090 (72%)] Loss: 1.2377 (1.151) Data (t): 0.001 Batch (t): 0.926, 565.187/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:36:16 | INFO | Train Epoch: 1 [ 7731712/10637090 (73%)] Loss: 1.2121 (1.152) Data (t): 0.001 Batch (t): 0.915, 564.164/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:37:48 | INFO | Train Epoch: 1 [ 7782912/10637090 (73%)] Loss: 1.0722 (1.151) Data (t): 0.001 Batch (t): 0.926, 565.085/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:39:19 | INFO | Train Epoch: 1 [ 7834112/10637090 (74%)] Loss: 1.1729 (1.151) Data (t): 0.001 Batch (t): 0.906, 567.682/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:40:49 | INFO | Train Epoch: 1 [ 7885312/10637090 (74%)] Loss: 1.1288 (1.151) Data (t): 0.001 Batch (t): 0.905, 564.341/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:42:23 | INFO | Train Epoch: 1 [ 7936512/10637090 (75%)] Loss: 1.1042 (1.151) Data (t): 0.001 Batch (t): 0.941, 567.728/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:43:54 | INFO | Train Epoch: 1 [ 7987712/10637090 (75%)] Loss: 1.0765 (1.150) Data (t): 0.001 Batch (t): 0.905, 565.947/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:45:26 | INFO | Train Epoch: 1 [ 8038912/10637090 (76%)] Loss: 0.98328 (1.149) Data (t): 0.001 Batch (t): 0.925, 565.552/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:46:58 | INFO | Train Epoch: 1 [ 8090112/10637090 (76%)] Loss: 0.99957 (1.148) Data (t): 0.001 Batch (t): 0.915, 565.838/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:48:28 | INFO | Train Epoch: 1 [ 8141312/10637090 (77%)] Loss: 1.2803 (1.149) Data (t): 0.001 Batch (t): 0.905, 566.837/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:50:00 | INFO | Train Epoch: 1 [ 8192512/10637090 (77%)] Loss: 1.0993 (1.149) Data (t): 0.001 Batch (t): 0.919, 567.254/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:51:33 | INFO | Train Epoch: 1 [ 8243712/10637090 (78%)] Loss: 1.2008 (1.149) Data (t): 0.001 Batch (t): 0.926, 566.216/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:53:04 | INFO | Train Epoch: 1 [ 8294912/10637090 (78%)] Loss: 1.1265 (1.149) Data (t): 0.001 Batch (t): 0.915, 565.011/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:54:37 | INFO | Train Epoch: 1 [ 8346112/10637090 (78%)] Loss: 1.0999 (1.149) Data (t): 0.001 Batch (t): 0.925, 564.941/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:56:07 | INFO | Train Epoch: 1 [ 8397312/10637090 (79%)] Loss: 1.1695 (1.149) Data (t): 0.001 Batch (t): 0.905, 567.078/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:57:39 | INFO | Train Epoch: 1 [ 8448512/10637090 (79%)] Loss: 1.1207 (1.149) Data (t): 0.001 Batch (t): 0.920, 565.229/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:59:12 | INFO | Train Epoch: 1 [ 8499712/10637090 (80%)] Loss: 1.2345 (1.149) Data (t): 0.001 Batch (t): 0.927, 564.107/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:00:44 | INFO | Train Epoch: 1 [ 8550912/10637090 (80%)] Loss: 1.1573 (1.149) Data (t): 0.001 Batch (t): 0.915, 568.099/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:02:16 | INFO | Train Epoch: 1 [ 8602112/10637090 (81%)] Loss: 0.89660 (1.148) Data (t): 0.001 Batch (t): 0.926, 563.038/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:03:47 | INFO | Train Epoch: 1 [ 8653312/10637090 (81%)] Loss: 1.0196 (1.147) Data (t): 0.001 Batch (t): 0.906, 566.984/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:05:19 | INFO | Train Epoch: 1 [ 8704512/10637090 (82%)] Loss: 1.1959 (1.147) Data (t): 0.001 Batch (t): 0.919, 564.807/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:06:51 | INFO | Train Epoch: 1 [ 8755712/10637090 (82%)] Loss: 1.2581 (1.148) Data (t): 0.001 Batch (t): 0.926, 566.843/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:08:23 | INFO | Train Epoch: 1 [ 8806912/10637090 (83%)] Loss: 1.2642 (1.149) Data (t): 0.001 Batch (t): 0.915, 564.401/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:09:55 | INFO | Train Epoch: 1 [ 8858112/10637090 (83%)] Loss: 1.0202 (1.148) Data (t): 0.001 Batch (t): 0.925, 565.253/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:11:26 | INFO | Train Epoch: 1 [ 8909312/10637090 (84%)] Loss: 1.1631 (1.148) Data (t): 0.001 Batch (t): 0.906, 567.493/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:12:57 | INFO | Train Epoch: 1 [ 8960512/10637090 (84%)] Loss: 1.0979 (1.148) Data (t): 0.001 Batch (t): 0.913, 310.349/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:14:31 | INFO | Train Epoch: 1 [ 9011712/10637090 (85%)] Loss: 1.2026 (1.148) Data (t): 0.001 Batch (t): 0.935, 564.573/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:16:02 | INFO | Train Epoch: 1 [ 9062912/10637090 (85%)] Loss: 1.1609 (1.148) Data (t): 0.001 Batch (t): 0.915, 264.616/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:17:35 | INFO | Train Epoch: 1 [ 9114112/10637090 (86%)] Loss: 1.2517 (1.149) Data (t): 0.001 Batch (t): 0.925, 564.945/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:19:05 | INFO | Train Epoch: 1 [ 9165312/10637090 (86%)] Loss: 1.1683 (1.149) Data (t): 0.001 Batch (t): 0.905, 565.435/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:20:36 | INFO | Train Epoch: 1 [ 9216512/10637090 (87%)] Loss: 1.1305 (1.149) Data (t): 0.001 Batch (t): 0.905, 565.797/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:22:09 | INFO | Train Epoch: 1 [ 9267712/10637090 (87%)] Loss: 0.97606 (1.148) Data (t): 0.001 Batch (t): 0.936, 567.587/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:23:40 | INFO | Train Epoch: 1 [ 9318912/10637090 (88%)] Loss: 1.0670 (1.147) Data (t): 0.001 Batch (t): 0.913, 567.888/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:25:13 | INFO | Train Epoch: 1 [ 9370112/10637090 (88%)] Loss: 1.1213 (1.147) Data (t): 0.001 Batch (t): 0.927, 565.866/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:26:45 | INFO | Train Epoch: 1 [ 9421312/10637090 (89%)] Loss: 1.0958 (1.147) Data (t): 0.001 Batch (t): 0.916, 561.937/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:28:15 | INFO | Train Epoch: 1 [ 9472512/10637090 (89%)] Loss: 1.0313 (1.146) Data (t): 0.001 Batch (t): 0.905, 566.182/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:29:47 | INFO | Train Epoch: 1 [ 9523712/10637090 (90%)] Loss: 1.0909 (1.146) Data (t): 0.001 Batch (t): 0.920, 566.038/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:31:20 | INFO | Train Epoch: 1 [ 9574912/10637090 (90%)] Loss: 1.2037 (1.146) Data (t): 0.001 Batch (t): 0.928, 562.930/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:32:52 | INFO | Train Epoch: 1 [ 9626112/10637090 (90%)] Loss: 1.0937 (1.146) Data (t): 0.001 Batch (t): 0.916, 564.861/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:34:24 | INFO | Train Epoch: 1 [ 9677312/10637090 (91%)] Loss: 1.0984 (1.146) Data (t): 0.001 Batch (t): 0.926, 566.187/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:35:55 | INFO | Train Epoch: 1 [ 9728512/10637090 (91%)] Loss: 1.1745 (1.146) Data (t): 0.001 Batch (t): 0.905, 565.219/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:37:27 | INFO | Train Epoch: 1 [ 9779712/10637090 (92%)] Loss: 1.0503 (1.145) Data (t): 0.001 Batch (t): 0.920, 564.235/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:38:59 | INFO | Train Epoch: 1 [ 9830912/10637090 (92%)] Loss: 1.1714 (1.145) Data (t): 0.001 Batch (t): 0.928, 567.173/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:40:31 | INFO | Train Epoch: 1 [ 9882112/10637090 (93%)] Loss: 1.1987 (1.146) Data (t): 0.001 Batch (t): 0.915, 567.815/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:42:04 | INFO | Train Epoch: 1 [ 9933312/10637090 (93%)] Loss: 1.1630 (1.146) Data (t): 0.001 Batch (t): 0.926, 565.133/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:43:34 | INFO | Train Epoch: 1 [ 9984512/10637090 (94%)] Loss: 1.1249 (1.146) Data (t): 0.001 Batch (t): 0.905, 565.255/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:45:06 | INFO | Train Epoch: 1 [10035712/10637090 (94%)] Loss: 1.0842 (1.145) Data (t): 0.001 Batch (t): 0.920, 564.436/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:46:39 | INFO | Train Epoch: 1 [10086912/10637090 (95%)] Loss: 1.1441 (1.145) Data (t): 0.001 Batch (t): 0.928, 566.782/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:48:10 | INFO | Train Epoch: 1 [10138112/10637090 (95%)] Loss: 1.1079 (1.145) Data (t): 0.001 Batch (t): 0.916, 566.827/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:49:43 | INFO | Train Epoch: 1 [10189312/10637090 (96%)] Loss: 1.2371 (1.146) Data (t): 0.001 Batch (t): 0.925, 566.392/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:51:13 | INFO | Train Epoch: 1 [10240512/10637090 (96%)] Loss: 1.0206 (1.145) Data (t): 0.001 Batch (t): 0.905, 564.624/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:52:44 | INFO | Train Epoch: 1 [10291712/10637090 (97%)] Loss: 1.0741 (1.145) Data (t): 0.001 Batch (t): 0.905, 566.348/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:54:17 | INFO | Train Epoch: 1 [10342912/10637090 (97%)] Loss: 1.1738 (1.145) Data (t): 0.001 Batch (t): 0.934, 563.307/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:55:49 | INFO | Train Epoch: 1 [10394112/10637090 (98%)] Loss: 1.1543 (1.145) Data (t): 0.001 Batch (t): 0.912, 567.610/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:57:22 | INFO | Train Epoch: 1 [10445312/10637090 (98%)] Loss: 1.1827 (1.145) Data (t): 0.001 Batch (t): 0.935, 567.477/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:58:52 | INFO | Train Epoch: 1 [10496512/10637090 (99%)] Loss: 1.1276 (1.145) Data (t): 0.001 Batch (t): 0.904, 568.564/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:00:23 | INFO | Train Epoch: 1 [10547712/10637090 (99%)] Loss: 1.2087 (1.145) Data (t): 0.001 Batch (t): 0.904, 565.236/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:01:56 | INFO | Train Epoch: 1 [10598912/10637090 (100%)] Loss: 1.3014 (1.146) Data (t): 0.001 Batch (t): 0.927, 565.369/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,00:03:04 | INFO | Train Epoch: 1 [10636800/10637090 (100%)] Loss: 1.1200 (1.146) Data (t): 0.001 Batch (t): 0.925, 569.249/s LR: 0.000000 Logit Scale: 100.000 - V4 diff --git a/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten/2024_11_26-13_26_16-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/params.txt b/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten/2024_11_26-13_26_16-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/params.txt deleted file mode 100644 index f90387916ce12e8cbd845473c7813c4b84981a36..0000000000000000000000000000000000000000 --- a/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten/2024_11_26-13_26_16-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/params.txt +++ /dev/null @@ -1,67 +0,0 @@ -batch_size: 64 -beta1: 0.9 -beta2: 0.98 -checkpoint_path: data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten/2024_11_26-13_26_16-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/checkpoints -copy_codebase: False -csv_caption_key: caption -csv_hard_captions_key: neg_caption -csv_img_key: img_path -csv_separator: , -dataset_resampled: False -dataset_type: csv -ddp_static_graph: False -debug: False -device: cuda:0 -dist_backend: nccl -dist_url: env:// -distributed: True -epochs: 2 -eps: 1e-06 -force_quick_gelu: True -gather_with_grad: False -grad_checkpointing: False -horovod: False -imagenet_v2: None -imagenet_val: None -local_loss: False -local_rank: 0 -lock_image: False -lock_image_freeze_bn_stats: False -lock_image_unlocked_groups: 0 -log_level: 20 -log_local: False -log_path: data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten/2024_11_26-13_26_16-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/out.log -logs: data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten -lr: 1e-06 -model: ViT-L-14-336 -name: 2024_11_26-13_26_16-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp -no_set_device_rank: False -norm_gradient_clip: None -precision: amp -pretrained: data/openclip-vit-14-336/openclip_model.pt -pretrained_image: False -rank: 0 -report_to: wandb -resume: None -save_frequency: 1 -save_most_recent: False -seed: 0 -skip_scheduler: False -tensorboard: False -tensorboard_path: -torchscript: False -trace: False -train_data: csv_data/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten.csv -train_num_samples: None -use_bn_sync: False -val_data: None -val_frequency: 1 -val_num_samples: None -wandb: True -wandb_notes: -wandb_project: neg-clip-plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten -warmup: 0 -wd: 0.1 -workers: 4 -world_size: 8 -zeroshot_frequency: 2 diff --git a/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten/2024_11_27-00_03_54-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/checkpoints/epoch_1.pt b/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten/2024_11_27-00_03_54-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/checkpoints/epoch_1.pt deleted file mode 100644 index 58ef04b364d56035285ac0da83d93bd95a49cc68..0000000000000000000000000000000000000000 --- a/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten/2024_11_27-00_03_54-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/checkpoints/epoch_1.pt +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:3d7c8f64799116f0a0254f77caca5de2f477f2e4e3fe2c844de0cf323b7cc72c -size 5135890710 diff --git a/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten/2024_11_27-00_03_54-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/checkpoints/epoch_2.pt b/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten/2024_11_27-00_03_54-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/checkpoints/epoch_2.pt deleted file mode 100644 index faf8b3838e26a3df5dae556211adfc81bce3da0b..0000000000000000000000000000000000000000 --- a/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten/2024_11_27-00_03_54-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/checkpoints/epoch_2.pt +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:0dc6cd1c015c0a387e2ab0e39f590384425b79634fe616c9c76ec144a6028284 -size 5135890710 diff --git a/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten/2024_11_27-00_03_54-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/out.log b/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten/2024_11_27-00_03_54-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/out.log deleted file mode 100644 index d05d1584aecb67af43c7d36aef7e30bcc92febbd..0000000000000000000000000000000000000000 --- a/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten/2024_11_27-00_03_54-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/out.log +++ /dev/null @@ -1,534 +0,0 @@ -2024-11-27,00:03:54 | INFO | Running in distributed mode with multiple processes. Device: cuda:0.Process (global: 0, local 0), total 8. -2024-11-27,00:03:54 | INFO | Loading ViT-L-14-336 model config. -2024-11-27,00:03:57 | INFO | Loading pretrained ViT-L-14-336 weights (data/openclip-vit-14-336/openclip_model.pt). -2024-11-27,00:04:03 | INFO | Model: -2024-11-27,00:04:03 | INFO | CLIP( - (visual): VisualTransformer( - (conv1): Conv2d(3, 1024, kernel_size=(14, 14), stride=(14, 14), bias=False) - (ln_pre): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) - (transformer): Transformer( - (resblocks): ModuleList( - (0-23): 24 x ResidualAttentionBlock( - (attn): MultiheadAttention( - (out_proj): NonDynamicallyQuantizableLinear(in_features=1024, out_features=1024, bias=True) - ) - (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) - (mlp): Sequential( - (c_fc): Linear(in_features=1024, out_features=4096, bias=True) - (gelu): QuickGELU() - (c_proj): Linear(in_features=4096, out_features=1024, bias=True) - ) - (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) - ) - ) - ) - (ln_post): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) - ) - (transformer): Transformer( - (resblocks): ModuleList( - (0-11): 12 x ResidualAttentionBlock( - (attn): MultiheadAttention( - (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) - ) - (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) - (mlp): Sequential( - (c_fc): Linear(in_features=768, out_features=3072, bias=True) - (gelu): QuickGELU() - (c_proj): Linear(in_features=3072, out_features=768, bias=True) - ) - (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) - ) - ) - ) - (token_embedding): Embedding(49408, 768) - (ln_final): LayerNorm((768,), eps=1e-05, elementwise_affine=True) -) -2024-11-27,00:04:03 | INFO | Params: -2024-11-27,00:04:03 | INFO | batch_size: 64 -2024-11-27,00:04:03 | INFO | beta1: 0.9 -2024-11-27,00:04:03 | INFO | beta2: 0.98 -2024-11-27,00:04:03 | INFO | checkpoint_path: data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten/2024_11_27-00_03_54-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/checkpoints -2024-11-27,00:04:03 | INFO | copy_codebase: False -2024-11-27,00:04:03 | INFO | csv_caption_key: caption -2024-11-27,00:04:03 | INFO | csv_hard_captions_key: neg_caption -2024-11-27,00:04:03 | INFO | csv_img_key: img_path -2024-11-27,00:04:03 | INFO | csv_separator: , -2024-11-27,00:04:03 | INFO | dataset_resampled: False -2024-11-27,00:04:03 | INFO | dataset_type: csv -2024-11-27,00:04:03 | INFO | ddp_static_graph: False -2024-11-27,00:04:03 | INFO | debug: False -2024-11-27,00:04:03 | INFO | device: cuda:0 -2024-11-27,00:04:03 | INFO | dist_backend: nccl -2024-11-27,00:04:03 | INFO | dist_url: env:// -2024-11-27,00:04:03 | INFO | distributed: True -2024-11-27,00:04:03 | INFO | epochs: 2 -2024-11-27,00:04:03 | INFO | eps: 1e-06 -2024-11-27,00:04:03 | INFO | force_quick_gelu: True -2024-11-27,00:04:03 | INFO | gather_with_grad: False -2024-11-27,00:04:03 | INFO | grad_checkpointing: False -2024-11-27,00:04:03 | INFO | horovod: False -2024-11-27,00:04:03 | INFO | imagenet_v2: None -2024-11-27,00:04:03 | INFO | imagenet_val: None -2024-11-27,00:04:03 | INFO | local_loss: False -2024-11-27,00:04:03 | INFO | local_rank: 0 -2024-11-27,00:04:03 | INFO | lock_image: False -2024-11-27,00:04:03 | INFO | lock_image_freeze_bn_stats: False -2024-11-27,00:04:03 | INFO | lock_image_unlocked_groups: 0 -2024-11-27,00:04:03 | INFO | log_level: 20 -2024-11-27,00:04:03 | INFO | log_local: False -2024-11-27,00:04:03 | INFO | log_path: data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten/2024_11_27-00_03_54-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/out.log -2024-11-27,00:04:03 | INFO | logs: data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten -2024-11-27,00:04:03 | INFO | lr: 5e-06 -2024-11-27,00:04:03 | INFO | model: ViT-L-14-336 -2024-11-27,00:04:03 | INFO | name: 2024_11_27-00_03_54-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp -2024-11-27,00:04:03 | INFO | no_set_device_rank: False -2024-11-27,00:04:03 | INFO | norm_gradient_clip: None -2024-11-27,00:04:03 | INFO | precision: amp -2024-11-27,00:04:03 | INFO | pretrained: data/openclip-vit-14-336/openclip_model.pt -2024-11-27,00:04:03 | INFO | pretrained_image: False -2024-11-27,00:04:03 | INFO | rank: 0 -2024-11-27,00:04:03 | INFO | report_to: wandb -2024-11-27,00:04:03 | INFO | resume: None -2024-11-27,00:04:03 | INFO | save_frequency: 1 -2024-11-27,00:04:03 | INFO | save_most_recent: False -2024-11-27,00:04:03 | INFO | seed: 0 -2024-11-27,00:04:03 | INFO | skip_scheduler: False -2024-11-27,00:04:03 | INFO | tensorboard: False -2024-11-27,00:04:03 | INFO | tensorboard_path: -2024-11-27,00:04:03 | INFO | torchscript: False -2024-11-27,00:04:03 | INFO | trace: False -2024-11-27,00:04:03 | INFO | train_data: csv_data/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten.csv -2024-11-27,00:04:03 | INFO | train_num_samples: None -2024-11-27,00:04:03 | INFO | use_bn_sync: False -2024-11-27,00:04:03 | INFO | val_data: None -2024-11-27,00:04:03 | INFO | val_frequency: 1 -2024-11-27,00:04:03 | INFO | val_num_samples: None -2024-11-27,00:04:03 | INFO | wandb: True -2024-11-27,00:04:03 | INFO | wandb_notes: -2024-11-27,00:04:03 | INFO | wandb_project: neg-clip-plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten -2024-11-27,00:04:03 | INFO | warmup: 0 -2024-11-27,00:04:03 | INFO | wd: 0.1 -2024-11-27,00:04:03 | INFO | workers: 4 -2024-11-27,00:04:03 | INFO | world_size: 8 -2024-11-27,00:04:03 | INFO | zeroshot_frequency: 2 -2024-11-27,00:05:02 | INFO | Init a wandb project! -2024-11-27,00:05:08 | INFO | Start epoch 0 -2024-11-27,00:05:16 | INFO | Train Epoch: 0 [ 512/10637090 (0%)] Loss: 5.5496 (5.550) Data (t): 2.915 Batch (t): 8.080, 63.3683/s LR: 0.000005 Logit Scale: 100.000 - V4 -2024-11-27,00:06:48 | INFO | Train Epoch: 0 [ 51712/10637090 (0%)] Loss: 2.0591 (3.804) Data (t): 0.001 Batch (t): 0.917, 560.824/s LR: 0.000005 Logit Scale: 99.996 - V4 -2024-11-27,00:08:19 | INFO | Train Epoch: 0 [ 102912/10637090 (1%)] Loss: 1.6920 (3.100) Data (t): 0.001 Batch (t): 0.909, 562.984/s LR: 0.000005 Logit Scale: 99.996 - V4 -2024-11-27,00:09:51 | INFO | Train Epoch: 0 [ 154112/10637090 (1%)] Loss: 1.6348 (2.734) Data (t): 0.001 Batch (t): 0.923, 565.382/s LR: 0.000005 Logit Scale: 99.995 - V4 -2024-11-27,00:11:26 | INFO | Train Epoch: 0 [ 205312/10637090 (2%)] Loss: 1.6666 (2.520) Data (t): 0.001 Batch (t): 0.948, 563.649/s LR: 0.000005 Logit Scale: 99.994 - V4 -2024-11-27,00:12:57 | INFO | Train Epoch: 0 [ 256512/10637090 (2%)] Loss: 1.5060 (2.351) Data (t): 0.001 Batch (t): 0.909, 563.118/s LR: 0.000005 Logit Scale: 99.991 - V4 -2024-11-27,00:14:28 | INFO | Train Epoch: 0 [ 307712/10637090 (3%)] Loss: 1.4260 (2.219) Data (t): 0.001 Batch (t): 0.908, 564.726/s LR: 0.000005 Logit Scale: 99.987 - V4 -2024-11-27,00:15:58 | INFO | Train Epoch: 0 [ 358912/10637090 (3%)] Loss: 1.5419 (2.134) Data (t): 0.001 Batch (t): 0.909, 562.432/s LR: 0.000005 Logit Scale: 99.987 - V4 -2024-11-27,00:17:30 | INFO | Train Epoch: 0 [ 410112/10637090 (4%)] Loss: 1.4776 (2.062) Data (t): 0.001 Batch (t): 0.919, 560.336/s LR: 0.000005 Logit Scale: 99.985 - V4 -2024-11-27,00:19:05 | INFO | Train Epoch: 0 [ 461312/10637090 (4%)] Loss: 1.3082 (1.986) Data (t): 0.001 Batch (t): 0.943, 562.329/s LR: 0.000005 Logit Scale: 99.984 - V4 -2024-11-27,00:20:35 | INFO | Train Epoch: 0 [ 512512/10637090 (5%)] Loss: 1.4132 (1.934) Data (t): 0.001 Batch (t): 0.909, 563.553/s LR: 0.000005 Logit Scale: 99.978 - V4 -2024-11-27,00:22:06 | INFO | Train Epoch: 0 [ 563712/10637090 (5%)] Loss: 1.3850 (1.888) Data (t): 0.001 Batch (t): 0.908, 562.223/s LR: 0.000005 Logit Scale: 99.975 - V4 -2024-11-27,00:23:37 | INFO | Train Epoch: 0 [ 614912/10637090 (6%)] Loss: 1.3926 (1.850) Data (t): 0.001 Batch (t): 0.907, 565.596/s LR: 0.000005 Logit Scale: 99.973 - V4 -2024-11-27,00:25:08 | INFO | Train Epoch: 0 [ 666112/10637090 (6%)] Loss: 1.2897 (1.810) Data (t): 0.001 Batch (t): 0.907, 565.753/s LR: 0.000005 Logit Scale: 99.970 - V4 -2024-11-27,00:26:44 | INFO | Train Epoch: 0 [ 717312/10637090 (7%)] Loss: 1.2888 (1.775) Data (t): 0.001 Batch (t): 0.966, 566.272/s LR: 0.000005 Logit Scale: 99.967 - V4 -2024-11-27,00:28:15 | INFO | Train Epoch: 0 [ 768512/10637090 (7%)] Loss: 1.4581 (1.756) Data (t): 0.001 Batch (t): 0.908, 564.712/s LR: 0.000005 Logit Scale: 99.962 - V4 -2024-11-27,00:29:46 | INFO | Train Epoch: 0 [ 819712/10637090 (8%)] Loss: 1.3129 (1.730) Data (t): 0.001 Batch (t): 0.909, 564.608/s LR: 0.000005 Logit Scale: 99.961 - V4 -2024-11-27,00:31:17 | INFO | Train Epoch: 0 [ 870912/10637090 (8%)] Loss: 1.2950 (1.705) Data (t): 0.001 Batch (t): 0.909, 566.253/s LR: 0.000005 Logit Scale: 99.958 - V4 -2024-11-27,00:32:48 | INFO | Train Epoch: 0 [ 922112/10637090 (9%)] Loss: 1.5051 (1.695) Data (t): 0.001 Batch (t): 0.908, 564.037/s LR: 0.000005 Logit Scale: 99.957 - V4 -2024-11-27,00:34:24 | INFO | Train Epoch: 0 [ 973312/10637090 (9%)] Loss: 1.3695 (1.679) Data (t): 0.001 Batch (t): 0.966, 566.314/s LR: 0.000005 Logit Scale: 99.954 - V4 -2024-11-27,00:35:55 | INFO | Train Epoch: 0 [ 1024512/10637090 (10%)] Loss: 1.2543 (1.658) Data (t): 0.001 Batch (t): 0.908, 562.764/s LR: 0.000005 Logit Scale: 99.951 - V4 -2024-11-27,00:37:26 | INFO | Train Epoch: 0 [ 1075712/10637090 (10%)] Loss: 1.4015 (1.647) Data (t): 0.001 Batch (t): 0.907, 563.740/s LR: 0.000005 Logit Scale: 99.951 - V4 -2024-11-27,00:38:56 | INFO | Train Epoch: 0 [ 1126912/10637090 (11%)] Loss: 1.2620 (1.630) Data (t): 0.001 Batch (t): 0.906, 566.337/s LR: 0.000005 Logit Scale: 99.948 - V4 -2024-11-27,00:40:27 | INFO | Train Epoch: 0 [ 1178112/10637090 (11%)] Loss: 1.3336 (1.618) Data (t): 0.001 Batch (t): 0.908, 564.522/s LR: 0.000005 Logit Scale: 99.947 - V4 -2024-11-27,00:42:03 | INFO | Train Epoch: 0 [ 1229312/10637090 (12%)] Loss: 1.3807 (1.608) Data (t): 0.001 Batch (t): 0.963, 565.424/s LR: 0.000005 Logit Scale: 99.944 - V4 -2024-11-27,00:43:35 | INFO | Train Epoch: 0 [ 1280512/10637090 (12%)] Loss: 1.2279 (1.594) Data (t): 0.001 Batch (t): 0.917, 563.331/s LR: 0.000005 Logit Scale: 99.944 - V4 -2024-11-27,00:45:06 | INFO | Train Epoch: 0 [ 1331712/10637090 (13%)] Loss: 1.2738 (1.582) Data (t): 0.001 Batch (t): 0.907, 561.282/s LR: 0.000005 Logit Scale: 99.942 - V4 -2024-11-27,00:46:37 | INFO | Train Epoch: 0 [ 1382912/10637090 (13%)] Loss: 1.3069 (1.572) Data (t): 0.001 Batch (t): 0.908, 566.991/s LR: 0.000005 Logit Scale: 99.939 - V4 -2024-11-27,00:48:07 | INFO | Train Epoch: 0 [ 1434112/10637090 (13%)] Loss: 1.3902 (1.566) Data (t): 0.001 Batch (t): 0.907, 565.220/s LR: 0.000005 Logit Scale: 99.936 - V4 -2024-11-27,00:49:39 | INFO | Train Epoch: 0 [ 1485312/10637090 (14%)] Loss: 1.3253 (1.558) Data (t): 0.001 Batch (t): 0.917, 560.577/s LR: 0.000005 Logit Scale: 99.935 - V4 -2024-11-27,00:51:14 | INFO | Train Epoch: 0 [ 1536512/10637090 (14%)] Loss: 1.4161 (1.553) Data (t): 0.001 Batch (t): 0.948, 565.131/s LR: 0.000005 Logit Scale: 99.931 - V4 -2024-11-27,00:52:45 | INFO | Train Epoch: 0 [ 1587712/10637090 (15%)] Loss: 1.2922 (1.545) Data (t): 0.001 Batch (t): 0.908, 562.395/s LR: 0.000005 Logit Scale: 99.928 - V4 -2024-11-27,00:54:15 | INFO | Train Epoch: 0 [ 1638912/10637090 (15%)] Loss: 1.2860 (1.537) Data (t): 0.001 Batch (t): 0.907, 564.746/s LR: 0.000005 Logit Scale: 99.928 - V4 -2024-11-27,00:55:46 | INFO | Train Epoch: 0 [ 1690112/10637090 (16%)] Loss: 1.3566 (1.532) Data (t): 0.001 Batch (t): 0.907, 563.971/s LR: 0.000005 Logit Scale: 99.926 - V4 -2024-11-27,00:57:17 | INFO | Train Epoch: 0 [ 1741312/10637090 (16%)] Loss: 1.2082 (1.522) Data (t): 0.001 Batch (t): 0.906, 563.194/s LR: 0.000005 Logit Scale: 99.925 - V4 -2024-11-27,00:58:53 | INFO | Train Epoch: 0 [ 1792512/10637090 (17%)] Loss: 1.1899 (1.513) Data (t): 0.001 Batch (t): 0.965, 563.591/s LR: 0.000005 Logit Scale: 99.921 - V4 -2024-11-27,01:00:24 | INFO | Train Epoch: 0 [ 1843712/10637090 (17%)] Loss: 1.3821 (1.510) Data (t): 0.001 Batch (t): 0.905, 565.801/s LR: 0.000005 Logit Scale: 99.921 - V4 -2024-11-27,01:01:54 | INFO | Train Epoch: 0 [ 1894912/10637090 (18%)] Loss: 1.3560 (1.506) Data (t): 0.001 Batch (t): 0.907, 563.000/s LR: 0.000005 Logit Scale: 99.920 - V4 -2024-11-27,01:03:25 | INFO | Train Epoch: 0 [ 1946112/10637090 (18%)] Loss: 1.3263 (1.501) Data (t): 0.001 Batch (t): 0.906, 563.843/s LR: 0.000005 Logit Scale: 99.919 - V4 -2024-11-27,01:04:56 | INFO | Train Epoch: 0 [ 1997312/10637090 (19%)] Loss: 1.1140 (1.491) Data (t): 0.001 Batch (t): 0.906, 561.312/s LR: 0.000005 Logit Scale: 99.918 - V4 -2024-11-27,01:06:32 | INFO | Train Epoch: 0 [ 2048512/10637090 (19%)] Loss: 1.2637 (1.486) Data (t): 0.001 Batch (t): 0.965, 565.332/s LR: 0.000005 Logit Scale: 99.916 - V4 -2024-11-27,01:08:03 | INFO | Train Epoch: 0 [ 2099712/10637090 (20%)] Loss: 1.2427 (1.480) Data (t): 0.001 Batch (t): 0.907, 564.089/s LR: 0.000005 Logit Scale: 99.914 - V4 -2024-11-27,01:09:33 | INFO | Train Epoch: 0 [ 2150912/10637090 (20%)] Loss: 1.0425 (1.470) Data (t): 0.001 Batch (t): 0.908, 563.341/s LR: 0.000005 Logit Scale: 99.911 - V4 -2024-11-27,01:11:04 | INFO | Train Epoch: 0 [ 2202112/10637090 (21%)] Loss: 1.2554 (1.465) Data (t): 0.001 Batch (t): 0.909, 566.173/s LR: 0.000005 Logit Scale: 99.912 - V4 -2024-11-27,01:12:35 | INFO | Train Epoch: 0 [ 2253312/10637090 (21%)] Loss: 1.1487 (1.458) Data (t): 0.001 Batch (t): 0.909, 562.763/s LR: 0.000005 Logit Scale: 99.910 - V4 -2024-11-27,01:14:12 | INFO | Train Epoch: 0 [ 2304512/10637090 (22%)] Loss: 1.2780 (1.454) Data (t): 0.001 Batch (t): 0.967, 563.359/s LR: 0.000005 Logit Scale: 99.909 - V4 -2024-11-27,01:15:43 | INFO | Train Epoch: 0 [ 2355712/10637090 (22%)] Loss: 1.1924 (1.448) Data (t): 0.001 Batch (t): 0.909, 564.695/s LR: 0.000005 Logit Scale: 99.907 - V4 -2024-11-27,01:17:14 | INFO | Train Epoch: 0 [ 2406912/10637090 (23%)] Loss: 1.2804 (1.445) Data (t): 0.001 Batch (t): 0.908, 564.077/s LR: 0.000005 Logit Scale: 99.904 - V4 -2024-11-27,01:18:44 | INFO | Train Epoch: 0 [ 2458112/10637090 (23%)] Loss: 1.2421 (1.441) Data (t): 0.001 Batch (t): 0.908, 562.105/s LR: 0.000005 Logit Scale: 99.905 - V4 -2024-11-27,01:20:15 | INFO | Train Epoch: 0 [ 2509312/10637090 (24%)] Loss: 1.2321 (1.437) Data (t): 0.001 Batch (t): 0.907, 564.190/s LR: 0.000005 Logit Scale: 99.904 - V4 -2024-11-27,01:21:50 | INFO | Train Epoch: 0 [ 2560512/10637090 (24%)] Loss: 1.3552 (1.435) Data (t): 0.001 Batch (t): 0.948, 564.421/s LR: 0.000005 Logit Scale: 99.902 - V4 -2024-11-27,01:23:22 | INFO | Train Epoch: 0 [ 2611712/10637090 (25%)] Loss: 1.2933 (1.432) Data (t): 0.001 Batch (t): 0.925, 565.797/s LR: 0.000005 Logit Scale: 99.901 - V4 -2024-11-27,01:24:53 | INFO | Train Epoch: 0 [ 2662912/10637090 (25%)] Loss: 1.1608 (1.427) Data (t): 0.001 Batch (t): 0.907, 565.335/s LR: 0.000005 Logit Scale: 99.898 - V4 -2024-11-27,01:26:24 | INFO | Train Epoch: 0 [ 2714112/10637090 (26%)] Loss: 1.2296 (1.424) Data (t): 0.001 Batch (t): 0.907, 563.085/s LR: 0.000005 Logit Scale: 99.898 - V4 -2024-11-27,01:27:54 | INFO | Train Epoch: 0 [ 2765312/10637090 (26%)] Loss: 1.2679 (1.421) Data (t): 0.001 Batch (t): 0.906, 564.493/s LR: 0.000005 Logit Scale: 99.895 - V4 -2024-11-27,01:29:25 | INFO | Train Epoch: 0 [ 2816512/10637090 (26%)] Loss: 1.0745 (1.415) Data (t): 0.001 Batch (t): 0.907, 563.675/s LR: 0.000005 Logit Scale: 99.896 - V4 -2024-11-27,01:31:02 | INFO | Train Epoch: 0 [ 2867712/10637090 (27%)] Loss: 1.2342 (1.411) Data (t): 0.001 Batch (t): 0.967, 562.898/s LR: 0.000005 Logit Scale: 99.893 - V4 -2024-11-27,01:32:33 | INFO | Train Epoch: 0 [ 2918912/10637090 (27%)] Loss: 1.2334 (1.408) Data (t): 0.001 Batch (t): 0.907, 564.139/s LR: 0.000005 Logit Scale: 99.891 - V4 -2024-11-27,01:34:03 | INFO | Train Epoch: 0 [ 2970112/10637090 (28%)] Loss: 1.2475 (1.406) Data (t): 0.001 Batch (t): 0.906, 566.407/s LR: 0.000005 Logit Scale: 99.889 - V4 -2024-11-27,01:35:34 | INFO | Train Epoch: 0 [ 3021312/10637090 (28%)] Loss: 1.1165 (1.401) Data (t): 0.001 Batch (t): 0.907, 564.933/s LR: 0.000005 Logit Scale: 99.890 - V4 -2024-11-27,01:37:04 | INFO | Train Epoch: 0 [ 3072512/10637090 (29%)] Loss: 1.2125 (1.398) Data (t): 0.001 Batch (t): 0.906, 564.736/s LR: 0.000005 Logit Scale: 99.890 - V4 -2024-11-27,01:38:42 | INFO | Train Epoch: 0 [ 3123712/10637090 (29%)] Loss: 1.1519 (1.394) Data (t): 0.001 Batch (t): 0.972, 562.768/s LR: 0.000005 Logit Scale: 99.889 - V4 -2024-11-27,01:40:12 | INFO | Train Epoch: 0 [ 3174912/10637090 (30%)] Loss: 1.0469 (1.388) Data (t): 0.001 Batch (t): 0.906, 564.360/s LR: 0.000005 Logit Scale: 99.887 - V4 -2024-11-27,01:41:43 | INFO | Train Epoch: 0 [ 3226112/10637090 (30%)] Loss: 1.2218 (1.386) Data (t): 0.001 Batch (t): 0.906, 564.814/s LR: 0.000005 Logit Scale: 99.883 - V4 -2024-11-27,01:43:13 | INFO | Train Epoch: 0 [ 3277312/10637090 (31%)] Loss: 1.2339 (1.383) Data (t): 0.001 Batch (t): 0.906, 564.536/s LR: 0.000005 Logit Scale: 99.882 - V4 -2024-11-27,01:44:44 | INFO | Train Epoch: 0 [ 3328512/10637090 (31%)] Loss: 1.2602 (1.381) Data (t): 0.001 Batch (t): 0.906, 565.416/s LR: 0.000005 Logit Scale: 99.883 - V4 -2024-11-27,01:46:20 | INFO | Train Epoch: 0 [ 3379712/10637090 (32%)] Loss: 1.2756 (1.380) Data (t): 0.001 Batch (t): 0.957, 569.027/s LR: 0.000005 Logit Scale: 99.883 - V4 -2024-11-27,01:47:50 | INFO | Train Epoch: 0 [ 3430912/10637090 (32%)] Loss: 1.1577 (1.377) Data (t): 0.001 Batch (t): 0.906, 565.673/s LR: 0.000005 Logit Scale: 99.883 - V4 -2024-11-27,01:49:21 | INFO | Train Epoch: 0 [ 3482112/10637090 (33%)] Loss: 1.3642 (1.376) Data (t): 0.001 Batch (t): 0.907, 564.255/s LR: 0.000005 Logit Scale: 99.883 - V4 -2024-11-27,01:50:52 | INFO | Train Epoch: 0 [ 3533312/10637090 (33%)] Loss: 1.0692 (1.372) Data (t): 0.001 Batch (t): 0.907, 562.650/s LR: 0.000005 Logit Scale: 99.883 - V4 -2024-11-27,01:52:22 | INFO | Train Epoch: 0 [ 3584512/10637090 (34%)] Loss: 1.0010 (1.367) Data (t): 0.001 Batch (t): 0.905, 567.810/s LR: 0.000005 Logit Scale: 99.881 - V4 -2024-11-27,01:53:57 | INFO | Train Epoch: 0 [ 3635712/10637090 (34%)] Loss: 1.2472 (1.365) Data (t): 0.001 Batch (t): 0.953, 567.326/s LR: 0.000005 Logit Scale: 99.879 - V4 -2024-11-27,01:55:29 | INFO | Train Epoch: 0 [ 3686912/10637090 (35%)] Loss: 1.1852 (1.363) Data (t): 0.001 Batch (t): 0.915, 562.093/s LR: 0.000005 Logit Scale: 99.879 - V4 -2024-11-27,01:57:00 | INFO | Train Epoch: 0 [ 3738112/10637090 (35%)] Loss: 1.2980 (1.362) Data (t): 0.001 Batch (t): 0.906, 564.317/s LR: 0.000005 Logit Scale: 99.880 - V4 -2024-11-27,01:58:30 | INFO | Train Epoch: 0 [ 3789312/10637090 (36%)] Loss: 1.0737 (1.358) Data (t): 0.001 Batch (t): 0.907, 563.488/s LR: 0.000005 Logit Scale: 99.878 - V4 -2024-11-27,02:00:01 | INFO | Train Epoch: 0 [ 3840512/10637090 (36%)] Loss: 1.2507 (1.357) Data (t): 0.001 Batch (t): 0.906, 564.094/s LR: 0.000005 Logit Scale: 99.877 - V4 -2024-11-27,02:01:35 | INFO | Train Epoch: 0 [ 3891712/10637090 (37%)] Loss: 1.2840 (1.356) Data (t): 0.001 Batch (t): 0.937, 566.199/s LR: 0.000005 Logit Scale: 99.877 - V4 -2024-11-27,02:03:09 | INFO | Train Epoch: 0 [ 3942912/10637090 (37%)] Loss: 1.2218 (1.354) Data (t): 0.001 Batch (t): 0.942, 563.305/s LR: 0.000005 Logit Scale: 99.878 - V4 -2024-11-27,02:04:40 | INFO | Train Epoch: 0 [ 3994112/10637090 (38%)] Loss: 1.1864 (1.352) Data (t): 0.001 Batch (t): 0.908, 561.669/s LR: 0.000005 Logit Scale: 99.878 - V4 -2024-11-27,02:06:10 | INFO | Train Epoch: 0 [ 4045312/10637090 (38%)] Loss: 1.1524 (1.349) Data (t): 0.001 Batch (t): 0.908, 561.194/s LR: 0.000005 Logit Scale: 99.878 - V4 -2024-11-27,02:07:41 | INFO | Train Epoch: 0 [ 4096512/10637090 (39%)] Loss: 1.0936 (1.346) Data (t): 0.001 Batch (t): 0.907, 563.653/s LR: 0.000005 Logit Scale: 99.880 - V4 -2024-11-27,02:09:12 | INFO | Train Epoch: 0 [ 4147712/10637090 (39%)] Loss: 1.0619 (1.343) Data (t): 0.001 Batch (t): 0.907, 567.106/s LR: 0.000005 Logit Scale: 99.876 - V4 -2024-11-27,02:10:49 | INFO | Train Epoch: 0 [ 4198912/10637090 (39%)] Loss: 1.2763 (1.342) Data (t): 0.001 Batch (t): 0.973, 566.887/s LR: 0.000005 Logit Scale: 99.878 - V4 -2024-11-27,02:12:20 | INFO | Train Epoch: 0 [ 4250112/10637090 (40%)] Loss: 1.2116 (1.340) Data (t): 0.001 Batch (t): 0.906, 565.165/s LR: 0.000005 Logit Scale: 99.879 - V4 -2024-11-27,02:13:50 | INFO | Train Epoch: 0 [ 4301312/10637090 (40%)] Loss: 1.2519 (1.339) Data (t): 0.001 Batch (t): 0.907, 565.488/s LR: 0.000005 Logit Scale: 99.878 - V4 -2024-11-27,02:15:21 | INFO | Train Epoch: 0 [ 4352512/10637090 (41%)] Loss: 1.0579 (1.336) Data (t): 0.001 Batch (t): 0.907, 565.474/s LR: 0.000005 Logit Scale: 99.879 - V4 -2024-11-27,02:16:52 | INFO | Train Epoch: 0 [ 4403712/10637090 (41%)] Loss: 1.1732 (1.334) Data (t): 0.001 Batch (t): 0.905, 566.659/s LR: 0.000004 Logit Scale: 99.879 - V4 -2024-11-27,02:18:29 | INFO | Train Epoch: 0 [ 4454912/10637090 (42%)] Loss: 1.3028 (1.334) Data (t): 0.001 Batch (t): 0.973, 564.583/s LR: 0.000004 Logit Scale: 99.877 - V4 -2024-11-27,02:20:00 | INFO | Train Epoch: 0 [ 4506112/10637090 (42%)] Loss: 1.1581 (1.332) Data (t): 0.001 Batch (t): 0.907, 564.599/s LR: 0.000004 Logit Scale: 99.876 - V4 -2024-11-27,02:21:30 | INFO | Train Epoch: 0 [ 4557312/10637090 (43%)] Loss: 1.1656 (1.330) Data (t): 0.001 Batch (t): 0.908, 565.709/s LR: 0.000004 Logit Scale: 99.876 - V4 -2024-11-27,02:23:01 | INFO | Train Epoch: 0 [ 4608512/10637090 (43%)] Loss: 1.2345 (1.329) Data (t): 0.001 Batch (t): 0.908, 567.399/s LR: 0.000004 Logit Scale: 99.878 - V4 -2024-11-27,02:24:32 | INFO | Train Epoch: 0 [ 4659712/10637090 (44%)] Loss: 1.2057 (1.328) Data (t): 0.001 Batch (t): 0.907, 565.329/s LR: 0.000004 Logit Scale: 99.878 - V4 -2024-11-27,02:26:08 | INFO | Train Epoch: 0 [ 4710912/10637090 (44%)] Loss: 1.1793 (1.326) Data (t): 0.001 Batch (t): 0.961, 567.611/s LR: 0.000004 Logit Scale: 99.875 - V4 -2024-11-27,02:27:40 | INFO | Train Epoch: 0 [ 4762112/10637090 (45%)] Loss: 1.1822 (1.324) Data (t): 0.001 Batch (t): 0.916, 564.163/s LR: 0.000004 Logit Scale: 99.877 - V4 -2024-11-27,02:29:10 | INFO | Train Epoch: 0 [ 4813312/10637090 (45%)] Loss: 1.1163 (1.322) Data (t): 0.001 Batch (t): 0.906, 565.708/s LR: 0.000004 Logit Scale: 99.879 - V4 -2024-11-27,02:30:41 | INFO | Train Epoch: 0 [ 4864512/10637090 (46%)] Loss: 1.2176 (1.321) Data (t): 0.001 Batch (t): 0.907, 566.702/s LR: 0.000004 Logit Scale: 99.877 - V4 -2024-11-27,02:32:12 | INFO | Train Epoch: 0 [ 4915712/10637090 (46%)] Loss: 1.1790 (1.320) Data (t): 0.001 Batch (t): 0.906, 565.709/s LR: 0.000004 Logit Scale: 99.876 - V4 -2024-11-27,02:33:48 | INFO | Train Epoch: 0 [ 4966912/10637090 (47%)] Loss: 1.1488 (1.318) Data (t): 0.001 Batch (t): 0.962, 565.630/s LR: 0.000004 Logit Scale: 99.877 - V4 -2024-11-27,02:35:20 | INFO | Train Epoch: 0 [ 5018112/10637090 (47%)] Loss: 1.2160 (1.317) Data (t): 0.001 Batch (t): 0.917, 564.620/s LR: 0.000004 Logit Scale: 99.878 - V4 -2024-11-27,02:36:50 | INFO | Train Epoch: 0 [ 5069312/10637090 (48%)] Loss: 1.0144 (1.314) Data (t): 0.001 Batch (t): 0.907, 565.310/s LR: 0.000004 Logit Scale: 99.879 - V4 -2024-11-27,02:38:21 | INFO | Train Epoch: 0 [ 5120512/10637090 (48%)] Loss: 1.0713 (1.311) Data (t): 0.001 Batch (t): 0.906, 564.709/s LR: 0.000004 Logit Scale: 99.880 - V4 -2024-11-27,02:39:52 | INFO | Train Epoch: 0 [ 5171712/10637090 (49%)] Loss: 1.1245 (1.310) Data (t): 0.001 Batch (t): 0.907, 564.576/s LR: 0.000004 Logit Scale: 99.877 - V4 -2024-11-27,02:41:27 | INFO | Train Epoch: 0 [ 5222912/10637090 (49%)] Loss: 1.1152 (1.308) Data (t): 0.001 Batch (t): 0.952, 323.467/s LR: 0.000004 Logit Scale: 99.877 - V4 -2024-11-27,02:42:59 | INFO | Train Epoch: 0 [ 5274112/10637090 (50%)] Loss: 1.0976 (1.306) Data (t): 0.001 Batch (t): 0.926, 566.396/s LR: 0.000004 Logit Scale: 99.879 - V4 -2024-11-27,02:44:30 | INFO | Train Epoch: 0 [ 5325312/10637090 (50%)] Loss: 1.1582 (1.304) Data (t): 0.001 Batch (t): 0.906, 565.759/s LR: 0.000004 Logit Scale: 99.879 - V4 -2024-11-27,02:46:01 | INFO | Train Epoch: 0 [ 5376512/10637090 (51%)] Loss: 1.0433 (1.302) Data (t): 0.001 Batch (t): 0.905, 563.090/s LR: 0.000004 Logit Scale: 99.878 - V4 -2024-11-27,02:47:31 | INFO | Train Epoch: 0 [ 5427712/10637090 (51%)] Loss: 1.1670 (1.301) Data (t): 0.001 Batch (t): 0.907, 565.995/s LR: 0.000004 Logit Scale: 99.878 - V4 -2024-11-27,02:49:02 | INFO | Train Epoch: 0 [ 5478912/10637090 (52%)] Loss: 1.2039 (1.300) Data (t): 0.001 Batch (t): 0.907, 565.876/s LR: 0.000004 Logit Scale: 99.879 - V4 -2024-11-27,02:50:38 | INFO | Train Epoch: 0 [ 5530112/10637090 (52%)] Loss: 1.1855 (1.299) Data (t): 0.001 Batch (t): 0.964, 565.582/s LR: 0.000004 Logit Scale: 99.881 - V4 -2024-11-27,02:52:09 | INFO | Train Epoch: 0 [ 5581312/10637090 (52%)] Loss: 1.0581 (1.296) Data (t): 0.001 Batch (t): 0.905, 563.976/s LR: 0.000004 Logit Scale: 99.883 - V4 -2024-11-27,02:53:40 | INFO | Train Epoch: 0 [ 5632512/10637090 (53%)] Loss: 1.1543 (1.295) Data (t): 0.001 Batch (t): 0.907, 565.803/s LR: 0.000004 Logit Scale: 99.884 - V4 -2024-11-27,02:55:10 | INFO | Train Epoch: 0 [ 5683712/10637090 (53%)] Loss: 1.0941 (1.293) Data (t): 0.001 Batch (t): 0.906, 565.987/s LR: 0.000004 Logit Scale: 99.883 - V4 -2024-11-27,02:56:41 | INFO | Train Epoch: 0 [ 5734912/10637090 (54%)] Loss: 1.0296 (1.291) Data (t): 0.001 Batch (t): 0.908, 563.915/s LR: 0.000004 Logit Scale: 99.887 - V4 -2024-11-27,02:58:17 | INFO | Train Epoch: 0 [ 5786112/10637090 (54%)] Loss: 1.0786 (1.289) Data (t): 0.001 Batch (t): 0.962, 560.123/s LR: 0.000004 Logit Scale: 99.887 - V4 -2024-11-27,02:59:49 | INFO | Train Epoch: 0 [ 5837312/10637090 (55%)] Loss: 0.97867 (1.287) Data (t): 0.001 Batch (t): 0.917, 559.717/s LR: 0.000004 Logit Scale: 99.887 - V4 -2024-11-27,03:01:20 | INFO | Train Epoch: 0 [ 5888512/10637090 (55%)] Loss: 1.1695 (1.285) Data (t): 0.001 Batch (t): 0.907, 564.841/s LR: 0.000004 Logit Scale: 99.889 - V4 -2024-11-27,03:02:50 | INFO | Train Epoch: 0 [ 5939712/10637090 (56%)] Loss: 1.2086 (1.285) Data (t): 0.001 Batch (t): 0.907, 565.609/s LR: 0.000004 Logit Scale: 99.891 - V4 -2024-11-27,03:04:21 | INFO | Train Epoch: 0 [ 5990912/10637090 (56%)] Loss: 1.1527 (1.284) Data (t): 0.001 Batch (t): 0.907, 564.843/s LR: 0.000004 Logit Scale: 99.894 - V4 -2024-11-27,03:05:57 | INFO | Train Epoch: 0 [ 6042112/10637090 (57%)] Loss: 0.91726 (1.281) Data (t): 0.001 Batch (t): 0.963, 567.789/s LR: 0.000004 Logit Scale: 99.894 - V4 -2024-11-27,03:07:29 | INFO | Train Epoch: 0 [ 6093312/10637090 (57%)] Loss: 1.0480 (1.279) Data (t): 0.001 Batch (t): 0.914, 566.422/s LR: 0.000004 Logit Scale: 99.894 - V4 -2024-11-27,03:08:59 | INFO | Train Epoch: 0 [ 6144512/10637090 (58%)] Loss: 1.2131 (1.278) Data (t): 0.001 Batch (t): 0.906, 564.047/s LR: 0.000004 Logit Scale: 99.897 - V4 -2024-11-27,03:10:30 | INFO | Train Epoch: 0 [ 6195712/10637090 (58%)] Loss: 1.1538 (1.277) Data (t): 0.001 Batch (t): 0.907, 564.773/s LR: 0.000004 Logit Scale: 99.899 - V4 -2024-11-27,03:12:00 | INFO | Train Epoch: 0 [ 6246912/10637090 (59%)] Loss: 1.0573 (1.275) Data (t): 0.001 Batch (t): 0.904, 565.742/s LR: 0.000004 Logit Scale: 99.898 - V4 -2024-11-27,03:13:36 | INFO | Train Epoch: 0 [ 6298112/10637090 (59%)] Loss: 1.0688 (1.274) Data (t): 0.001 Batch (t): 0.953, 565.402/s LR: 0.000004 Logit Scale: 99.900 - V4 -2024-11-27,03:15:08 | INFO | Train Epoch: 0 [ 6349312/10637090 (60%)] Loss: 1.1105 (1.272) Data (t): 0.001 Batch (t): 0.927, 565.196/s LR: 0.000004 Logit Scale: 99.902 - V4 -2024-11-27,03:16:39 | INFO | Train Epoch: 0 [ 6400512/10637090 (60%)] Loss: 1.2064 (1.272) Data (t): 0.001 Batch (t): 0.906, 565.563/s LR: 0.000004 Logit Scale: 99.903 - V4 -2024-11-27,03:18:10 | INFO | Train Epoch: 0 [ 6451712/10637090 (61%)] Loss: 1.1697 (1.271) Data (t): 0.001 Batch (t): 0.906, 565.669/s LR: 0.000004 Logit Scale: 99.904 - V4 -2024-11-27,03:19:40 | INFO | Train Epoch: 0 [ 6502912/10637090 (61%)] Loss: 1.1387 (1.270) Data (t): 0.001 Batch (t): 0.906, 567.191/s LR: 0.000004 Logit Scale: 99.905 - V4 -2024-11-27,03:21:14 | INFO | Train Epoch: 0 [ 6554112/10637090 (62%)] Loss: 1.0262 (1.268) Data (t): 0.001 Batch (t): 0.938, 319.866/s LR: 0.000004 Logit Scale: 99.907 - V4 -2024-11-27,03:22:48 | INFO | Train Epoch: 0 [ 6605312/10637090 (62%)] Loss: 1.0539 (1.266) Data (t): 0.001 Batch (t): 0.941, 565.541/s LR: 0.000004 Logit Scale: 99.909 - V4 -2024-11-27,03:24:19 | INFO | Train Epoch: 0 [ 6656512/10637090 (63%)] Loss: 1.0189 (1.265) Data (t): 0.001 Batch (t): 0.905, 562.521/s LR: 0.000004 Logit Scale: 99.912 - V4 -2024-11-27,03:25:49 | INFO | Train Epoch: 0 [ 6707712/10637090 (63%)] Loss: 1.0560 (1.263) Data (t): 0.001 Batch (t): 0.906, 566.932/s LR: 0.000004 Logit Scale: 99.911 - V4 -2024-11-27,03:27:20 | INFO | Train Epoch: 0 [ 6758912/10637090 (64%)] Loss: 1.0828 (1.262) Data (t): 0.001 Batch (t): 0.906, 564.973/s LR: 0.000004 Logit Scale: 99.911 - V4 -2024-11-27,03:28:50 | INFO | Train Epoch: 0 [ 6810112/10637090 (64%)] Loss: 1.1878 (1.261) Data (t): 0.001 Batch (t): 0.905, 566.106/s LR: 0.000004 Logit Scale: 99.914 - V4 -2024-11-27,03:30:27 | INFO | Train Epoch: 0 [ 6861312/10637090 (65%)] Loss: 1.1957 (1.261) Data (t): 0.001 Batch (t): 0.961, 566.985/s LR: 0.000004 Logit Scale: 99.915 - V4 -2024-11-27,03:31:58 | INFO | Train Epoch: 0 [ 6912512/10637090 (65%)] Loss: 1.0947 (1.259) Data (t): 0.001 Batch (t): 0.915, 565.294/s LR: 0.000004 Logit Scale: 99.918 - V4 -2024-11-27,03:33:29 | INFO | Train Epoch: 0 [ 6963712/10637090 (65%)] Loss: 1.1910 (1.259) Data (t): 0.001 Batch (t): 0.905, 564.840/s LR: 0.000004 Logit Scale: 99.920 - V4 -2024-11-27,03:34:59 | INFO | Train Epoch: 0 [ 7014912/10637090 (66%)] Loss: 0.98965 (1.257) Data (t): 0.001 Batch (t): 0.905, 566.808/s LR: 0.000004 Logit Scale: 99.919 - V4 -2024-11-27,03:36:30 | INFO | Train Epoch: 0 [ 7066112/10637090 (66%)] Loss: 1.2333 (1.257) Data (t): 0.001 Batch (t): 0.906, 563.277/s LR: 0.000004 Logit Scale: 99.922 - V4 -2024-11-27,03:38:06 | INFO | Train Epoch: 0 [ 7117312/10637090 (67%)] Loss: 1.0522 (1.255) Data (t): 0.001 Batch (t): 0.964, 261.884/s LR: 0.000004 Logit Scale: 99.923 - V4 -2024-11-27,03:39:38 | INFO | Train Epoch: 0 [ 7168512/10637090 (67%)] Loss: 1.1185 (1.254) Data (t): 0.001 Batch (t): 0.917, 563.931/s LR: 0.000004 Logit Scale: 99.926 - V4 -2024-11-27,03:41:08 | INFO | Train Epoch: 0 [ 7219712/10637090 (68%)] Loss: 1.0537 (1.253) Data (t): 0.001 Batch (t): 0.905, 567.143/s LR: 0.000004 Logit Scale: 99.928 - V4 -2024-11-27,03:42:39 | INFO | Train Epoch: 0 [ 7270912/10637090 (68%)] Loss: 1.2028 (1.253) Data (t): 0.001 Batch (t): 0.907, 565.542/s LR: 0.000004 Logit Scale: 99.932 - V4 -2024-11-27,03:44:10 | INFO | Train Epoch: 0 [ 7322112/10637090 (69%)] Loss: 1.1686 (1.252) Data (t): 0.001 Batch (t): 0.908, 566.566/s LR: 0.000004 Logit Scale: 99.932 - V4 -2024-11-27,03:45:45 | INFO | Train Epoch: 0 [ 7373312/10637090 (69%)] Loss: 1.0920 (1.251) Data (t): 0.001 Batch (t): 0.953, 565.327/s LR: 0.000004 Logit Scale: 99.935 - V4 -2024-11-27,03:47:18 | INFO | Train Epoch: 0 [ 7424512/10637090 (70%)] Loss: 1.1358 (1.250) Data (t): 0.001 Batch (t): 0.928, 567.053/s LR: 0.000004 Logit Scale: 99.937 - V4 -2024-11-27,03:48:48 | INFO | Train Epoch: 0 [ 7475712/10637090 (70%)] Loss: 1.1103 (1.249) Data (t): 0.001 Batch (t): 0.907, 564.318/s LR: 0.000004 Logit Scale: 99.939 - V4 -2024-11-27,03:50:19 | INFO | Train Epoch: 0 [ 7526912/10637090 (71%)] Loss: 1.1976 (1.249) Data (t): 0.001 Batch (t): 0.906, 566.610/s LR: 0.000004 Logit Scale: 99.941 - V4 -2024-11-27,03:51:50 | INFO | Train Epoch: 0 [ 7578112/10637090 (71%)] Loss: 1.0606 (1.248) Data (t): 0.001 Batch (t): 0.906, 566.326/s LR: 0.000004 Logit Scale: 99.941 - V4 -2024-11-27,03:53:25 | INFO | Train Epoch: 0 [ 7629312/10637090 (72%)] Loss: 1.1570 (1.247) Data (t): 0.001 Batch (t): 0.953, 566.576/s LR: 0.000004 Logit Scale: 99.946 - V4 -2024-11-27,03:54:57 | INFO | Train Epoch: 0 [ 7680512/10637090 (72%)] Loss: 1.0365 (1.246) Data (t): 0.001 Batch (t): 0.926, 565.160/s LR: 0.000004 Logit Scale: 99.947 - V4 -2024-11-27,03:56:28 | INFO | Train Epoch: 0 [ 7731712/10637090 (73%)] Loss: 1.0135 (1.244) Data (t): 0.001 Batch (t): 0.904, 564.774/s LR: 0.000004 Logit Scale: 99.950 - V4 -2024-11-27,03:57:58 | INFO | Train Epoch: 0 [ 7782912/10637090 (73%)] Loss: 1.2436 (1.244) Data (t): 0.001 Batch (t): 0.905, 565.661/s LR: 0.000004 Logit Scale: 99.953 - V4 -2024-11-27,03:59:29 | INFO | Train Epoch: 0 [ 7834112/10637090 (74%)] Loss: 1.0197 (1.243) Data (t): 0.001 Batch (t): 0.905, 565.650/s LR: 0.000004 Logit Scale: 99.957 - V4 -2024-11-27,04:01:03 | INFO | Train Epoch: 0 [ 7885312/10637090 (74%)] Loss: 1.0551 (1.241) Data (t): 0.001 Batch (t): 0.938, 566.026/s LR: 0.000003 Logit Scale: 99.958 - V4 -2024-11-27,04:02:36 | INFO | Train Epoch: 0 [ 7936512/10637090 (75%)] Loss: 1.1634 (1.241) Data (t): 0.001 Batch (t): 0.932, 565.840/s LR: 0.000003 Logit Scale: 99.960 - V4 -2024-11-27,04:04:07 | INFO | Train Epoch: 0 [ 7987712/10637090 (75%)] Loss: 1.0412 (1.240) Data (t): 0.001 Batch (t): 0.916, 565.631/s LR: 0.000003 Logit Scale: 99.963 - V4 -2024-11-27,04:05:38 | INFO | Train Epoch: 0 [ 8038912/10637090 (76%)] Loss: 1.1871 (1.239) Data (t): 0.001 Batch (t): 0.907, 564.041/s LR: 0.000003 Logit Scale: 99.965 - V4 -2024-11-27,04:07:09 | INFO | Train Epoch: 0 [ 8090112/10637090 (76%)] Loss: 1.2274 (1.239) Data (t): 0.001 Batch (t): 0.908, 562.570/s LR: 0.000003 Logit Scale: 99.967 - V4 -2024-11-27,04:08:40 | INFO | Train Epoch: 0 [ 8141312/10637090 (77%)] Loss: 1.1849 (1.239) Data (t): 0.001 Batch (t): 0.907, 563.275/s LR: 0.000003 Logit Scale: 99.969 - V4 -2024-11-27,04:10:16 | INFO | Train Epoch: 0 [ 8192512/10637090 (77%)] Loss: 1.0565 (1.238) Data (t): 0.001 Batch (t): 0.965, 566.708/s LR: 0.000003 Logit Scale: 99.972 - V4 -2024-11-27,04:11:48 | INFO | Train Epoch: 0 [ 8243712/10637090 (78%)] Loss: 0.96787 (1.236) Data (t): 0.001 Batch (t): 0.916, 566.115/s LR: 0.000003 Logit Scale: 99.975 - V4 -2024-11-27,04:13:18 | INFO | Train Epoch: 0 [ 8294912/10637090 (78%)] Loss: 1.2400 (1.236) Data (t): 0.001 Batch (t): 0.905, 564.940/s LR: 0.000003 Logit Scale: 99.978 - V4 -2024-11-27,04:14:49 | INFO | Train Epoch: 0 [ 8346112/10637090 (78%)] Loss: 1.1404 (1.235) Data (t): 0.001 Batch (t): 0.907, 567.661/s LR: 0.000003 Logit Scale: 99.981 - V4 -2024-11-27,04:16:20 | INFO | Train Epoch: 0 [ 8397312/10637090 (79%)] Loss: 1.0614 (1.234) Data (t): 0.001 Batch (t): 0.907, 561.883/s LR: 0.000003 Logit Scale: 99.984 - V4 -2024-11-27,04:17:55 | INFO | Train Epoch: 0 [ 8448512/10637090 (79%)] Loss: 1.0117 (1.233) Data (t): 0.001 Batch (t): 0.956, 564.351/s LR: 0.000003 Logit Scale: 99.990 - V4 -2024-11-27,04:19:28 | INFO | Train Epoch: 0 [ 8499712/10637090 (80%)] Loss: 1.0210 (1.232) Data (t): 0.001 Batch (t): 0.929, 565.590/s LR: 0.000003 Logit Scale: 99.990 - V4 -2024-11-27,04:20:59 | INFO | Train Epoch: 0 [ 8550912/10637090 (80%)] Loss: 1.0776 (1.231) Data (t): 0.001 Batch (t): 0.907, 561.515/s LR: 0.000003 Logit Scale: 99.993 - V4 -2024-11-27,04:22:30 | INFO | Train Epoch: 0 [ 8602112/10637090 (81%)] Loss: 1.2603 (1.231) Data (t): 0.001 Batch (t): 0.908, 565.205/s LR: 0.000003 Logit Scale: 99.995 - V4 -2024-11-27,04:24:00 | INFO | Train Epoch: 0 [ 8653312/10637090 (81%)] Loss: 1.0624 (1.230) Data (t): 0.001 Batch (t): 0.907, 567.606/s LR: 0.000003 Logit Scale: 99.997 - V4 -2024-11-27,04:25:36 | INFO | Train Epoch: 0 [ 8704512/10637090 (82%)] Loss: 1.0406 (1.229) Data (t): 0.001 Batch (t): 0.954, 563.472/s LR: 0.000003 Logit Scale: 99.999 - V4 -2024-11-27,04:27:09 | INFO | Train Epoch: 0 [ 8755712/10637090 (82%)] Loss: 1.1790 (1.229) Data (t): 0.001 Batch (t): 0.929, 564.248/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,04:28:39 | INFO | Train Epoch: 0 [ 8806912/10637090 (83%)] Loss: 1.0720 (1.228) Data (t): 0.001 Batch (t): 0.907, 565.555/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,04:30:10 | INFO | Train Epoch: 0 [ 8858112/10637090 (83%)] Loss: 1.1143 (1.227) Data (t): 0.001 Batch (t): 0.908, 562.775/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,04:31:41 | INFO | Train Epoch: 0 [ 8909312/10637090 (84%)] Loss: 1.1544 (1.227) Data (t): 0.001 Batch (t): 0.906, 567.008/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,04:33:16 | INFO | Train Epoch: 0 [ 8960512/10637090 (84%)] Loss: 1.2570 (1.227) Data (t): 0.001 Batch (t): 0.954, 566.439/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,04:34:48 | INFO | Train Epoch: 0 [ 9011712/10637090 (85%)] Loss: 1.0736 (1.226) Data (t): 0.001 Batch (t): 0.917, 565.595/s LR: 0.000003 Logit Scale: 99.999 - V4 -2024-11-27,04:36:20 | INFO | Train Epoch: 0 [ 9062912/10637090 (85%)] Loss: 1.0660 (1.225) Data (t): 0.001 Batch (t): 0.918, 566.646/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,04:37:50 | INFO | Train Epoch: 0 [ 9114112/10637090 (86%)] Loss: 1.0883 (1.224) Data (t): 0.001 Batch (t): 0.907, 563.627/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,04:39:21 | INFO | Train Epoch: 0 [ 9165312/10637090 (86%)] Loss: 1.1771 (1.224) Data (t): 0.001 Batch (t): 0.907, 568.406/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,04:40:54 | INFO | Train Epoch: 0 [ 9216512/10637090 (87%)] Loss: 1.0513 (1.223) Data (t): 0.001 Batch (t): 0.930, 564.746/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,04:42:28 | INFO | Train Epoch: 0 [ 9267712/10637090 (87%)] Loss: 1.1358 (1.223) Data (t): 0.001 Batch (t): 0.942, 565.882/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,04:44:00 | INFO | Train Epoch: 0 [ 9318912/10637090 (88%)] Loss: 1.0467 (1.222) Data (t): 0.001 Batch (t): 0.915, 566.400/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,04:45:30 | INFO | Train Epoch: 0 [ 9370112/10637090 (88%)] Loss: 1.0472 (1.221) Data (t): 0.001 Batch (t): 0.905, 567.472/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,04:47:01 | INFO | Train Epoch: 0 [ 9421312/10637090 (89%)] Loss: 1.1237 (1.220) Data (t): 0.001 Batch (t): 0.905, 562.914/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,04:48:31 | INFO | Train Epoch: 0 [ 9472512/10637090 (89%)] Loss: 0.98222 (1.219) Data (t): 0.001 Batch (t): 0.905, 563.595/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,04:50:07 | INFO | Train Epoch: 0 [ 9523712/10637090 (90%)] Loss: 1.0848 (1.218) Data (t): 0.001 Batch (t): 0.955, 560.502/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,04:51:40 | INFO | Train Epoch: 0 [ 9574912/10637090 (90%)] Loss: 1.0220 (1.217) Data (t): 0.001 Batch (t): 0.928, 562.770/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,04:53:10 | INFO | Train Epoch: 0 [ 9626112/10637090 (90%)] Loss: 1.0301 (1.216) Data (t): 0.001 Batch (t): 0.906, 564.688/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,04:54:41 | INFO | Train Epoch: 0 [ 9677312/10637090 (91%)] Loss: 1.0248 (1.215) Data (t): 0.001 Batch (t): 0.906, 565.551/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,04:56:12 | INFO | Train Epoch: 0 [ 9728512/10637090 (91%)] Loss: 1.2502 (1.215) Data (t): 0.001 Batch (t): 0.906, 566.045/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,04:57:47 | INFO | Train Epoch: 0 [ 9779712/10637090 (92%)] Loss: 0.91515 (1.214) Data (t): 0.001 Batch (t): 0.956, 563.774/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,04:59:20 | INFO | Train Epoch: 0 [ 9830912/10637090 (92%)] Loss: 1.1894 (1.214) Data (t): 0.001 Batch (t): 0.928, 561.993/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,05:00:51 | INFO | Train Epoch: 0 [ 9882112/10637090 (93%)] Loss: 1.2648 (1.214) Data (t): 0.001 Batch (t): 0.906, 564.816/s LR: 0.000003 Logit Scale: 99.999 - V4 -2024-11-27,05:02:21 | INFO | Train Epoch: 0 [ 9933312/10637090 (93%)] Loss: 1.0943 (1.213) Data (t): 0.001 Batch (t): 0.907, 565.354/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,05:03:52 | INFO | Train Epoch: 0 [ 9984512/10637090 (94%)] Loss: 1.1343 (1.213) Data (t): 0.001 Batch (t): 0.907, 564.173/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,05:05:28 | INFO | Train Epoch: 0 [10035712/10637090 (94%)] Loss: 1.1223 (1.212) Data (t): 0.001 Batch (t): 0.956, 565.636/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,05:07:00 | INFO | Train Epoch: 0 [10086912/10637090 (95%)] Loss: 1.1591 (1.212) Data (t): 0.001 Batch (t): 0.928, 565.406/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,05:08:31 | INFO | Train Epoch: 0 [10138112/10637090 (95%)] Loss: 1.0973 (1.212) Data (t): 0.001 Batch (t): 0.905, 565.222/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,05:10:02 | INFO | Train Epoch: 0 [10189312/10637090 (96%)] Loss: 1.2007 (1.212) Data (t): 0.001 Batch (t): 0.905, 567.536/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,05:11:32 | INFO | Train Epoch: 0 [10240512/10637090 (96%)] Loss: 1.0202 (1.211) Data (t): 0.001 Batch (t): 0.905, 563.347/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,05:13:07 | INFO | Train Epoch: 0 [10291712/10637090 (97%)] Loss: 1.1201 (1.210) Data (t): 0.001 Batch (t): 0.949, 567.357/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,05:14:39 | INFO | Train Epoch: 0 [10342912/10637090 (97%)] Loss: 0.90820 (1.209) Data (t): 0.001 Batch (t): 0.923, 564.457/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,05:16:11 | INFO | Train Epoch: 0 [10394112/10637090 (98%)] Loss: 1.0708 (1.208) Data (t): 0.001 Batch (t): 0.917, 561.375/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,05:17:42 | INFO | Train Epoch: 0 [10445312/10637090 (98%)] Loss: 1.2011 (1.208) Data (t): 0.001 Batch (t): 0.906, 565.315/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,05:19:12 | INFO | Train Epoch: 0 [10496512/10637090 (99%)] Loss: 0.97144 (1.207) Data (t): 0.001 Batch (t): 0.904, 565.353/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,05:20:43 | INFO | Train Epoch: 0 [10547712/10637090 (99%)] Loss: 1.2910 (1.207) Data (t): 0.001 Batch (t): 0.914, 290.590/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,05:22:17 | INFO | Train Epoch: 0 [10598912/10637090 (100%)] Loss: 1.1027 (1.207) Data (t): 0.001 Batch (t): 0.938, 565.447/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,05:23:26 | INFO | Train Epoch: 0 [10636800/10637090 (100%)] Loss: 1.1504 (1.206) Data (t): 0.001 Batch (t): 0.934, 569.359/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,05:23:34 | INFO | Start epoch 1 -2024-11-27,05:23:38 | INFO | Train Epoch: 1 [ 512/10637090 (0%)] Loss: 0.99605 (0.9960) Data (t): 3.047 Batch (t): 3.970, 128.961/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,05:25:08 | INFO | Train Epoch: 1 [ 51712/10637090 (0%)] Loss: 1.0653 (1.031) Data (t): 0.001 Batch (t): 0.908, 565.990/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,05:26:39 | INFO | Train Epoch: 1 [ 102912/10637090 (1%)] Loss: 1.0920 (1.051) Data (t): 0.001 Batch (t): 0.906, 566.813/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,05:28:10 | INFO | Train Epoch: 1 [ 154112/10637090 (1%)] Loss: 1.1251 (1.070) Data (t): 0.001 Batch (t): 0.906, 563.222/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,05:29:46 | INFO | Train Epoch: 1 [ 205312/10637090 (2%)] Loss: 0.96004 (1.048) Data (t): 0.001 Batch (t): 0.961, 567.453/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,05:31:17 | INFO | Train Epoch: 1 [ 256512/10637090 (2%)] Loss: 1.1357 (1.062) Data (t): 0.001 Batch (t): 0.914, 566.725/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,05:32:47 | INFO | Train Epoch: 1 [ 307712/10637090 (3%)] Loss: 1.0054 (1.054) Data (t): 0.001 Batch (t): 0.904, 567.400/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,05:34:18 | INFO | Train Epoch: 1 [ 358912/10637090 (3%)] Loss: 1.1098 (1.061) Data (t): 0.001 Batch (t): 0.905, 566.172/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,05:35:48 | INFO | Train Epoch: 1 [ 410112/10637090 (4%)] Loss: 1.1216 (1.068) Data (t): 0.001 Batch (t): 0.904, 566.204/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,05:37:23 | INFO | Train Epoch: 1 [ 461312/10637090 (4%)] Loss: 0.89254 (1.050) Data (t): 0.001 Batch (t): 0.942, 566.342/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,05:38:55 | INFO | Train Epoch: 1 [ 512512/10637090 (5%)] Loss: 1.0946 (1.054) Data (t): 0.001 Batch (t): 0.922, 275.669/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,05:40:25 | INFO | Train Epoch: 1 [ 563712/10637090 (5%)] Loss: 1.1492 (1.062) Data (t): 0.001 Batch (t): 0.905, 564.122/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,05:41:56 | INFO | Train Epoch: 1 [ 614912/10637090 (6%)] Loss: 1.0533 (1.062) Data (t): 0.001 Batch (t): 0.906, 567.595/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,05:43:26 | INFO | Train Epoch: 1 [ 666112/10637090 (6%)] Loss: 1.0244 (1.059) Data (t): 0.001 Batch (t): 0.904, 568.043/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,05:45:00 | INFO | Train Epoch: 1 [ 717312/10637090 (7%)] Loss: 1.1731 (1.067) Data (t): 0.001 Batch (t): 0.935, 567.687/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,05:46:32 | INFO | Train Epoch: 1 [ 768512/10637090 (7%)] Loss: 1.0956 (1.068) Data (t): 0.001 Batch (t): 0.926, 562.400/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,05:48:04 | INFO | Train Epoch: 1 [ 819712/10637090 (8%)] Loss: 1.1266 (1.072) Data (t): 0.001 Batch (t): 0.915, 565.419/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,05:49:34 | INFO | Train Epoch: 1 [ 870912/10637090 (8%)] Loss: 0.92508 (1.064) Data (t): 0.001 Batch (t): 0.905, 564.122/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,05:51:05 | INFO | Train Epoch: 1 [ 922112/10637090 (9%)] Loss: 1.0077 (1.061) Data (t): 0.001 Batch (t): 0.907, 563.798/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,05:52:36 | INFO | Train Epoch: 1 [ 973312/10637090 (9%)] Loss: 1.1039 (1.063) Data (t): 0.001 Batch (t): 0.912, 325.849/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,05:54:11 | INFO | Train Epoch: 1 [ 1024512/10637090 (10%)] Loss: 1.0021 (1.060) Data (t): 0.001 Batch (t): 0.951, 565.388/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,05:55:43 | INFO | Train Epoch: 1 [ 1075712/10637090 (10%)] Loss: 1.0619 (1.060) Data (t): 0.001 Batch (t): 0.917, 563.605/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,05:57:14 | INFO | Train Epoch: 1 [ 1126912/10637090 (11%)] Loss: 0.97982 (1.057) Data (t): 0.001 Batch (t): 0.906, 565.205/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,05:58:44 | INFO | Train Epoch: 1 [ 1178112/10637090 (11%)] Loss: 1.0500 (1.056) Data (t): 0.001 Batch (t): 0.906, 564.697/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,06:00:15 | INFO | Train Epoch: 1 [ 1229312/10637090 (12%)] Loss: 1.1384 (1.060) Data (t): 0.001 Batch (t): 0.907, 565.260/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,06:01:51 | INFO | Train Epoch: 1 [ 1280512/10637090 (12%)] Loss: 0.89766 (1.053) Data (t): 0.001 Batch (t): 0.958, 561.988/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,06:03:22 | INFO | Train Epoch: 1 [ 1331712/10637090 (13%)] Loss: 1.0120 (1.052) Data (t): 0.001 Batch (t): 0.915, 565.976/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,06:04:53 | INFO | Train Epoch: 1 [ 1382912/10637090 (13%)] Loss: 1.0200 (1.051) Data (t): 0.001 Batch (t): 0.906, 560.877/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,06:06:24 | INFO | Train Epoch: 1 [ 1434112/10637090 (13%)] Loss: 0.97388 (1.048) Data (t): 0.001 Batch (t): 0.906, 566.830/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,06:07:54 | INFO | Train Epoch: 1 [ 1485312/10637090 (14%)] Loss: 0.99186 (1.046) Data (t): 0.001 Batch (t): 0.905, 567.056/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,06:09:29 | INFO | Train Epoch: 1 [ 1536512/10637090 (14%)] Loss: 0.96438 (1.044) Data (t): 0.001 Batch (t): 0.948, 567.650/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,06:11:01 | INFO | Train Epoch: 1 [ 1587712/10637090 (15%)] Loss: 1.0170 (1.043) Data (t): 0.001 Batch (t): 0.922, 564.996/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,06:12:32 | INFO | Train Epoch: 1 [ 1638912/10637090 (15%)] Loss: 0.91122 (1.039) Data (t): 0.001 Batch (t): 0.906, 563.498/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,06:14:02 | INFO | Train Epoch: 1 [ 1690112/10637090 (16%)] Loss: 0.98500 (1.037) Data (t): 0.001 Batch (t): 0.907, 565.624/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,06:15:33 | INFO | Train Epoch: 1 [ 1741312/10637090 (16%)] Loss: 0.94624 (1.035) Data (t): 0.001 Batch (t): 0.904, 566.717/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,06:17:08 | INFO | Train Epoch: 1 [ 1792512/10637090 (17%)] Loss: 1.0650 (1.035) Data (t): 0.001 Batch (t): 0.950, 562.945/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,06:18:39 | INFO | Train Epoch: 1 [ 1843712/10637090 (17%)] Loss: 0.85102 (1.030) Data (t): 0.001 Batch (t): 0.915, 564.332/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,06:20:11 | INFO | Train Epoch: 1 [ 1894912/10637090 (18%)] Loss: 1.1179 (1.033) Data (t): 0.001 Batch (t): 0.917, 567.175/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,06:21:42 | INFO | Train Epoch: 1 [ 1946112/10637090 (18%)] Loss: 1.0313 (1.033) Data (t): 0.001 Batch (t): 0.907, 562.038/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,06:23:12 | INFO | Train Epoch: 1 [ 1997312/10637090 (19%)] Loss: 1.0216 (1.032) Data (t): 0.001 Batch (t): 0.905, 564.146/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,06:24:46 | INFO | Train Epoch: 1 [ 2048512/10637090 (19%)] Loss: 0.93176 (1.030) Data (t): 0.001 Batch (t): 0.937, 567.844/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,06:26:19 | INFO | Train Epoch: 1 [ 2099712/10637090 (20%)] Loss: 1.0021 (1.029) Data (t): 0.001 Batch (t): 0.927, 563.530/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,06:27:50 | INFO | Train Epoch: 1 [ 2150912/10637090 (20%)] Loss: 1.0069 (1.029) Data (t): 0.001 Batch (t): 0.915, 564.485/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,06:29:21 | INFO | Train Epoch: 1 [ 2202112/10637090 (21%)] Loss: 1.0092 (1.028) Data (t): 0.001 Batch (t): 0.905, 564.221/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,06:30:51 | INFO | Train Epoch: 1 [ 2253312/10637090 (21%)] Loss: 0.99873 (1.028) Data (t): 0.001 Batch (t): 0.905, 564.241/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,06:32:22 | INFO | Train Epoch: 1 [ 2304512/10637090 (22%)] Loss: 1.0552 (1.028) Data (t): 0.001 Batch (t): 0.914, 566.532/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,06:33:58 | INFO | Train Epoch: 1 [ 2355712/10637090 (22%)] Loss: 1.1399 (1.031) Data (t): 0.001 Batch (t): 0.952, 565.086/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,06:35:29 | INFO | Train Epoch: 1 [ 2406912/10637090 (23%)] Loss: 1.0182 (1.030) Data (t): 0.001 Batch (t): 0.917, 564.931/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,06:37:00 | INFO | Train Epoch: 1 [ 2458112/10637090 (23%)] Loss: 1.1023 (1.032) Data (t): 0.001 Batch (t): 0.906, 567.323/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,06:38:30 | INFO | Train Epoch: 1 [ 2509312/10637090 (24%)] Loss: 0.98974 (1.031) Data (t): 0.001 Batch (t): 0.905, 566.239/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,06:40:01 | INFO | Train Epoch: 1 [ 2560512/10637090 (24%)] Loss: 1.0722 (1.032) Data (t): 0.001 Batch (t): 0.907, 564.504/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,06:41:36 | INFO | Train Epoch: 1 [ 2611712/10637090 (25%)] Loss: 1.1386 (1.034) Data (t): 0.001 Batch (t): 0.951, 562.775/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,06:43:08 | INFO | Train Epoch: 1 [ 2662912/10637090 (25%)] Loss: 1.0205 (1.034) Data (t): 0.001 Batch (t): 0.922, 561.643/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,06:44:39 | INFO | Train Epoch: 1 [ 2714112/10637090 (26%)] Loss: 0.96769 (1.032) Data (t): 0.001 Batch (t): 0.904, 566.540/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,06:46:09 | INFO | Train Epoch: 1 [ 2765312/10637090 (26%)] Loss: 1.0600 (1.033) Data (t): 0.001 Batch (t): 0.907, 561.604/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,06:47:40 | INFO | Train Epoch: 1 [ 2816512/10637090 (26%)] Loss: 0.95154 (1.031) Data (t): 0.001 Batch (t): 0.907, 563.200/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,06:49:15 | INFO | Train Epoch: 1 [ 2867712/10637090 (27%)] Loss: 1.0999 (1.033) Data (t): 0.001 Batch (t): 0.951, 565.942/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,06:50:48 | INFO | Train Epoch: 1 [ 2918912/10637090 (27%)] Loss: 1.0344 (1.033) Data (t): 0.001 Batch (t): 0.923, 566.765/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,06:52:18 | INFO | Train Epoch: 1 [ 2970112/10637090 (28%)] Loss: 1.0589 (1.033) Data (t): 0.001 Batch (t): 0.907, 567.177/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,06:53:49 | INFO | Train Epoch: 1 [ 3021312/10637090 (28%)] Loss: 1.0874 (1.034) Data (t): 0.001 Batch (t): 0.906, 564.328/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,06:55:20 | INFO | Train Epoch: 1 [ 3072512/10637090 (29%)] Loss: 1.0388 (1.034) Data (t): 0.001 Batch (t): 0.907, 564.209/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,06:56:54 | INFO | Train Epoch: 1 [ 3123712/10637090 (29%)] Loss: 1.1097 (1.035) Data (t): 0.001 Batch (t): 0.944, 565.862/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,06:58:25 | INFO | Train Epoch: 1 [ 3174912/10637090 (30%)] Loss: 0.95967 (1.034) Data (t): 0.001 Batch (t): 0.912, 561.042/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,06:59:57 | INFO | Train Epoch: 1 [ 3226112/10637090 (30%)] Loss: 1.1461 (1.036) Data (t): 0.001 Batch (t): 0.915, 562.685/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:01:27 | INFO | Train Epoch: 1 [ 3277312/10637090 (31%)] Loss: 1.0143 (1.036) Data (t): 0.001 Batch (t): 0.906, 563.140/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:02:58 | INFO | Train Epoch: 1 [ 3328512/10637090 (31%)] Loss: 1.2315 (1.038) Data (t): 0.001 Batch (t): 0.905, 565.750/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:04:30 | INFO | Train Epoch: 1 [ 3379712/10637090 (32%)] Loss: 0.99611 (1.038) Data (t): 0.001 Batch (t): 0.918, 566.282/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:06:04 | INFO | Train Epoch: 1 [ 3430912/10637090 (32%)] Loss: 1.1552 (1.040) Data (t): 0.001 Batch (t): 0.941, 566.611/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:07:35 | INFO | Train Epoch: 1 [ 3482112/10637090 (33%)] Loss: 0.95877 (1.038) Data (t): 0.001 Batch (t): 0.916, 566.782/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:09:06 | INFO | Train Epoch: 1 [ 3533312/10637090 (33%)] Loss: 1.0217 (1.038) Data (t): 0.001 Batch (t): 0.904, 566.456/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:10:36 | INFO | Train Epoch: 1 [ 3584512/10637090 (34%)] Loss: 0.96306 (1.037) Data (t): 0.001 Batch (t): 0.906, 566.385/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:12:07 | INFO | Train Epoch: 1 [ 3635712/10637090 (34%)] Loss: 1.0495 (1.037) Data (t): 0.001 Batch (t): 0.905, 564.076/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:13:41 | INFO | Train Epoch: 1 [ 3686912/10637090 (35%)] Loss: 0.86775 (1.035) Data (t): 0.001 Batch (t): 0.942, 565.481/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:15:13 | INFO | Train Epoch: 1 [ 3738112/10637090 (35%)] Loss: 1.1263 (1.036) Data (t): 0.001 Batch (t): 0.921, 564.546/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:16:44 | INFO | Train Epoch: 1 [ 3789312/10637090 (36%)] Loss: 1.0803 (1.037) Data (t): 0.001 Batch (t): 0.904, 566.396/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:18:14 | INFO | Train Epoch: 1 [ 3840512/10637090 (36%)] Loss: 1.0569 (1.037) Data (t): 0.001 Batch (t): 0.904, 567.187/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:19:44 | INFO | Train Epoch: 1 [ 3891712/10637090 (37%)] Loss: 0.92729 (1.036) Data (t): 0.001 Batch (t): 0.904, 565.604/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:21:19 | INFO | Train Epoch: 1 [ 3942912/10637090 (37%)] Loss: 0.91242 (1.034) Data (t): 0.001 Batch (t): 0.942, 566.131/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:22:51 | INFO | Train Epoch: 1 [ 3994112/10637090 (38%)] Loss: 0.89468 (1.032) Data (t): 0.001 Batch (t): 0.921, 564.739/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:24:21 | INFO | Train Epoch: 1 [ 4045312/10637090 (38%)] Loss: 0.99255 (1.032) Data (t): 0.001 Batch (t): 0.905, 565.871/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:25:52 | INFO | Train Epoch: 1 [ 4096512/10637090 (39%)] Loss: 1.0470 (1.032) Data (t): 0.001 Batch (t): 0.904, 566.582/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:27:22 | INFO | Train Epoch: 1 [ 4147712/10637090 (39%)] Loss: 0.93214 (1.031) Data (t): 0.001 Batch (t): 0.904, 565.994/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:28:56 | INFO | Train Epoch: 1 [ 4198912/10637090 (39%)] Loss: 1.0591 (1.031) Data (t): 0.001 Batch (t): 0.942, 566.044/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:30:28 | INFO | Train Epoch: 1 [ 4250112/10637090 (40%)] Loss: 1.1059 (1.032) Data (t): 0.001 Batch (t): 0.921, 565.716/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:31:59 | INFO | Train Epoch: 1 [ 4301312/10637090 (40%)] Loss: 0.91878 (1.031) Data (t): 0.001 Batch (t): 0.906, 564.831/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:33:30 | INFO | Train Epoch: 1 [ 4352512/10637090 (41%)] Loss: 0.99103 (1.030) Data (t): 0.001 Batch (t): 0.907, 563.558/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:35:00 | INFO | Train Epoch: 1 [ 4403712/10637090 (41%)] Loss: 1.0128 (1.030) Data (t): 0.001 Batch (t): 0.907, 564.319/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:36:35 | INFO | Train Epoch: 1 [ 4454912/10637090 (42%)] Loss: 1.1169 (1.031) Data (t): 0.001 Batch (t): 0.945, 565.396/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:38:06 | INFO | Train Epoch: 1 [ 4506112/10637090 (42%)] Loss: 0.93320 (1.030) Data (t): 0.001 Batch (t): 0.913, 563.863/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:39:38 | INFO | Train Epoch: 1 [ 4557312/10637090 (43%)] Loss: 0.96299 (1.029) Data (t): 0.001 Batch (t): 0.915, 565.037/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:41:08 | INFO | Train Epoch: 1 [ 4608512/10637090 (43%)] Loss: 1.0005 (1.029) Data (t): 0.001 Batch (t): 0.905, 563.905/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:42:39 | INFO | Train Epoch: 1 [ 4659712/10637090 (44%)] Loss: 1.0549 (1.029) Data (t): 0.001 Batch (t): 0.905, 567.201/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:44:11 | INFO | Train Epoch: 1 [ 4710912/10637090 (44%)] Loss: 1.1337 (1.030) Data (t): 0.001 Batch (t): 0.920, 565.926/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:45:44 | INFO | Train Epoch: 1 [ 4762112/10637090 (45%)] Loss: 0.97907 (1.030) Data (t): 0.001 Batch (t): 0.929, 564.644/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:47:16 | INFO | Train Epoch: 1 [ 4813312/10637090 (45%)] Loss: 0.94102 (1.029) Data (t): 0.001 Batch (t): 0.924, 564.756/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:48:47 | INFO | Train Epoch: 1 [ 4864512/10637090 (46%)] Loss: 1.0158 (1.029) Data (t): 0.001 Batch (t): 0.906, 566.274/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:50:17 | INFO | Train Epoch: 1 [ 4915712/10637090 (46%)] Loss: 0.89690 (1.027) Data (t): 0.001 Batch (t): 0.906, 567.019/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:51:48 | INFO | Train Epoch: 1 [ 4966912/10637090 (47%)] Loss: 1.1335 (1.028) Data (t): 0.001 Batch (t): 0.905, 565.623/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:53:23 | INFO | Train Epoch: 1 [ 5018112/10637090 (47%)] Loss: 0.94765 (1.028) Data (t): 0.001 Batch (t): 0.950, 567.271/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:54:55 | INFO | Train Epoch: 1 [ 5069312/10637090 (48%)] Loss: 0.93306 (1.027) Data (t): 0.001 Batch (t): 0.922, 566.493/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:56:25 | INFO | Train Epoch: 1 [ 5120512/10637090 (48%)] Loss: 1.0547 (1.027) Data (t): 0.001 Batch (t): 0.906, 563.600/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:57:56 | INFO | Train Epoch: 1 [ 5171712/10637090 (49%)] Loss: 1.0597 (1.027) Data (t): 0.001 Batch (t): 0.906, 567.034/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:59:27 | INFO | Train Epoch: 1 [ 5222912/10637090 (49%)] Loss: 0.97989 (1.027) Data (t): 0.001 Batch (t): 0.906, 567.593/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,08:01:02 | INFO | Train Epoch: 1 [ 5274112/10637090 (50%)] Loss: 1.0548 (1.027) Data (t): 0.001 Batch (t): 0.951, 566.319/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,08:02:34 | INFO | Train Epoch: 1 [ 5325312/10637090 (50%)] Loss: 0.94453 (1.026) Data (t): 0.001 Batch (t): 0.923, 564.214/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,08:04:04 | INFO | Train Epoch: 1 [ 5376512/10637090 (51%)] Loss: 1.0222 (1.026) Data (t): 0.001 Batch (t): 0.906, 565.438/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,08:05:35 | INFO | Train Epoch: 1 [ 5427712/10637090 (51%)] Loss: 0.99847 (1.026) Data (t): 0.001 Batch (t): 0.907, 564.791/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,08:07:06 | INFO | Train Epoch: 1 [ 5478912/10637090 (52%)] Loss: 1.1068 (1.027) Data (t): 0.001 Batch (t): 0.906, 566.754/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,08:08:41 | INFO | Train Epoch: 1 [ 5530112/10637090 (52%)] Loss: 0.95260 (1.026) Data (t): 0.001 Batch (t): 0.951, 566.922/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,08:10:12 | INFO | Train Epoch: 1 [ 5581312/10637090 (52%)] Loss: 1.0146 (1.026) Data (t): 0.001 Batch (t): 0.916, 267.261/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,08:11:44 | INFO | Train Epoch: 1 [ 5632512/10637090 (53%)] Loss: 0.88795 (1.025) Data (t): 0.001 Batch (t): 0.913, 566.896/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,08:13:14 | INFO | Train Epoch: 1 [ 5683712/10637090 (53%)] Loss: 1.0358 (1.025) Data (t): 0.001 Batch (t): 0.905, 567.783/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,08:14:45 | INFO | Train Epoch: 1 [ 5734912/10637090 (54%)] Loss: 1.0684 (1.025) Data (t): 0.001 Batch (t): 0.905, 566.366/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,08:16:18 | INFO | Train Epoch: 1 [ 5786112/10637090 (54%)] Loss: 0.94225 (1.024) Data (t): 0.001 Batch (t): 0.936, 568.469/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,08:17:50 | INFO | Train Epoch: 1 [ 5837312/10637090 (55%)] Loss: 1.0869 (1.025) Data (t): 0.001 Batch (t): 0.919, 566.358/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,08:19:22 | INFO | Train Epoch: 1 [ 5888512/10637090 (55%)] Loss: 0.93740 (1.024) Data (t): 0.001 Batch (t): 0.923, 564.485/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,08:20:53 | INFO | Train Epoch: 1 [ 5939712/10637090 (56%)] Loss: 1.0763 (1.025) Data (t): 0.001 Batch (t): 0.906, 563.449/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,08:22:24 | INFO | Train Epoch: 1 [ 5990912/10637090 (56%)] Loss: 0.90599 (1.024) Data (t): 0.001 Batch (t): 0.906, 562.998/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,08:23:56 | INFO | Train Epoch: 1 [ 6042112/10637090 (57%)] Loss: 0.99699 (1.023) Data (t): 0.001 Batch (t): 0.922, 565.232/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,08:25:30 | INFO | Train Epoch: 1 [ 6093312/10637090 (57%)] Loss: 1.1403 (1.024) Data (t): 0.001 Batch (t): 0.936, 563.738/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,08:27:02 | INFO | Train Epoch: 1 [ 6144512/10637090 (58%)] Loss: 0.92787 (1.024) Data (t): 0.001 Batch (t): 0.923, 565.007/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,08:28:33 | INFO | Train Epoch: 1 [ 6195712/10637090 (58%)] Loss: 1.1130 (1.024) Data (t): 0.001 Batch (t): 0.906, 564.766/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,08:30:03 | INFO | Train Epoch: 1 [ 6246912/10637090 (59%)] Loss: 0.94117 (1.024) Data (t): 0.001 Batch (t): 0.907, 562.227/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,08:31:34 | INFO | Train Epoch: 1 [ 6298112/10637090 (59%)] Loss: 0.92106 (1.023) Data (t): 0.001 Batch (t): 0.907, 567.182/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,08:33:09 | INFO | Train Epoch: 1 [ 6349312/10637090 (60%)] Loss: 0.89584 (1.022) Data (t): 0.001 Batch (t): 0.952, 566.305/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,08:34:41 | INFO | Train Epoch: 1 [ 6400512/10637090 (60%)] Loss: 0.98642 (1.022) Data (t): 0.001 Batch (t): 0.922, 564.370/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,08:36:12 | INFO | Train Epoch: 1 [ 6451712/10637090 (61%)] Loss: 1.0965 (1.022) Data (t): 0.001 Batch (t): 0.905, 565.747/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,08:37:42 | INFO | Train Epoch: 1 [ 6502912/10637090 (61%)] Loss: 1.1340 (1.023) Data (t): 0.001 Batch (t): 0.906, 564.926/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,08:39:13 | INFO | Train Epoch: 1 [ 6554112/10637090 (62%)] Loss: 1.0770 (1.023) Data (t): 0.001 Batch (t): 0.907, 563.914/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,08:40:49 | INFO | Train Epoch: 1 [ 6605312/10637090 (62%)] Loss: 1.0448 (1.024) Data (t): 0.001 Batch (t): 0.955, 561.226/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,08:42:21 | INFO | Train Epoch: 1 [ 6656512/10637090 (63%)] Loss: 0.86651 (1.022) Data (t): 0.001 Batch (t): 0.926, 565.842/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,08:43:52 | INFO | Train Epoch: 1 [ 6707712/10637090 (63%)] Loss: 0.97040 (1.022) Data (t): 0.001 Batch (t): 0.907, 563.521/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,08:45:22 | INFO | Train Epoch: 1 [ 6758912/10637090 (64%)] Loss: 1.1047 (1.023) Data (t): 0.001 Batch (t): 0.906, 566.229/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,08:46:53 | INFO | Train Epoch: 1 [ 6810112/10637090 (64%)] Loss: 0.97643 (1.022) Data (t): 0.001 Batch (t): 0.906, 563.671/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,08:48:28 | INFO | Train Epoch: 1 [ 6861312/10637090 (65%)] Loss: 0.86220 (1.021) Data (t): 0.001 Batch (t): 0.952, 567.008/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,08:49:59 | INFO | Train Epoch: 1 [ 6912512/10637090 (65%)] Loss: 1.0355 (1.021) Data (t): 0.001 Batch (t): 0.906, 565.695/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,08:51:31 | INFO | Train Epoch: 1 [ 6963712/10637090 (65%)] Loss: 1.1112 (1.022) Data (t): 0.001 Batch (t): 0.923, 562.959/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,08:53:02 | INFO | Train Epoch: 1 [ 7014912/10637090 (66%)] Loss: 1.0347 (1.022) Data (t): 0.001 Batch (t): 0.906, 565.479/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,08:54:33 | INFO | Train Epoch: 1 [ 7066112/10637090 (66%)] Loss: 1.0209 (1.022) Data (t): 0.001 Batch (t): 0.908, 563.315/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,08:56:06 | INFO | Train Epoch: 1 [ 7117312/10637090 (67%)] Loss: 0.98528 (1.022) Data (t): 0.001 Batch (t): 0.939, 311.203/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,08:57:39 | INFO | Train Epoch: 1 [ 7168512/10637090 (67%)] Loss: 0.90495 (1.021) Data (t): 0.001 Batch (t): 0.925, 562.615/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,08:59:11 | INFO | Train Epoch: 1 [ 7219712/10637090 (68%)] Loss: 1.1059 (1.021) Data (t): 0.001 Batch (t): 0.924, 564.994/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:00:42 | INFO | Train Epoch: 1 [ 7270912/10637090 (68%)] Loss: 1.0582 (1.022) Data (t): 0.001 Batch (t): 0.908, 565.052/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:02:13 | INFO | Train Epoch: 1 [ 7322112/10637090 (69%)] Loss: 1.0287 (1.022) Data (t): 0.001 Batch (t): 0.906, 562.875/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:03:45 | INFO | Train Epoch: 1 [ 7373312/10637090 (69%)] Loss: 1.0140 (1.022) Data (t): 0.001 Batch (t): 0.923, 561.382/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:05:19 | INFO | Train Epoch: 1 [ 7424512/10637090 (70%)] Loss: 0.97930 (1.021) Data (t): 0.001 Batch (t): 0.939, 563.123/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:06:52 | INFO | Train Epoch: 1 [ 7475712/10637090 (70%)] Loss: 1.0888 (1.022) Data (t): 0.001 Batch (t): 0.926, 564.008/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:08:22 | INFO | Train Epoch: 1 [ 7526912/10637090 (71%)] Loss: 0.97723 (1.022) Data (t): 0.001 Batch (t): 0.907, 565.380/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:09:53 | INFO | Train Epoch: 1 [ 7578112/10637090 (71%)] Loss: 1.0862 (1.022) Data (t): 0.001 Batch (t): 0.906, 565.697/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:11:24 | INFO | Train Epoch: 1 [ 7629312/10637090 (72%)] Loss: 0.95046 (1.022) Data (t): 0.001 Batch (t): 0.908, 566.509/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:12:59 | INFO | Train Epoch: 1 [ 7680512/10637090 (72%)] Loss: 1.0851 (1.022) Data (t): 0.001 Batch (t): 0.953, 565.420/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:14:31 | INFO | Train Epoch: 1 [ 7731712/10637090 (73%)] Loss: 1.0705 (1.022) Data (t): 0.001 Batch (t): 0.923, 564.119/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:16:02 | INFO | Train Epoch: 1 [ 7782912/10637090 (73%)] Loss: 0.91027 (1.022) Data (t): 0.001 Batch (t): 0.906, 563.653/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:17:33 | INFO | Train Epoch: 1 [ 7834112/10637090 (74%)] Loss: 1.0479 (1.022) Data (t): 0.001 Batch (t): 0.908, 564.853/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:19:03 | INFO | Train Epoch: 1 [ 7885312/10637090 (74%)] Loss: 1.0007 (1.022) Data (t): 0.001 Batch (t): 0.908, 562.832/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:20:38 | INFO | Train Epoch: 1 [ 7936512/10637090 (75%)] Loss: 0.97559 (1.021) Data (t): 0.001 Batch (t): 0.948, 564.404/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:22:10 | INFO | Train Epoch: 1 [ 7987712/10637090 (75%)] Loss: 0.92651 (1.021) Data (t): 0.001 Batch (t): 0.916, 562.082/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:23:41 | INFO | Train Epoch: 1 [ 8038912/10637090 (76%)] Loss: 0.85818 (1.020) Data (t): 0.001 Batch (t): 0.914, 566.555/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:25:12 | INFO | Train Epoch: 1 [ 8090112/10637090 (76%)] Loss: 0.86241 (1.019) Data (t): 0.001 Batch (t): 0.906, 567.106/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:26:42 | INFO | Train Epoch: 1 [ 8141312/10637090 (77%)] Loss: 1.1077 (1.019) Data (t): 0.001 Batch (t): 0.907, 563.186/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:28:17 | INFO | Train Epoch: 1 [ 8192512/10637090 (77%)] Loss: 0.97823 (1.019) Data (t): 0.001 Batch (t): 0.947, 566.863/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:29:48 | INFO | Train Epoch: 1 [ 8243712/10637090 (78%)] Loss: 1.0374 (1.019) Data (t): 0.001 Batch (t): 0.914, 565.784/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:31:21 | INFO | Train Epoch: 1 [ 8294912/10637090 (78%)] Loss: 1.0225 (1.019) Data (t): 0.001 Batch (t): 0.925, 563.876/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:32:51 | INFO | Train Epoch: 1 [ 8346112/10637090 (78%)] Loss: 0.97318 (1.019) Data (t): 0.001 Batch (t): 0.906, 566.196/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:34:22 | INFO | Train Epoch: 1 [ 8397312/10637090 (79%)] Loss: 1.0428 (1.019) Data (t): 0.001 Batch (t): 0.905, 566.293/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:35:55 | INFO | Train Epoch: 1 [ 8448512/10637090 (79%)] Loss: 0.96848 (1.019) Data (t): 0.001 Batch (t): 0.929, 564.782/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:37:28 | INFO | Train Epoch: 1 [ 8499712/10637090 (80%)] Loss: 1.1015 (1.019) Data (t): 0.001 Batch (t): 0.931, 567.245/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:39:00 | INFO | Train Epoch: 1 [ 8550912/10637090 (80%)] Loss: 1.0210 (1.019) Data (t): 0.001 Batch (t): 0.924, 565.455/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:40:31 | INFO | Train Epoch: 1 [ 8602112/10637090 (81%)] Loss: 0.76609 (1.018) Data (t): 0.001 Batch (t): 0.906, 566.002/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:42:01 | INFO | Train Epoch: 1 [ 8653312/10637090 (81%)] Loss: 0.91934 (1.017) Data (t): 0.001 Batch (t): 0.905, 566.385/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:43:33 | INFO | Train Epoch: 1 [ 8704512/10637090 (82%)] Loss: 1.0500 (1.017) Data (t): 0.001 Batch (t): 0.913, 566.963/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:45:07 | INFO | Train Epoch: 1 [ 8755712/10637090 (82%)] Loss: 1.0902 (1.018) Data (t): 0.001 Batch (t): 0.945, 562.549/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:46:39 | INFO | Train Epoch: 1 [ 8806912/10637090 (83%)] Loss: 1.1124 (1.018) Data (t): 0.001 Batch (t): 0.918, 564.213/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:48:10 | INFO | Train Epoch: 1 [ 8858112/10637090 (83%)] Loss: 0.87710 (1.017) Data (t): 0.001 Batch (t): 0.914, 567.206/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:49:41 | INFO | Train Epoch: 1 [ 8909312/10637090 (84%)] Loss: 1.0338 (1.018) Data (t): 0.001 Batch (t): 0.906, 565.747/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:51:12 | INFO | Train Epoch: 1 [ 8960512/10637090 (84%)] Loss: 1.0121 (1.017) Data (t): 0.001 Batch (t): 0.907, 564.501/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:52:47 | INFO | Train Epoch: 1 [ 9011712/10637090 (85%)] Loss: 1.0576 (1.018) Data (t): 0.001 Batch (t): 0.955, 565.691/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:54:19 | INFO | Train Epoch: 1 [ 9062912/10637090 (85%)] Loss: 1.0241 (1.018) Data (t): 0.001 Batch (t): 0.916, 567.483/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:55:50 | INFO | Train Epoch: 1 [ 9114112/10637090 (86%)] Loss: 1.0915 (1.018) Data (t): 0.001 Batch (t): 0.913, 565.679/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:57:21 | INFO | Train Epoch: 1 [ 9165312/10637090 (86%)] Loss: 1.0455 (1.018) Data (t): 0.001 Batch (t): 0.905, 564.263/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:58:51 | INFO | Train Epoch: 1 [ 9216512/10637090 (87%)] Loss: 0.98658 (1.018) Data (t): 0.001 Batch (t): 0.907, 565.168/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,10:00:27 | INFO | Train Epoch: 1 [ 9267712/10637090 (87%)] Loss: 0.83012 (1.017) Data (t): 0.001 Batch (t): 0.954, 565.891/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,10:01:58 | INFO | Train Epoch: 1 [ 9318912/10637090 (88%)] Loss: 0.91993 (1.017) Data (t): 0.001 Batch (t): 0.916, 566.650/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,10:03:30 | INFO | Train Epoch: 1 [ 9370112/10637090 (88%)] Loss: 0.95294 (1.016) Data (t): 0.001 Batch (t): 0.914, 566.302/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,10:05:00 | INFO | Train Epoch: 1 [ 9421312/10637090 (89%)] Loss: 0.99583 (1.016) Data (t): 0.001 Batch (t): 0.906, 565.011/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,10:06:31 | INFO | Train Epoch: 1 [ 9472512/10637090 (89%)] Loss: 0.91248 (1.016) Data (t): 0.001 Batch (t): 0.905, 565.156/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,10:08:06 | INFO | Train Epoch: 1 [ 9523712/10637090 (90%)] Loss: 0.94513 (1.015) Data (t): 0.001 Batch (t): 0.947, 302.579/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,10:09:37 | INFO | Train Epoch: 1 [ 9574912/10637090 (90%)] Loss: 1.0810 (1.016) Data (t): 0.001 Batch (t): 0.913, 564.455/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,10:11:09 | INFO | Train Epoch: 1 [ 9626112/10637090 (90%)] Loss: 0.95236 (1.015) Data (t): 0.001 Batch (t): 0.926, 561.952/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,10:12:40 | INFO | Train Epoch: 1 [ 9677312/10637090 (91%)] Loss: 0.94826 (1.015) Data (t): 0.001 Batch (t): 0.907, 563.711/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,10:14:11 | INFO | Train Epoch: 1 [ 9728512/10637090 (91%)] Loss: 1.0354 (1.015) Data (t): 0.001 Batch (t): 0.907, 566.087/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,10:15:44 | INFO | Train Epoch: 1 [ 9779712/10637090 (92%)] Loss: 0.90536 (1.014) Data (t): 0.001 Batch (t): 0.930, 562.897/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,10:17:17 | INFO | Train Epoch: 1 [ 9830912/10637090 (92%)] Loss: 1.0303 (1.014) Data (t): 0.001 Batch (t): 0.932, 563.609/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,10:18:49 | INFO | Train Epoch: 1 [ 9882112/10637090 (93%)] Loss: 1.0716 (1.015) Data (t): 0.001 Batch (t): 0.917, 563.220/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,10:20:20 | INFO | Train Epoch: 1 [ 9933312/10637090 (93%)] Loss: 1.0059 (1.015) Data (t): 0.001 Batch (t): 0.914, 564.446/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,10:21:51 | INFO | Train Epoch: 1 [ 9984512/10637090 (94%)] Loss: 1.0010 (1.015) Data (t): 0.001 Batch (t): 0.906, 567.366/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,10:23:22 | INFO | Train Epoch: 1 [10035712/10637090 (94%)] Loss: 0.93830 (1.014) Data (t): 0.001 Batch (t): 0.915, 566.496/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,10:24:56 | INFO | Train Epoch: 1 [10086912/10637090 (95%)] Loss: 0.98885 (1.014) Data (t): 0.001 Batch (t): 0.940, 563.388/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,10:26:28 | INFO | Train Epoch: 1 [10138112/10637090 (95%)] Loss: 0.97667 (1.014) Data (t): 0.001 Batch (t): 0.916, 567.157/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,10:27:59 | INFO | Train Epoch: 1 [10189312/10637090 (96%)] Loss: 1.0805 (1.014) Data (t): 0.001 Batch (t): 0.914, 563.164/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,10:29:30 | INFO | Train Epoch: 1 [10240512/10637090 (96%)] Loss: 0.84258 (1.013) Data (t): 0.001 Batch (t): 0.907, 566.468/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,10:31:01 | INFO | Train Epoch: 1 [10291712/10637090 (97%)] Loss: 0.95244 (1.013) Data (t): 0.001 Batch (t): 0.907, 563.463/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,10:32:36 | INFO | Train Epoch: 1 [10342912/10637090 (97%)] Loss: 1.0020 (1.013) Data (t): 0.001 Batch (t): 0.955, 564.334/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,10:34:08 | INFO | Train Epoch: 1 [10394112/10637090 (98%)] Loss: 1.0205 (1.013) Data (t): 0.001 Batch (t): 0.916, 565.652/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,10:35:39 | INFO | Train Epoch: 1 [10445312/10637090 (98%)] Loss: 1.0271 (1.013) Data (t): 0.001 Batch (t): 0.914, 564.856/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,10:37:10 | INFO | Train Epoch: 1 [10496512/10637090 (99%)] Loss: 0.99658 (1.013) Data (t): 0.001 Batch (t): 0.907, 562.422/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,10:38:41 | INFO | Train Epoch: 1 [10547712/10637090 (99%)] Loss: 1.0557 (1.013) Data (t): 0.001 Batch (t): 0.908, 565.340/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,10:40:16 | INFO | Train Epoch: 1 [10598912/10637090 (100%)] Loss: 1.1365 (1.014) Data (t): 0.001 Batch (t): 0.956, 566.695/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,10:41:23 | INFO | Train Epoch: 1 [10636800/10637090 (100%)] Loss: 0.96837 (1.014) Data (t): 0.002 Batch (t): 0.907, 566.356/s LR: 0.000000 Logit Scale: 100.000 - V4 diff --git a/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten/2024_11_27-00_03_54-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/params.txt b/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten/2024_11_27-00_03_54-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/params.txt deleted file mode 100644 index 2fd04ed84f3df01c9c4429ad99220ce01ea1aee4..0000000000000000000000000000000000000000 --- a/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten/2024_11_27-00_03_54-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/params.txt +++ /dev/null @@ -1,67 +0,0 @@ -batch_size: 64 -beta1: 0.9 -beta2: 0.98 -checkpoint_path: data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten/2024_11_27-00_03_54-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/checkpoints -copy_codebase: False -csv_caption_key: caption -csv_hard_captions_key: neg_caption -csv_img_key: img_path -csv_separator: , -dataset_resampled: False -dataset_type: csv -ddp_static_graph: False -debug: False -device: cuda:0 -dist_backend: nccl -dist_url: env:// -distributed: True -epochs: 2 -eps: 1e-06 -force_quick_gelu: True -gather_with_grad: False -grad_checkpointing: False -horovod: False -imagenet_v2: None -imagenet_val: None -local_loss: False -local_rank: 0 -lock_image: False -lock_image_freeze_bn_stats: False -lock_image_unlocked_groups: 0 -log_level: 20 -log_local: False -log_path: data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten/2024_11_27-00_03_54-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/out.log -logs: data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten -lr: 5e-06 -model: ViT-L-14-336 -name: 2024_11_27-00_03_54-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp -no_set_device_rank: False -norm_gradient_clip: None -precision: amp -pretrained: data/openclip-vit-14-336/openclip_model.pt -pretrained_image: False -rank: 0 -report_to: wandb -resume: None -save_frequency: 1 -save_most_recent: False -seed: 0 -skip_scheduler: False -tensorboard: False -tensorboard_path: -torchscript: False -trace: False -train_data: csv_data/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten.csv -train_num_samples: None -use_bn_sync: False -val_data: None -val_frequency: 1 -val_num_samples: None -wandb: True -wandb_notes: -wandb_project: neg-clip-plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten -warmup: 0 -wd: 0.1 -workers: 4 -world_size: 8 -zeroshot_frequency: 2 diff --git a/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten_decimal2/2024_11_26-13_26_45-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/checkpoints/epoch_1.pt b/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten_decimal2/2024_11_26-13_26_45-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/checkpoints/epoch_1.pt deleted file mode 100644 index 6708357fc8d6c6f39baa0f3c14012c6198a055d7..0000000000000000000000000000000000000000 --- a/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten_decimal2/2024_11_26-13_26_45-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/checkpoints/epoch_1.pt +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:1ef4d80ec486f5f01136fdf042e33e31d3e7a31ed27bc82dccb78a27ef52ec40 -size 5135890710 diff --git a/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten_decimal2/2024_11_26-13_26_45-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/checkpoints/epoch_2.pt b/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten_decimal2/2024_11_26-13_26_45-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/checkpoints/epoch_2.pt deleted file mode 100644 index 4548c9b46d81fb1996c43821aa46c63893870cfe..0000000000000000000000000000000000000000 --- a/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten_decimal2/2024_11_26-13_26_45-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/checkpoints/epoch_2.pt +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:b8e076c064dbf722cda1c971b98755117cf36c95fd5a99626ca7a67cda409773 -size 5135890710 diff --git a/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten_decimal2/2024_11_26-13_26_45-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/out.log b/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten_decimal2/2024_11_26-13_26_45-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/out.log deleted file mode 100644 index 812d317732b436a0a64e521e1c4754da0a114d35..0000000000000000000000000000000000000000 --- a/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten_decimal2/2024_11_26-13_26_45-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/out.log +++ /dev/null @@ -1,534 +0,0 @@ -2024-11-26,13:26:45 | INFO | Running in distributed mode with multiple processes. Device: cuda:0.Process (global: 0, local 0), total 8. -2024-11-26,13:26:45 | INFO | Loading ViT-L-14-336 model config. -2024-11-26,13:26:48 | INFO | Loading pretrained ViT-L-14-336 weights (data/openclip-vit-14-336/openclip_model.pt). -2024-11-26,13:26:55 | INFO | Model: -2024-11-26,13:26:55 | INFO | CLIP( - (visual): VisualTransformer( - (conv1): Conv2d(3, 1024, kernel_size=(14, 14), stride=(14, 14), bias=False) - (ln_pre): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) - (transformer): Transformer( - (resblocks): ModuleList( - (0-23): 24 x ResidualAttentionBlock( - (attn): MultiheadAttention( - (out_proj): NonDynamicallyQuantizableLinear(in_features=1024, out_features=1024, bias=True) - ) - (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) - (mlp): Sequential( - (c_fc): Linear(in_features=1024, out_features=4096, bias=True) - (gelu): QuickGELU() - (c_proj): Linear(in_features=4096, out_features=1024, bias=True) - ) - (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) - ) - ) - ) - (ln_post): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) - ) - (transformer): Transformer( - (resblocks): ModuleList( - (0-11): 12 x ResidualAttentionBlock( - (attn): MultiheadAttention( - (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) - ) - (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) - (mlp): Sequential( - (c_fc): Linear(in_features=768, out_features=3072, bias=True) - (gelu): QuickGELU() - (c_proj): Linear(in_features=3072, out_features=768, bias=True) - ) - (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) - ) - ) - ) - (token_embedding): Embedding(49408, 768) - (ln_final): LayerNorm((768,), eps=1e-05, elementwise_affine=True) -) -2024-11-26,13:26:55 | INFO | Params: -2024-11-26,13:26:55 | INFO | batch_size: 64 -2024-11-26,13:26:55 | INFO | beta1: 0.9 -2024-11-26,13:26:55 | INFO | beta2: 0.98 -2024-11-26,13:26:55 | INFO | checkpoint_path: data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten_decimal2/2024_11_26-13_26_45-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/checkpoints -2024-11-26,13:26:55 | INFO | copy_codebase: False -2024-11-26,13:26:55 | INFO | csv_caption_key: caption -2024-11-26,13:26:55 | INFO | csv_hard_captions_key: neg_caption -2024-11-26,13:26:55 | INFO | csv_img_key: img_path -2024-11-26,13:26:55 | INFO | csv_separator: , -2024-11-26,13:26:55 | INFO | dataset_resampled: False -2024-11-26,13:26:55 | INFO | dataset_type: csv -2024-11-26,13:26:55 | INFO | ddp_static_graph: False -2024-11-26,13:26:55 | INFO | debug: False -2024-11-26,13:26:55 | INFO | device: cuda:0 -2024-11-26,13:26:55 | INFO | dist_backend: nccl -2024-11-26,13:26:55 | INFO | dist_url: env:// -2024-11-26,13:26:55 | INFO | distributed: True -2024-11-26,13:26:55 | INFO | epochs: 2 -2024-11-26,13:26:55 | INFO | eps: 1e-06 -2024-11-26,13:26:55 | INFO | force_quick_gelu: True -2024-11-26,13:26:55 | INFO | gather_with_grad: False -2024-11-26,13:26:55 | INFO | grad_checkpointing: False -2024-11-26,13:26:55 | INFO | horovod: False -2024-11-26,13:26:55 | INFO | imagenet_v2: None -2024-11-26,13:26:55 | INFO | imagenet_val: None -2024-11-26,13:26:55 | INFO | local_loss: False -2024-11-26,13:26:55 | INFO | local_rank: 0 -2024-11-26,13:26:55 | INFO | lock_image: False -2024-11-26,13:26:55 | INFO | lock_image_freeze_bn_stats: False -2024-11-26,13:26:55 | INFO | lock_image_unlocked_groups: 0 -2024-11-26,13:26:55 | INFO | log_level: 20 -2024-11-26,13:26:55 | INFO | log_local: False -2024-11-26,13:26:55 | INFO | log_path: data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten_decimal2/2024_11_26-13_26_45-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/out.log -2024-11-26,13:26:55 | INFO | logs: data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten_decimal2 -2024-11-26,13:26:55 | INFO | lr: 1e-06 -2024-11-26,13:26:55 | INFO | model: ViT-L-14-336 -2024-11-26,13:26:55 | INFO | name: 2024_11_26-13_26_45-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp -2024-11-26,13:26:55 | INFO | no_set_device_rank: False -2024-11-26,13:26:55 | INFO | norm_gradient_clip: None -2024-11-26,13:26:55 | INFO | precision: amp -2024-11-26,13:26:55 | INFO | pretrained: data/openclip-vit-14-336/openclip_model.pt -2024-11-26,13:26:55 | INFO | pretrained_image: False -2024-11-26,13:26:55 | INFO | rank: 0 -2024-11-26,13:26:55 | INFO | report_to: wandb -2024-11-26,13:26:55 | INFO | resume: None -2024-11-26,13:26:55 | INFO | save_frequency: 1 -2024-11-26,13:26:55 | INFO | save_most_recent: False -2024-11-26,13:26:55 | INFO | seed: 0 -2024-11-26,13:26:55 | INFO | skip_scheduler: False -2024-11-26,13:26:55 | INFO | tensorboard: False -2024-11-26,13:26:55 | INFO | tensorboard_path: -2024-11-26,13:26:55 | INFO | torchscript: False -2024-11-26,13:26:55 | INFO | trace: False -2024-11-26,13:26:55 | INFO | train_data: csv_data/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten_decimal2.csv -2024-11-26,13:26:55 | INFO | train_num_samples: None -2024-11-26,13:26:55 | INFO | use_bn_sync: False -2024-11-26,13:26:55 | INFO | val_data: None -2024-11-26,13:26:55 | INFO | val_frequency: 1 -2024-11-26,13:26:55 | INFO | val_num_samples: None -2024-11-26,13:26:55 | INFO | wandb: True -2024-11-26,13:26:55 | INFO | wandb_notes: -2024-11-26,13:26:55 | INFO | wandb_project: neg-clip-plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten_decimal2 -2024-11-26,13:26:55 | INFO | warmup: 0 -2024-11-26,13:26:55 | INFO | wd: 0.1 -2024-11-26,13:26:55 | INFO | workers: 4 -2024-11-26,13:26:55 | INFO | world_size: 8 -2024-11-26,13:26:55 | INFO | zeroshot_frequency: 2 -2024-11-26,13:27:49 | INFO | Init a wandb project! -2024-11-26,13:27:59 | INFO | Start epoch 0 -2024-11-26,13:28:05 | INFO | Train Epoch: 0 [ 512/10637090 (0%)] Loss: 6.0211 (6.021) Data (t): 2.675 Batch (t): 6.225, 82.2449/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,13:29:36 | INFO | Train Epoch: 0 [ 51712/10637090 (0%)] Loss: 2.6505 (4.336) Data (t): 0.001 Batch (t): 0.908, 568.068/s LR: 0.000001 Logit Scale: 99.998 - V4 -2024-11-26,13:31:06 | INFO | Train Epoch: 0 [ 102912/10637090 (1%)] Loss: 2.3597 (3.677) Data (t): 0.001 Batch (t): 0.901, 567.151/s LR: 0.000001 Logit Scale: 99.998 - V4 -2024-11-26,13:32:38 | INFO | Train Epoch: 0 [ 154112/10637090 (1%)] Loss: 2.0625 (3.273) Data (t): 0.001 Batch (t): 0.916, 571.802/s LR: 0.000001 Logit Scale: 99.998 - V4 -2024-11-26,13:34:11 | INFO | Train Epoch: 0 [ 205312/10637090 (2%)] Loss: 1.8719 (2.993) Data (t): 0.001 Batch (t): 0.933, 567.169/s LR: 0.000001 Logit Scale: 99.999 - V4 -2024-11-26,13:35:41 | INFO | Train Epoch: 0 [ 256512/10637090 (2%)] Loss: 1.7555 (2.787) Data (t): 0.001 Batch (t): 0.902, 570.080/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,13:37:11 | INFO | Train Epoch: 0 [ 307712/10637090 (3%)] Loss: 1.7410 (2.637) Data (t): 0.001 Batch (t): 0.901, 567.160/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,13:38:42 | INFO | Train Epoch: 0 [ 358912/10637090 (3%)] Loss: 1.7635 (2.528) Data (t): 0.001 Batch (t): 0.901, 569.098/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,13:40:13 | INFO | Train Epoch: 0 [ 410112/10637090 (4%)] Loss: 1.7196 (2.438) Data (t): 0.001 Batch (t): 0.909, 567.021/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,13:41:48 | INFO | Train Epoch: 0 [ 461312/10637090 (4%)] Loss: 1.9196 (2.386) Data (t): 0.001 Batch (t): 0.950, 570.077/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,13:43:18 | INFO | Train Epoch: 0 [ 512512/10637090 (5%)] Loss: 1.6487 (2.319) Data (t): 0.001 Batch (t): 0.901, 567.572/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,13:44:48 | INFO | Train Epoch: 0 [ 563712/10637090 (5%)] Loss: 1.6103 (2.260) Data (t): 0.001 Batch (t): 0.902, 569.064/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,13:46:18 | INFO | Train Epoch: 0 [ 614912/10637090 (6%)] Loss: 1.5905 (2.209) Data (t): 0.001 Batch (t): 0.901, 569.221/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,13:47:48 | INFO | Train Epoch: 0 [ 666112/10637090 (6%)] Loss: 1.5883 (2.164) Data (t): 0.001 Batch (t): 0.901, 570.760/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,13:49:24 | INFO | Train Epoch: 0 [ 717312/10637090 (7%)] Loss: 1.4338 (2.116) Data (t): 0.001 Batch (t): 0.957, 570.627/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,13:50:54 | INFO | Train Epoch: 0 [ 768512/10637090 (7%)] Loss: 1.6144 (2.084) Data (t): 0.001 Batch (t): 0.900, 569.146/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,13:52:24 | INFO | Train Epoch: 0 [ 819712/10637090 (8%)] Loss: 1.6176 (2.057) Data (t): 0.001 Batch (t): 0.902, 568.243/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,13:53:54 | INFO | Train Epoch: 0 [ 870912/10637090 (8%)] Loss: 1.7019 (2.037) Data (t): 0.001 Batch (t): 0.900, 570.904/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,13:55:24 | INFO | Train Epoch: 0 [ 922112/10637090 (9%)] Loss: 1.6732 (2.018) Data (t): 0.001 Batch (t): 0.900, 569.139/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,13:56:59 | INFO | Train Epoch: 0 [ 973312/10637090 (9%)] Loss: 1.5010 (1.992) Data (t): 0.001 Batch (t): 0.954, 569.706/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,13:58:29 | INFO | Train Epoch: 0 [ 1024512/10637090 (10%)] Loss: 1.5747 (1.972) Data (t): 0.001 Batch (t): 0.899, 568.905/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,13:59:59 | INFO | Train Epoch: 0 [ 1075712/10637090 (10%)] Loss: 1.5527 (1.953) Data (t): 0.001 Batch (t): 0.899, 569.620/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:01:29 | INFO | Train Epoch: 0 [ 1126912/10637090 (11%)] Loss: 1.6238 (1.939) Data (t): 0.001 Batch (t): 0.900, 567.832/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:02:59 | INFO | Train Epoch: 0 [ 1178112/10637090 (11%)] Loss: 1.3380 (1.914) Data (t): 0.001 Batch (t): 0.900, 570.975/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:04:34 | INFO | Train Epoch: 0 [ 1229312/10637090 (12%)] Loss: 1.2799 (1.889) Data (t): 0.001 Batch (t): 0.950, 327.981/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:06:06 | INFO | Train Epoch: 0 [ 1280512/10637090 (12%)] Loss: 1.3642 (1.868) Data (t): 0.001 Batch (t): 0.914, 569.657/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:07:36 | INFO | Train Epoch: 0 [ 1331712/10637090 (13%)] Loss: 1.5145 (1.855) Data (t): 0.001 Batch (t): 0.901, 567.738/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:09:06 | INFO | Train Epoch: 0 [ 1382912/10637090 (13%)] Loss: 1.6791 (1.849) Data (t): 0.001 Batch (t): 0.901, 569.797/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:10:36 | INFO | Train Epoch: 0 [ 1434112/10637090 (13%)] Loss: 1.4249 (1.834) Data (t): 0.001 Batch (t): 0.901, 568.832/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:12:07 | INFO | Train Epoch: 0 [ 1485312/10637090 (14%)] Loss: 1.4562 (1.822) Data (t): 0.001 Batch (t): 0.910, 566.126/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:13:42 | INFO | Train Epoch: 0 [ 1536512/10637090 (14%)] Loss: 1.4888 (1.811) Data (t): 0.001 Batch (t): 0.945, 564.916/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:15:12 | INFO | Train Epoch: 0 [ 1587712/10637090 (15%)] Loss: 1.4355 (1.799) Data (t): 0.001 Batch (t): 0.901, 570.004/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:16:42 | INFO | Train Epoch: 0 [ 1638912/10637090 (15%)] Loss: 1.4400 (1.788) Data (t): 0.001 Batch (t): 0.900, 568.622/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:18:12 | INFO | Train Epoch: 0 [ 1690112/10637090 (16%)] Loss: 1.2937 (1.774) Data (t): 0.001 Batch (t): 0.901, 569.205/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:19:43 | INFO | Train Epoch: 0 [ 1741312/10637090 (16%)] Loss: 1.4636 (1.765) Data (t): 0.001 Batch (t): 0.909, 271.898/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:21:18 | INFO | Train Epoch: 0 [ 1792512/10637090 (17%)] Loss: 1.3336 (1.753) Data (t): 0.001 Batch (t): 0.948, 571.164/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:22:48 | INFO | Train Epoch: 0 [ 1843712/10637090 (17%)] Loss: 1.4242 (1.744) Data (t): 0.001 Batch (t): 0.900, 569.579/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:24:17 | INFO | Train Epoch: 0 [ 1894912/10637090 (18%)] Loss: 1.2935 (1.732) Data (t): 0.001 Batch (t): 0.899, 569.778/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:25:47 | INFO | Train Epoch: 0 [ 1946112/10637090 (18%)] Loss: 1.3457 (1.722) Data (t): 0.001 Batch (t): 0.899, 570.664/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:27:17 | INFO | Train Epoch: 0 [ 1997312/10637090 (19%)] Loss: 1.4240 (1.715) Data (t): 0.001 Batch (t): 0.898, 566.842/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:28:53 | INFO | Train Epoch: 0 [ 2048512/10637090 (19%)] Loss: 1.4699 (1.709) Data (t): 0.001 Batch (t): 0.958, 571.469/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:30:23 | INFO | Train Epoch: 0 [ 2099712/10637090 (20%)] Loss: 1.5219 (1.704) Data (t): 0.001 Batch (t): 0.898, 569.506/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:31:53 | INFO | Train Epoch: 0 [ 2150912/10637090 (20%)] Loss: 1.3657 (1.697) Data (t): 0.001 Batch (t): 0.898, 569.664/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:33:22 | INFO | Train Epoch: 0 [ 2202112/10637090 (21%)] Loss: 1.3388 (1.688) Data (t): 0.001 Batch (t): 0.899, 570.094/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:34:52 | INFO | Train Epoch: 0 [ 2253312/10637090 (21%)] Loss: 1.4835 (1.684) Data (t): 0.001 Batch (t): 0.898, 568.711/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:36:28 | INFO | Train Epoch: 0 [ 2304512/10637090 (22%)] Loss: 1.4458 (1.679) Data (t): 0.001 Batch (t): 0.958, 327.824/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:37:58 | INFO | Train Epoch: 0 [ 2355712/10637090 (22%)] Loss: 1.4833 (1.675) Data (t): 0.001 Batch (t): 0.899, 571.156/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:39:28 | INFO | Train Epoch: 0 [ 2406912/10637090 (23%)] Loss: 1.5002 (1.671) Data (t): 0.001 Batch (t): 0.899, 571.241/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:40:58 | INFO | Train Epoch: 0 [ 2458112/10637090 (23%)] Loss: 1.5528 (1.669) Data (t): 0.001 Batch (t): 0.898, 568.659/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:42:28 | INFO | Train Epoch: 0 [ 2509312/10637090 (24%)] Loss: 1.4062 (1.663) Data (t): 0.001 Batch (t): 0.898, 571.460/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:44:00 | INFO | Train Epoch: 0 [ 2560512/10637090 (24%)] Loss: 1.4364 (1.659) Data (t): 0.001 Batch (t): 0.927, 571.364/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:45:33 | INFO | Train Epoch: 0 [ 2611712/10637090 (25%)] Loss: 1.2963 (1.652) Data (t): 0.001 Batch (t): 0.926, 570.582/s LR: 0.000001 Logit Scale: 99.999 - V4 -2024-11-26,14:47:03 | INFO | Train Epoch: 0 [ 2662912/10637090 (25%)] Loss: 1.2933 (1.645) Data (t): 0.001 Batch (t): 0.898, 569.676/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:48:33 | INFO | Train Epoch: 0 [ 2714112/10637090 (26%)] Loss: 1.2771 (1.638) Data (t): 0.001 Batch (t): 0.898, 569.279/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:50:02 | INFO | Train Epoch: 0 [ 2765312/10637090 (26%)] Loss: 1.3485 (1.633) Data (t): 0.001 Batch (t): 0.898, 568.489/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:51:34 | INFO | Train Epoch: 0 [ 2816512/10637090 (26%)] Loss: 1.2289 (1.626) Data (t): 0.001 Batch (t): 0.914, 572.484/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:53:08 | INFO | Train Epoch: 0 [ 2867712/10637090 (27%)] Loss: 1.4018 (1.622) Data (t): 0.001 Batch (t): 0.941, 572.417/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:54:38 | INFO | Train Epoch: 0 [ 2918912/10637090 (27%)] Loss: 1.3707 (1.618) Data (t): 0.001 Batch (t): 0.897, 571.299/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:56:07 | INFO | Train Epoch: 0 [ 2970112/10637090 (28%)] Loss: 1.3077 (1.612) Data (t): 0.001 Batch (t): 0.898, 569.293/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:57:37 | INFO | Train Epoch: 0 [ 3021312/10637090 (28%)] Loss: 1.3162 (1.607) Data (t): 0.001 Batch (t): 0.897, 569.669/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,14:59:07 | INFO | Train Epoch: 0 [ 3072512/10637090 (29%)] Loss: 1.1386 (1.600) Data (t): 0.001 Batch (t): 0.898, 570.275/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:00:43 | INFO | Train Epoch: 0 [ 3123712/10637090 (29%)] Loss: 1.3472 (1.596) Data (t): 0.001 Batch (t): 0.958, 570.992/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:02:12 | INFO | Train Epoch: 0 [ 3174912/10637090 (30%)] Loss: 1.3549 (1.592) Data (t): 0.001 Batch (t): 0.897, 571.916/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:03:42 | INFO | Train Epoch: 0 [ 3226112/10637090 (30%)] Loss: 1.3102 (1.587) Data (t): 0.001 Batch (t): 0.898, 569.407/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:05:12 | INFO | Train Epoch: 0 [ 3277312/10637090 (31%)] Loss: 1.2370 (1.582) Data (t): 0.001 Batch (t): 0.899, 570.822/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:06:42 | INFO | Train Epoch: 0 [ 3328512/10637090 (31%)] Loss: 1.3859 (1.579) Data (t): 0.001 Batch (t): 0.898, 571.059/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:08:16 | INFO | Train Epoch: 0 [ 3379712/10637090 (32%)] Loss: 1.3788 (1.576) Data (t): 0.001 Batch (t): 0.945, 569.700/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:09:47 | INFO | Train Epoch: 0 [ 3430912/10637090 (32%)] Loss: 1.2963 (1.572) Data (t): 0.001 Batch (t): 0.905, 571.613/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:11:17 | INFO | Train Epoch: 0 [ 3482112/10637090 (33%)] Loss: 1.3762 (1.569) Data (t): 0.001 Batch (t): 0.899, 568.268/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:12:47 | INFO | Train Epoch: 0 [ 3533312/10637090 (33%)] Loss: 1.3043 (1.565) Data (t): 0.001 Batch (t): 0.898, 569.454/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:14:16 | INFO | Train Epoch: 0 [ 3584512/10637090 (34%)] Loss: 1.3195 (1.562) Data (t): 0.001 Batch (t): 0.898, 571.086/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:15:50 | INFO | Train Epoch: 0 [ 3635712/10637090 (34%)] Loss: 1.3561 (1.559) Data (t): 0.001 Batch (t): 0.939, 571.159/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:17:22 | INFO | Train Epoch: 0 [ 3686912/10637090 (35%)] Loss: 1.5171 (1.558) Data (t): 0.001 Batch (t): 0.918, 569.509/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:18:52 | INFO | Train Epoch: 0 [ 3738112/10637090 (35%)] Loss: 1.2799 (1.555) Data (t): 0.001 Batch (t): 0.899, 571.044/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:20:22 | INFO | Train Epoch: 0 [ 3789312/10637090 (36%)] Loss: 1.1552 (1.549) Data (t): 0.001 Batch (t): 0.898, 569.819/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:21:52 | INFO | Train Epoch: 0 [ 3840512/10637090 (36%)] Loss: 1.3273 (1.546) Data (t): 0.001 Batch (t): 0.899, 568.020/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:23:24 | INFO | Train Epoch: 0 [ 3891712/10637090 (37%)] Loss: 1.2578 (1.543) Data (t): 0.001 Batch (t): 0.922, 571.115/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:24:58 | INFO | Train Epoch: 0 [ 3942912/10637090 (37%)] Loss: 1.2760 (1.539) Data (t): 0.001 Batch (t): 0.936, 570.522/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:26:27 | INFO | Train Epoch: 0 [ 3994112/10637090 (38%)] Loss: 1.2114 (1.535) Data (t): 0.001 Batch (t): 0.898, 571.387/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:27:57 | INFO | Train Epoch: 0 [ 4045312/10637090 (38%)] Loss: 1.2218 (1.531) Data (t): 0.001 Batch (t): 0.899, 568.116/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:29:27 | INFO | Train Epoch: 0 [ 4096512/10637090 (39%)] Loss: 1.2275 (1.527) Data (t): 0.001 Batch (t): 0.901, 569.249/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:30:58 | INFO | Train Epoch: 0 [ 4147712/10637090 (39%)] Loss: 1.2819 (1.524) Data (t): 0.001 Batch (t): 0.910, 570.455/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:32:33 | INFO | Train Epoch: 0 [ 4198912/10637090 (39%)] Loss: 1.2112 (1.521) Data (t): 0.001 Batch (t): 0.949, 568.145/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:34:03 | INFO | Train Epoch: 0 [ 4250112/10637090 (40%)] Loss: 1.3589 (1.519) Data (t): 0.001 Batch (t): 0.899, 570.476/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:35:33 | INFO | Train Epoch: 0 [ 4301312/10637090 (40%)] Loss: 1.4017 (1.517) Data (t): 0.001 Batch (t): 0.899, 571.676/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:37:03 | INFO | Train Epoch: 0 [ 4352512/10637090 (41%)] Loss: 1.2269 (1.514) Data (t): 0.001 Batch (t): 0.899, 568.670/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:38:33 | INFO | Train Epoch: 0 [ 4403712/10637090 (41%)] Loss: 1.2209 (1.511) Data (t): 0.001 Batch (t): 0.899, 570.317/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:40:08 | INFO | Train Epoch: 0 [ 4454912/10637090 (42%)] Loss: 1.2596 (1.508) Data (t): 0.001 Batch (t): 0.953, 571.961/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:41:39 | INFO | Train Epoch: 0 [ 4506112/10637090 (42%)] Loss: 1.2666 (1.505) Data (t): 0.001 Batch (t): 0.904, 570.514/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:43:08 | INFO | Train Epoch: 0 [ 4557312/10637090 (43%)] Loss: 1.3623 (1.503) Data (t): 0.001 Batch (t): 0.898, 569.718/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:44:38 | INFO | Train Epoch: 0 [ 4608512/10637090 (43%)] Loss: 1.1735 (1.500) Data (t): 0.001 Batch (t): 0.898, 570.883/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:46:08 | INFO | Train Epoch: 0 [ 4659712/10637090 (44%)] Loss: 1.3254 (1.498) Data (t): 0.001 Batch (t): 0.898, 571.044/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:47:42 | INFO | Train Epoch: 0 [ 4710912/10637090 (44%)] Loss: 1.3611 (1.496) Data (t): 0.001 Batch (t): 0.939, 570.032/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:49:14 | INFO | Train Epoch: 0 [ 4762112/10637090 (45%)] Loss: 1.4570 (1.496) Data (t): 0.001 Batch (t): 0.918, 569.304/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:50:44 | INFO | Train Epoch: 0 [ 4813312/10637090 (45%)] Loss: 1.2662 (1.494) Data (t): 0.001 Batch (t): 0.899, 568.471/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:52:14 | INFO | Train Epoch: 0 [ 4864512/10637090 (46%)] Loss: 1.2966 (1.492) Data (t): 0.001 Batch (t): 0.899, 569.787/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:53:44 | INFO | Train Epoch: 0 [ 4915712/10637090 (46%)] Loss: 1.4136 (1.491) Data (t): 0.001 Batch (t): 0.900, 570.394/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:55:17 | INFO | Train Epoch: 0 [ 4966912/10637090 (47%)] Loss: 1.2369 (1.488) Data (t): 0.001 Batch (t): 0.930, 569.732/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:56:50 | INFO | Train Epoch: 0 [ 5018112/10637090 (47%)] Loss: 1.2626 (1.486) Data (t): 0.001 Batch (t): 0.930, 566.710/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:58:20 | INFO | Train Epoch: 0 [ 5069312/10637090 (48%)] Loss: 1.1438 (1.482) Data (t): 0.001 Batch (t): 0.900, 569.983/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,15:59:50 | INFO | Train Epoch: 0 [ 5120512/10637090 (48%)] Loss: 1.3868 (1.482) Data (t): 0.001 Batch (t): 0.899, 570.512/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:01:20 | INFO | Train Epoch: 0 [ 5171712/10637090 (49%)] Loss: 1.3372 (1.480) Data (t): 0.001 Batch (t): 0.899, 572.159/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:02:52 | INFO | Train Epoch: 0 [ 5222912/10637090 (49%)] Loss: 1.1923 (1.477) Data (t): 0.001 Batch (t): 0.924, 567.108/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:04:26 | INFO | Train Epoch: 0 [ 5274112/10637090 (50%)] Loss: 1.2317 (1.475) Data (t): 0.001 Batch (t): 0.938, 570.208/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:05:56 | INFO | Train Epoch: 0 [ 5325312/10637090 (50%)] Loss: 1.2536 (1.473) Data (t): 0.001 Batch (t): 0.899, 569.669/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:07:26 | INFO | Train Epoch: 0 [ 5376512/10637090 (51%)] Loss: 1.2602 (1.471) Data (t): 0.001 Batch (t): 0.899, 568.815/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:08:56 | INFO | Train Epoch: 0 [ 5427712/10637090 (51%)] Loss: 1.2263 (1.469) Data (t): 0.001 Batch (t): 0.900, 570.632/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:10:27 | INFO | Train Epoch: 0 [ 5478912/10637090 (52%)] Loss: 1.2208 (1.466) Data (t): 0.001 Batch (t): 0.910, 569.131/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:12:02 | INFO | Train Epoch: 0 [ 5530112/10637090 (52%)] Loss: 1.3252 (1.465) Data (t): 0.001 Batch (t): 0.951, 569.417/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:13:32 | INFO | Train Epoch: 0 [ 5581312/10637090 (52%)] Loss: 1.3377 (1.464) Data (t): 0.001 Batch (t): 0.900, 568.483/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:15:02 | INFO | Train Epoch: 0 [ 5632512/10637090 (53%)] Loss: 1.3099 (1.462) Data (t): 0.001 Batch (t): 0.900, 569.297/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:16:32 | INFO | Train Epoch: 0 [ 5683712/10637090 (53%)] Loss: 1.2511 (1.461) Data (t): 0.001 Batch (t): 0.900, 570.130/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:18:02 | INFO | Train Epoch: 0 [ 5734912/10637090 (54%)] Loss: 1.2541 (1.459) Data (t): 0.001 Batch (t): 0.900, 568.688/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:19:37 | INFO | Train Epoch: 0 [ 5786112/10637090 (54%)] Loss: 1.1981 (1.456) Data (t): 0.001 Batch (t): 0.949, 568.508/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:21:08 | INFO | Train Epoch: 0 [ 5837312/10637090 (55%)] Loss: 1.2915 (1.455) Data (t): 0.001 Batch (t): 0.914, 569.130/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:22:38 | INFO | Train Epoch: 0 [ 5888512/10637090 (55%)] Loss: 1.2872 (1.454) Data (t): 0.001 Batch (t): 0.900, 569.196/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:24:08 | INFO | Train Epoch: 0 [ 5939712/10637090 (56%)] Loss: 1.2329 (1.452) Data (t): 0.001 Batch (t): 0.901, 566.003/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:25:38 | INFO | Train Epoch: 0 [ 5990912/10637090 (56%)] Loss: 1.2263 (1.450) Data (t): 0.001 Batch (t): 0.900, 569.279/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:27:11 | INFO | Train Epoch: 0 [ 6042112/10637090 (57%)] Loss: 1.3165 (1.449) Data (t): 0.001 Batch (t): 0.930, 570.634/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:28:44 | INFO | Train Epoch: 0 [ 6093312/10637090 (57%)] Loss: 1.2909 (1.447) Data (t): 0.001 Batch (t): 0.930, 570.690/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:30:14 | INFO | Train Epoch: 0 [ 6144512/10637090 (58%)] Loss: 1.1370 (1.445) Data (t): 0.001 Batch (t): 0.899, 569.606/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:31:44 | INFO | Train Epoch: 0 [ 6195712/10637090 (58%)] Loss: 1.3181 (1.444) Data (t): 0.001 Batch (t): 0.900, 571.386/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:33:14 | INFO | Train Epoch: 0 [ 6246912/10637090 (59%)] Loss: 1.2128 (1.442) Data (t): 0.001 Batch (t): 0.899, 566.859/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:34:46 | INFO | Train Epoch: 0 [ 6298112/10637090 (59%)] Loss: 1.1429 (1.439) Data (t): 0.001 Batch (t): 0.922, 568.686/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:36:20 | INFO | Train Epoch: 0 [ 6349312/10637090 (60%)] Loss: 1.2498 (1.438) Data (t): 0.001 Batch (t): 0.938, 568.535/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:37:50 | INFO | Train Epoch: 0 [ 6400512/10637090 (60%)] Loss: 1.2077 (1.436) Data (t): 0.001 Batch (t): 0.900, 569.745/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:39:20 | INFO | Train Epoch: 0 [ 6451712/10637090 (61%)] Loss: 1.2972 (1.435) Data (t): 0.001 Batch (t): 0.900, 571.371/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:40:50 | INFO | Train Epoch: 0 [ 6502912/10637090 (61%)] Loss: 1.1936 (1.433) Data (t): 0.001 Batch (t): 0.899, 569.002/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:42:22 | INFO | Train Epoch: 0 [ 6554112/10637090 (62%)] Loss: 1.2448 (1.432) Data (t): 0.001 Batch (t): 0.924, 570.664/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:43:56 | INFO | Train Epoch: 0 [ 6605312/10637090 (62%)] Loss: 1.1709 (1.430) Data (t): 0.001 Batch (t): 0.936, 570.478/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:45:26 | INFO | Train Epoch: 0 [ 6656512/10637090 (63%)] Loss: 1.2946 (1.429) Data (t): 0.001 Batch (t): 0.899, 569.146/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:46:56 | INFO | Train Epoch: 0 [ 6707712/10637090 (63%)] Loss: 1.3742 (1.428) Data (t): 0.001 Batch (t): 0.900, 568.575/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:48:26 | INFO | Train Epoch: 0 [ 6758912/10637090 (64%)] Loss: 1.1728 (1.426) Data (t): 0.001 Batch (t): 0.899, 569.071/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:49:57 | INFO | Train Epoch: 0 [ 6810112/10637090 (64%)] Loss: 1.2177 (1.425) Data (t): 0.001 Batch (t): 0.910, 568.539/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:51:31 | INFO | Train Epoch: 0 [ 6861312/10637090 (65%)] Loss: 1.2139 (1.423) Data (t): 0.001 Batch (t): 0.945, 568.533/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:53:02 | INFO | Train Epoch: 0 [ 6912512/10637090 (65%)] Loss: 1.2024 (1.422) Data (t): 0.001 Batch (t): 0.906, 570.082/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:54:32 | INFO | Train Epoch: 0 [ 6963712/10637090 (65%)] Loss: 1.2551 (1.420) Data (t): 0.001 Batch (t): 0.900, 567.906/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:56:02 | INFO | Train Epoch: 0 [ 7014912/10637090 (66%)] Loss: 1.4345 (1.420) Data (t): 0.001 Batch (t): 0.899, 570.889/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:57:32 | INFO | Train Epoch: 0 [ 7066112/10637090 (66%)] Loss: 1.1707 (1.419) Data (t): 0.001 Batch (t): 0.899, 567.717/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,16:59:06 | INFO | Train Epoch: 0 [ 7117312/10637090 (67%)] Loss: 1.2277 (1.417) Data (t): 0.001 Batch (t): 0.941, 569.467/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:00:38 | INFO | Train Epoch: 0 [ 7168512/10637090 (67%)] Loss: 1.1800 (1.416) Data (t): 0.001 Batch (t): 0.921, 569.605/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:02:08 | INFO | Train Epoch: 0 [ 7219712/10637090 (68%)] Loss: 1.1229 (1.414) Data (t): 0.001 Batch (t): 0.900, 569.514/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:03:38 | INFO | Train Epoch: 0 [ 7270912/10637090 (68%)] Loss: 1.3891 (1.413) Data (t): 0.001 Batch (t): 0.900, 569.009/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:05:08 | INFO | Train Epoch: 0 [ 7322112/10637090 (69%)] Loss: 1.0706 (1.411) Data (t): 0.001 Batch (t): 0.900, 566.596/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:06:40 | INFO | Train Epoch: 0 [ 7373312/10637090 (69%)] Loss: 1.3101 (1.410) Data (t): 0.001 Batch (t): 0.923, 571.396/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:08:13 | INFO | Train Epoch: 0 [ 7424512/10637090 (70%)] Loss: 1.1021 (1.408) Data (t): 0.001 Batch (t): 0.930, 570.699/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:09:43 | INFO | Train Epoch: 0 [ 7475712/10637090 (70%)] Loss: 1.1971 (1.407) Data (t): 0.001 Batch (t): 0.899, 569.514/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:11:13 | INFO | Train Epoch: 0 [ 7526912/10637090 (71%)] Loss: 1.0900 (1.405) Data (t): 0.001 Batch (t): 0.899, 568.834/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:12:43 | INFO | Train Epoch: 0 [ 7578112/10637090 (71%)] Loss: 1.2845 (1.404) Data (t): 0.001 Batch (t): 0.900, 569.436/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:14:15 | INFO | Train Epoch: 0 [ 7629312/10637090 (72%)] Loss: 1.0373 (1.401) Data (t): 0.001 Batch (t): 0.924, 570.141/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:15:49 | INFO | Train Epoch: 0 [ 7680512/10637090 (72%)] Loss: 1.1573 (1.400) Data (t): 0.001 Batch (t): 0.937, 571.663/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:17:19 | INFO | Train Epoch: 0 [ 7731712/10637090 (73%)] Loss: 1.2276 (1.399) Data (t): 0.001 Batch (t): 0.898, 571.793/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:18:49 | INFO | Train Epoch: 0 [ 7782912/10637090 (73%)] Loss: 1.2173 (1.397) Data (t): 0.001 Batch (t): 0.900, 569.486/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:20:19 | INFO | Train Epoch: 0 [ 7834112/10637090 (74%)] Loss: 1.1969 (1.396) Data (t): 0.001 Batch (t): 0.899, 567.732/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:21:51 | INFO | Train Epoch: 0 [ 7885312/10637090 (74%)] Loss: 1.3347 (1.396) Data (t): 0.001 Batch (t): 0.926, 568.903/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:23:24 | INFO | Train Epoch: 0 [ 7936512/10637090 (75%)] Loss: 1.2011 (1.394) Data (t): 0.001 Batch (t): 0.932, 571.177/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:24:55 | INFO | Train Epoch: 0 [ 7987712/10637090 (75%)] Loss: 1.2774 (1.394) Data (t): 0.001 Batch (t): 0.907, 569.049/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:26:25 | INFO | Train Epoch: 0 [ 8038912/10637090 (76%)] Loss: 1.0803 (1.392) Data (t): 0.001 Batch (t): 0.900, 571.377/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:27:55 | INFO | Train Epoch: 0 [ 8090112/10637090 (76%)] Loss: 1.2829 (1.391) Data (t): 0.001 Batch (t): 0.901, 568.095/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:29:25 | INFO | Train Epoch: 0 [ 8141312/10637090 (77%)] Loss: 1.1095 (1.389) Data (t): 0.001 Batch (t): 0.899, 569.986/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:31:00 | INFO | Train Epoch: 0 [ 8192512/10637090 (77%)] Loss: 1.2348 (1.388) Data (t): 0.001 Batch (t): 0.949, 571.927/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:32:31 | INFO | Train Epoch: 0 [ 8243712/10637090 (78%)] Loss: 1.1336 (1.387) Data (t): 0.001 Batch (t): 0.913, 570.498/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:34:01 | INFO | Train Epoch: 0 [ 8294912/10637090 (78%)] Loss: 1.2583 (1.386) Data (t): 0.001 Batch (t): 0.899, 570.770/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:35:31 | INFO | Train Epoch: 0 [ 8346112/10637090 (78%)] Loss: 1.1911 (1.385) Data (t): 0.001 Batch (t): 0.899, 571.924/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:37:01 | INFO | Train Epoch: 0 [ 8397312/10637090 (79%)] Loss: 1.1734 (1.383) Data (t): 0.001 Batch (t): 0.899, 565.386/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:38:34 | INFO | Train Epoch: 0 [ 8448512/10637090 (79%)] Loss: 1.2354 (1.383) Data (t): 0.001 Batch (t): 0.931, 570.636/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:40:07 | INFO | Train Epoch: 0 [ 8499712/10637090 (80%)] Loss: 1.1634 (1.381) Data (t): 0.001 Batch (t): 0.924, 567.707/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:41:36 | INFO | Train Epoch: 0 [ 8550912/10637090 (80%)] Loss: 1.2009 (1.380) Data (t): 0.001 Batch (t): 0.898, 569.702/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:43:06 | INFO | Train Epoch: 0 [ 8602112/10637090 (81%)] Loss: 1.0738 (1.378) Data (t): 0.001 Batch (t): 0.898, 570.831/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:44:36 | INFO | Train Epoch: 0 [ 8653312/10637090 (81%)] Loss: 1.2410 (1.378) Data (t): 0.001 Batch (t): 0.900, 569.181/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:46:08 | INFO | Train Epoch: 0 [ 8704512/10637090 (82%)] Loss: 1.3493 (1.377) Data (t): 0.001 Batch (t): 0.916, 571.074/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:47:41 | INFO | Train Epoch: 0 [ 8755712/10637090 (82%)] Loss: 1.1247 (1.376) Data (t): 0.001 Batch (t): 0.930, 570.210/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:49:11 | INFO | Train Epoch: 0 [ 8806912/10637090 (83%)] Loss: 1.1758 (1.375) Data (t): 0.001 Batch (t): 0.898, 569.424/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:50:41 | INFO | Train Epoch: 0 [ 8858112/10637090 (83%)] Loss: 1.4430 (1.375) Data (t): 0.001 Batch (t): 0.899, 570.117/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:52:10 | INFO | Train Epoch: 0 [ 8909312/10637090 (84%)] Loss: 1.2844 (1.375) Data (t): 0.001 Batch (t): 0.898, 569.938/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:53:43 | INFO | Train Epoch: 0 [ 8960512/10637090 (84%)] Loss: 1.2235 (1.374) Data (t): 0.001 Batch (t): 0.923, 569.088/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:55:16 | INFO | Train Epoch: 0 [ 9011712/10637090 (85%)] Loss: 1.3818 (1.374) Data (t): 0.001 Batch (t): 0.930, 570.654/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:56:46 | INFO | Train Epoch: 0 [ 9062912/10637090 (85%)] Loss: 1.2917 (1.373) Data (t): 0.001 Batch (t): 0.905, 568.413/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:58:16 | INFO | Train Epoch: 0 [ 9114112/10637090 (86%)] Loss: 1.4781 (1.374) Data (t): 0.001 Batch (t): 0.898, 571.441/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,17:59:46 | INFO | Train Epoch: 0 [ 9165312/10637090 (86%)] Loss: 1.1813 (1.373) Data (t): 0.001 Batch (t): 0.898, 572.722/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:01:17 | INFO | Train Epoch: 0 [ 9216512/10637090 (87%)] Loss: 1.2649 (1.372) Data (t): 0.001 Batch (t): 0.916, 571.388/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:02:51 | INFO | Train Epoch: 0 [ 9267712/10637090 (87%)] Loss: 1.1824 (1.371) Data (t): 0.001 Batch (t): 0.931, 568.484/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:04:22 | INFO | Train Epoch: 0 [ 9318912/10637090 (88%)] Loss: 1.1912 (1.370) Data (t): 0.001 Batch (t): 0.914, 568.624/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:05:52 | INFO | Train Epoch: 0 [ 9370112/10637090 (88%)] Loss: 1.4411 (1.371) Data (t): 0.001 Batch (t): 0.898, 571.071/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:07:21 | INFO | Train Epoch: 0 [ 9421312/10637090 (89%)] Loss: 0.98230 (1.369) Data (t): 0.001 Batch (t): 0.897, 573.989/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:08:51 | INFO | Train Epoch: 0 [ 9472512/10637090 (89%)] Loss: 1.1880 (1.368) Data (t): 0.001 Batch (t): 0.899, 572.881/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:10:26 | INFO | Train Epoch: 0 [ 9523712/10637090 (90%)] Loss: 1.3178 (1.367) Data (t): 0.001 Batch (t): 0.949, 314.720/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:11:57 | INFO | Train Epoch: 0 [ 9574912/10637090 (90%)] Loss: 1.2982 (1.367) Data (t): 0.001 Batch (t): 0.911, 571.868/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:13:27 | INFO | Train Epoch: 0 [ 9626112/10637090 (90%)] Loss: 1.2339 (1.366) Data (t): 0.001 Batch (t): 0.898, 570.843/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:14:57 | INFO | Train Epoch: 0 [ 9677312/10637090 (91%)] Loss: 1.3044 (1.366) Data (t): 0.001 Batch (t): 0.897, 570.286/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:16:27 | INFO | Train Epoch: 0 [ 9728512/10637090 (91%)] Loss: 1.2013 (1.365) Data (t): 0.001 Batch (t): 0.899, 571.575/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:18:00 | INFO | Train Epoch: 0 [ 9779712/10637090 (92%)] Loss: 1.1965 (1.364) Data (t): 0.001 Batch (t): 0.930, 573.865/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:19:33 | INFO | Train Epoch: 0 [ 9830912/10637090 (92%)] Loss: 1.2956 (1.364) Data (t): 0.001 Batch (t): 0.930, 570.286/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:21:02 | INFO | Train Epoch: 0 [ 9882112/10637090 (93%)] Loss: 1.2174 (1.363) Data (t): 0.001 Batch (t): 0.897, 571.233/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:22:32 | INFO | Train Epoch: 0 [ 9933312/10637090 (93%)] Loss: 1.2816 (1.363) Data (t): 0.001 Batch (t): 0.898, 570.071/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:24:02 | INFO | Train Epoch: 0 [ 9984512/10637090 (94%)] Loss: 1.2450 (1.362) Data (t): 0.001 Batch (t): 0.898, 570.906/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:25:34 | INFO | Train Epoch: 0 [10035712/10637090 (94%)] Loss: 1.3246 (1.362) Data (t): 0.001 Batch (t): 0.925, 571.460/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:27:08 | INFO | Train Epoch: 0 [10086912/10637090 (95%)] Loss: 1.0945 (1.361) Data (t): 0.001 Batch (t): 0.931, 571.226/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:28:38 | INFO | Train Epoch: 0 [10138112/10637090 (95%)] Loss: 1.3692 (1.361) Data (t): 0.001 Batch (t): 0.905, 570.400/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:30:08 | INFO | Train Epoch: 0 [10189312/10637090 (96%)] Loss: 1.2519 (1.360) Data (t): 0.001 Batch (t): 0.897, 571.391/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:31:37 | INFO | Train Epoch: 0 [10240512/10637090 (96%)] Loss: 1.2631 (1.360) Data (t): 0.001 Batch (t): 0.897, 572.820/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:33:09 | INFO | Train Epoch: 0 [10291712/10637090 (97%)] Loss: 1.2612 (1.359) Data (t): 0.001 Batch (t): 0.918, 570.538/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:34:42 | INFO | Train Epoch: 0 [10342912/10637090 (97%)] Loss: 1.1512 (1.358) Data (t): 0.001 Batch (t): 0.931, 570.150/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:36:13 | INFO | Train Epoch: 0 [10394112/10637090 (98%)] Loss: 1.2540 (1.358) Data (t): 0.001 Batch (t): 0.904, 569.754/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:37:43 | INFO | Train Epoch: 0 [10445312/10637090 (98%)] Loss: 1.3530 (1.357) Data (t): 0.001 Batch (t): 0.898, 570.079/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:39:12 | INFO | Train Epoch: 0 [10496512/10637090 (99%)] Loss: 1.1090 (1.356) Data (t): 0.001 Batch (t): 0.898, 570.845/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:40:44 | INFO | Train Epoch: 0 [10547712/10637090 (99%)] Loss: 1.3959 (1.356) Data (t): 0.001 Batch (t): 0.917, 570.875/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:42:17 | INFO | Train Epoch: 0 [10598912/10637090 (100%)] Loss: 1.1885 (1.356) Data (t): 0.001 Batch (t): 0.930, 572.573/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:43:25 | INFO | Train Epoch: 0 [10636800/10637090 (100%)] Loss: 1.1315 (1.355) Data (t): 0.002 Batch (t): 0.919, 570.887/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-26,18:43:32 | INFO | Start epoch 1 -2024-11-26,18:43:36 | INFO | Train Epoch: 1 [ 512/10637090 (0%)] Loss: 1.1272 (1.127) Data (t): 2.823 Batch (t): 3.742, 136.843/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,18:45:06 | INFO | Train Epoch: 1 [ 51712/10637090 (0%)] Loss: 1.3047 (1.216) Data (t): 0.001 Batch (t): 0.900, 570.502/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,18:46:35 | INFO | Train Epoch: 1 [ 102912/10637090 (1%)] Loss: 1.3060 (1.246) Data (t): 0.001 Batch (t): 0.899, 571.723/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,18:48:06 | INFO | Train Epoch: 1 [ 154112/10637090 (1%)] Loss: 1.2823 (1.255) Data (t): 0.001 Batch (t): 0.910, 570.631/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,18:49:39 | INFO | Train Epoch: 1 [ 205312/10637090 (2%)] Loss: 1.1136 (1.227) Data (t): 0.001 Batch (t): 0.924, 570.965/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,18:51:12 | INFO | Train Epoch: 1 [ 256512/10637090 (2%)] Loss: 1.1178 (1.209) Data (t): 0.001 Batch (t): 0.933, 568.766/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,18:52:42 | INFO | Train Epoch: 1 [ 307712/10637090 (3%)] Loss: 1.1960 (1.207) Data (t): 0.001 Batch (t): 0.899, 569.822/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,18:54:12 | INFO | Train Epoch: 1 [ 358912/10637090 (3%)] Loss: 1.1630 (1.201) Data (t): 0.001 Batch (t): 0.900, 568.075/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,18:55:43 | INFO | Train Epoch: 1 [ 410112/10637090 (4%)] Loss: 1.1219 (1.193) Data (t): 0.001 Batch (t): 0.909, 567.355/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,18:57:14 | INFO | Train Epoch: 1 [ 461312/10637090 (4%)] Loss: 1.2985 (1.203) Data (t): 0.001 Batch (t): 0.913, 566.980/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,18:58:47 | INFO | Train Epoch: 1 [ 512512/10637090 (5%)] Loss: 1.1123 (1.195) Data (t): 0.001 Batch (t): 0.928, 567.868/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:00:18 | INFO | Train Epoch: 1 [ 563712/10637090 (5%)] Loss: 1.1470 (1.191) Data (t): 0.001 Batch (t): 0.906, 571.082/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:01:48 | INFO | Train Epoch: 1 [ 614912/10637090 (6%)] Loss: 1.2744 (1.197) Data (t): 0.001 Batch (t): 0.899, 569.837/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:03:17 | INFO | Train Epoch: 1 [ 666112/10637090 (6%)] Loss: 1.0854 (1.189) Data (t): 0.001 Batch (t): 0.898, 568.425/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:04:50 | INFO | Train Epoch: 1 [ 717312/10637090 (7%)] Loss: 1.1137 (1.184) Data (t): 0.001 Batch (t): 0.921, 571.932/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:06:22 | INFO | Train Epoch: 1 [ 768512/10637090 (7%)] Loss: 1.0684 (1.177) Data (t): 0.001 Batch (t): 0.922, 567.544/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:07:53 | INFO | Train Epoch: 1 [ 819712/10637090 (8%)] Loss: 1.2528 (1.181) Data (t): 0.001 Batch (t): 0.912, 571.835/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:09:23 | INFO | Train Epoch: 1 [ 870912/10637090 (8%)] Loss: 1.0918 (1.176) Data (t): 0.001 Batch (t): 0.899, 567.959/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:10:53 | INFO | Train Epoch: 1 [ 922112/10637090 (9%)] Loss: 1.3715 (1.187) Data (t): 0.001 Batch (t): 0.900, 570.266/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:12:24 | INFO | Train Epoch: 1 [ 973312/10637090 (9%)] Loss: 1.1492 (1.185) Data (t): 0.001 Batch (t): 0.915, 570.129/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:13:57 | INFO | Train Epoch: 1 [ 1024512/10637090 (10%)] Loss: 1.0801 (1.180) Data (t): 0.001 Batch (t): 0.929, 564.189/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:15:28 | INFO | Train Epoch: 1 [ 1075712/10637090 (10%)] Loss: 1.1641 (1.179) Data (t): 0.001 Batch (t): 0.913, 563.636/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:16:58 | INFO | Train Epoch: 1 [ 1126912/10637090 (11%)] Loss: 1.0773 (1.175) Data (t): 0.001 Batch (t): 0.899, 571.633/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:18:28 | INFO | Train Epoch: 1 [ 1178112/10637090 (11%)] Loss: 1.1838 (1.175) Data (t): 0.001 Batch (t): 0.898, 569.161/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:19:59 | INFO | Train Epoch: 1 [ 1229312/10637090 (12%)] Loss: 1.1636 (1.175) Data (t): 0.001 Batch (t): 0.909, 566.818/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:21:31 | INFO | Train Epoch: 1 [ 1280512/10637090 (12%)] Loss: 1.2164 (1.176) Data (t): 0.001 Batch (t): 0.918, 569.866/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:23:04 | INFO | Train Epoch: 1 [ 1331712/10637090 (13%)] Loss: 1.1400 (1.175) Data (t): 0.001 Batch (t): 0.927, 572.217/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:24:33 | INFO | Train Epoch: 1 [ 1382912/10637090 (13%)] Loss: 1.3023 (1.179) Data (t): 0.001 Batch (t): 0.897, 570.665/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:26:03 | INFO | Train Epoch: 1 [ 1434112/10637090 (13%)] Loss: 1.0929 (1.176) Data (t): 0.001 Batch (t): 0.898, 569.468/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:27:34 | INFO | Train Epoch: 1 [ 1485312/10637090 (14%)] Loss: 1.1918 (1.177) Data (t): 0.001 Batch (t): 0.907, 570.668/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:29:06 | INFO | Train Epoch: 1 [ 1536512/10637090 (14%)] Loss: 1.1910 (1.177) Data (t): 0.001 Batch (t): 0.917, 570.185/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:30:38 | INFO | Train Epoch: 1 [ 1587712/10637090 (15%)] Loss: 1.2453 (1.180) Data (t): 0.001 Batch (t): 0.927, 571.132/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:32:08 | INFO | Train Epoch: 1 [ 1638912/10637090 (15%)] Loss: 1.3020 (1.183) Data (t): 0.001 Batch (t): 0.898, 570.613/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:33:38 | INFO | Train Epoch: 1 [ 1690112/10637090 (16%)] Loss: 1.1995 (1.184) Data (t): 0.001 Batch (t): 0.897, 571.345/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:35:07 | INFO | Train Epoch: 1 [ 1741312/10637090 (16%)] Loss: 1.2071 (1.184) Data (t): 0.001 Batch (t): 0.898, 569.574/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:36:40 | INFO | Train Epoch: 1 [ 1792512/10637090 (17%)] Loss: 1.2923 (1.187) Data (t): 0.001 Batch (t): 0.921, 568.688/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:38:12 | INFO | Train Epoch: 1 [ 1843712/10637090 (17%)] Loss: 1.1152 (1.185) Data (t): 0.001 Batch (t): 0.928, 327.312/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:39:43 | INFO | Train Epoch: 1 [ 1894912/10637090 (18%)] Loss: 1.0761 (1.183) Data (t): 0.001 Batch (t): 0.905, 571.211/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:41:13 | INFO | Train Epoch: 1 [ 1946112/10637090 (18%)] Loss: 1.3059 (1.186) Data (t): 0.001 Batch (t): 0.898, 569.130/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:42:42 | INFO | Train Epoch: 1 [ 1997312/10637090 (19%)] Loss: 1.1767 (1.186) Data (t): 0.001 Batch (t): 0.899, 567.882/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:44:15 | INFO | Train Epoch: 1 [ 2048512/10637090 (19%)] Loss: 1.1926 (1.186) Data (t): 0.001 Batch (t): 0.921, 571.107/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:45:47 | INFO | Train Epoch: 1 [ 2099712/10637090 (20%)] Loss: 0.98852 (1.181) Data (t): 0.001 Batch (t): 0.921, 565.473/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:47:18 | INFO | Train Epoch: 1 [ 2150912/10637090 (20%)] Loss: 1.1040 (1.179) Data (t): 0.001 Batch (t): 0.912, 570.252/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:48:48 | INFO | Train Epoch: 1 [ 2202112/10637090 (21%)] Loss: 1.1025 (1.177) Data (t): 0.001 Batch (t): 0.899, 568.608/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:50:18 | INFO | Train Epoch: 1 [ 2253312/10637090 (21%)] Loss: 1.1215 (1.176) Data (t): 0.001 Batch (t): 0.899, 571.476/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:51:49 | INFO | Train Epoch: 1 [ 2304512/10637090 (22%)] Loss: 1.2547 (1.178) Data (t): 0.001 Batch (t): 0.916, 571.678/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:53:21 | INFO | Train Epoch: 1 [ 2355712/10637090 (22%)] Loss: 1.1704 (1.178) Data (t): 0.001 Batch (t): 0.914, 569.301/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:54:53 | INFO | Train Epoch: 1 [ 2406912/10637090 (23%)] Loss: 1.1251 (1.177) Data (t): 0.001 Batch (t): 0.928, 571.243/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:56:23 | INFO | Train Epoch: 1 [ 2458112/10637090 (23%)] Loss: 1.3239 (1.180) Data (t): 0.001 Batch (t): 0.899, 570.420/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:57:53 | INFO | Train Epoch: 1 [ 2509312/10637090 (24%)] Loss: 1.1951 (1.180) Data (t): 0.001 Batch (t): 0.899, 571.080/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,19:59:24 | INFO | Train Epoch: 1 [ 2560512/10637090 (24%)] Loss: 1.2716 (1.182) Data (t): 0.001 Batch (t): 0.908, 568.033/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:00:56 | INFO | Train Epoch: 1 [ 2611712/10637090 (25%)] Loss: 1.1840 (1.182) Data (t): 0.001 Batch (t): 0.919, 571.383/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:02:29 | INFO | Train Epoch: 1 [ 2662912/10637090 (25%)] Loss: 1.1681 (1.182) Data (t): 0.001 Batch (t): 0.929, 570.933/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:03:59 | INFO | Train Epoch: 1 [ 2714112/10637090 (26%)] Loss: 1.2029 (1.182) Data (t): 0.001 Batch (t): 0.898, 571.058/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:05:29 | INFO | Train Epoch: 1 [ 2765312/10637090 (26%)] Loss: 1.1721 (1.182) Data (t): 0.001 Batch (t): 0.899, 566.658/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:06:59 | INFO | Train Epoch: 1 [ 2816512/10637090 (26%)] Loss: 1.1462 (1.181) Data (t): 0.001 Batch (t): 0.909, 569.733/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:08:32 | INFO | Train Epoch: 1 [ 2867712/10637090 (27%)] Loss: 1.1959 (1.181) Data (t): 0.001 Batch (t): 0.921, 569.731/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:10:05 | INFO | Train Epoch: 1 [ 2918912/10637090 (27%)] Loss: 1.2124 (1.182) Data (t): 0.001 Batch (t): 0.930, 325.826/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:11:34 | INFO | Train Epoch: 1 [ 2970112/10637090 (28%)] Loss: 1.2197 (1.183) Data (t): 0.001 Batch (t): 0.899, 571.573/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:13:04 | INFO | Train Epoch: 1 [ 3021312/10637090 (28%)] Loss: 1.2276 (1.183) Data (t): 0.001 Batch (t): 0.900, 568.050/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:14:34 | INFO | Train Epoch: 1 [ 3072512/10637090 (29%)] Loss: 1.0580 (1.181) Data (t): 0.001 Batch (t): 0.900, 568.648/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:16:07 | INFO | Train Epoch: 1 [ 3123712/10637090 (29%)] Loss: 1.1839 (1.181) Data (t): 0.001 Batch (t): 0.922, 569.204/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:17:39 | INFO | Train Epoch: 1 [ 3174912/10637090 (30%)] Loss: 1.3073 (1.183) Data (t): 0.001 Batch (t): 0.928, 569.760/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:19:10 | INFO | Train Epoch: 1 [ 3226112/10637090 (30%)] Loss: 1.2545 (1.184) Data (t): 0.001 Batch (t): 0.905, 567.984/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:20:40 | INFO | Train Epoch: 1 [ 3277312/10637090 (31%)] Loss: 1.1420 (1.184) Data (t): 0.001 Batch (t): 0.900, 572.471/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:22:10 | INFO | Train Epoch: 1 [ 3328512/10637090 (31%)] Loss: 1.3115 (1.186) Data (t): 0.001 Batch (t): 0.898, 572.822/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:23:42 | INFO | Train Epoch: 1 [ 3379712/10637090 (32%)] Loss: 1.1180 (1.185) Data (t): 0.001 Batch (t): 0.922, 569.949/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:25:14 | INFO | Train Epoch: 1 [ 3430912/10637090 (32%)] Loss: 1.1664 (1.184) Data (t): 0.001 Batch (t): 0.921, 568.426/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:26:45 | INFO | Train Epoch: 1 [ 3482112/10637090 (33%)] Loss: 1.0871 (1.183) Data (t): 0.001 Batch (t): 0.911, 571.307/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:28:15 | INFO | Train Epoch: 1 [ 3533312/10637090 (33%)] Loss: 1.3068 (1.185) Data (t): 0.001 Batch (t): 0.897, 572.100/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:29:45 | INFO | Train Epoch: 1 [ 3584512/10637090 (34%)] Loss: 1.1484 (1.184) Data (t): 0.001 Batch (t): 0.899, 571.685/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:31:16 | INFO | Train Epoch: 1 [ 3635712/10637090 (34%)] Loss: 1.2818 (1.186) Data (t): 0.001 Batch (t): 0.914, 573.222/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:32:49 | INFO | Train Epoch: 1 [ 3686912/10637090 (35%)] Loss: 1.1526 (1.185) Data (t): 0.001 Batch (t): 0.928, 571.248/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:34:20 | INFO | Train Epoch: 1 [ 3738112/10637090 (35%)] Loss: 0.99751 (1.183) Data (t): 0.001 Batch (t): 0.911, 571.760/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:35:50 | INFO | Train Epoch: 1 [ 3789312/10637090 (36%)] Loss: 1.1876 (1.183) Data (t): 0.001 Batch (t): 0.898, 569.914/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:37:20 | INFO | Train Epoch: 1 [ 3840512/10637090 (36%)] Loss: 1.1905 (1.183) Data (t): 0.001 Batch (t): 0.900, 570.957/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:38:51 | INFO | Train Epoch: 1 [ 3891712/10637090 (37%)] Loss: 1.1538 (1.182) Data (t): 0.001 Batch (t): 0.910, 565.558/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:40:23 | INFO | Train Epoch: 1 [ 3942912/10637090 (37%)] Loss: 1.0978 (1.181) Data (t): 0.001 Batch (t): 0.919, 569.475/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:41:56 | INFO | Train Epoch: 1 [ 3994112/10637090 (38%)] Loss: 1.1547 (1.181) Data (t): 0.001 Batch (t): 0.928, 570.513/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:43:25 | INFO | Train Epoch: 1 [ 4045312/10637090 (38%)] Loss: 1.4202 (1.184) Data (t): 0.001 Batch (t): 0.899, 569.742/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:44:55 | INFO | Train Epoch: 1 [ 4096512/10637090 (39%)] Loss: 1.2166 (1.184) Data (t): 0.001 Batch (t): 0.898, 574.082/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:46:25 | INFO | Train Epoch: 1 [ 4147712/10637090 (39%)] Loss: 1.1548 (1.184) Data (t): 0.001 Batch (t): 0.898, 573.561/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:47:57 | INFO | Train Epoch: 1 [ 4198912/10637090 (39%)] Loss: 1.2397 (1.185) Data (t): 0.001 Batch (t): 0.921, 569.802/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:49:31 | INFO | Train Epoch: 1 [ 4250112/10637090 (40%)] Loss: 1.2459 (1.185) Data (t): 0.001 Batch (t): 0.935, 569.457/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:51:00 | INFO | Train Epoch: 1 [ 4301312/10637090 (40%)] Loss: 1.1428 (1.185) Data (t): 0.001 Batch (t): 0.898, 572.439/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:52:30 | INFO | Train Epoch: 1 [ 4352512/10637090 (41%)] Loss: 1.1446 (1.184) Data (t): 0.001 Batch (t): 0.899, 570.299/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:54:00 | INFO | Train Epoch: 1 [ 4403712/10637090 (41%)] Loss: 1.0985 (1.183) Data (t): 0.001 Batch (t): 0.899, 570.359/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:55:32 | INFO | Train Epoch: 1 [ 4454912/10637090 (42%)] Loss: 1.1671 (1.183) Data (t): 0.001 Batch (t): 0.920, 572.689/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:57:05 | INFO | Train Epoch: 1 [ 4506112/10637090 (42%)] Loss: 1.1655 (1.183) Data (t): 0.001 Batch (t): 0.927, 322.234/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,20:58:35 | INFO | Train Epoch: 1 [ 4557312/10637090 (43%)] Loss: 1.1359 (1.183) Data (t): 0.001 Batch (t): 0.903, 571.277/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:00:05 | INFO | Train Epoch: 1 [ 4608512/10637090 (43%)] Loss: 1.2041 (1.183) Data (t): 0.001 Batch (t): 0.899, 569.163/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:01:35 | INFO | Train Epoch: 1 [ 4659712/10637090 (44%)] Loss: 1.1353 (1.182) Data (t): 0.001 Batch (t): 0.898, 570.520/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:03:07 | INFO | Train Epoch: 1 [ 4710912/10637090 (44%)] Loss: 1.2965 (1.184) Data (t): 0.001 Batch (t): 0.921, 568.051/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:04:39 | INFO | Train Epoch: 1 [ 4762112/10637090 (45%)] Loss: 1.1852 (1.184) Data (t): 0.001 Batch (t): 0.921, 571.169/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:06:10 | INFO | Train Epoch: 1 [ 4813312/10637090 (45%)] Loss: 1.1519 (1.183) Data (t): 0.001 Batch (t): 0.912, 568.091/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:07:40 | INFO | Train Epoch: 1 [ 4864512/10637090 (46%)] Loss: 1.2208 (1.184) Data (t): 0.001 Batch (t): 0.899, 569.083/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:09:10 | INFO | Train Epoch: 1 [ 4915712/10637090 (46%)] Loss: 1.0217 (1.182) Data (t): 0.001 Batch (t): 0.899, 568.266/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:10:41 | INFO | Train Epoch: 1 [ 4966912/10637090 (47%)] Loss: 1.2111 (1.182) Data (t): 0.001 Batch (t): 0.914, 572.742/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:12:14 | INFO | Train Epoch: 1 [ 5018112/10637090 (47%)] Loss: 1.1928 (1.182) Data (t): 0.001 Batch (t): 0.929, 570.259/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:13:46 | INFO | Train Epoch: 1 [ 5069312/10637090 (48%)] Loss: 1.1379 (1.182) Data (t): 0.001 Batch (t): 0.912, 566.363/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:15:15 | INFO | Train Epoch: 1 [ 5120512/10637090 (48%)] Loss: 0.98969 (1.180) Data (t): 0.001 Batch (t): 0.899, 566.979/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:16:45 | INFO | Train Epoch: 1 [ 5171712/10637090 (49%)] Loss: 1.2899 (1.181) Data (t): 0.001 Batch (t): 0.897, 570.488/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:18:15 | INFO | Train Epoch: 1 [ 5222912/10637090 (49%)] Loss: 1.1594 (1.181) Data (t): 0.001 Batch (t): 0.897, 571.862/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:19:48 | INFO | Train Epoch: 1 [ 5274112/10637090 (50%)] Loss: 1.1718 (1.181) Data (t): 0.001 Batch (t): 0.929, 569.649/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:21:21 | INFO | Train Epoch: 1 [ 5325312/10637090 (50%)] Loss: 1.1299 (1.180) Data (t): 0.001 Batch (t): 0.928, 572.603/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:22:50 | INFO | Train Epoch: 1 [ 5376512/10637090 (51%)] Loss: 1.2869 (1.181) Data (t): 0.001 Batch (t): 0.898, 569.349/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:24:20 | INFO | Train Epoch: 1 [ 5427712/10637090 (51%)] Loss: 1.1522 (1.181) Data (t): 0.001 Batch (t): 0.898, 570.411/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:25:50 | INFO | Train Epoch: 1 [ 5478912/10637090 (52%)] Loss: 1.2841 (1.182) Data (t): 0.001 Batch (t): 0.898, 569.983/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:27:22 | INFO | Train Epoch: 1 [ 5530112/10637090 (52%)] Loss: 1.2017 (1.182) Data (t): 0.001 Batch (t): 0.922, 569.563/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:28:55 | INFO | Train Epoch: 1 [ 5581312/10637090 (52%)] Loss: 1.2017 (1.182) Data (t): 0.001 Batch (t): 0.929, 319.869/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:30:25 | INFO | Train Epoch: 1 [ 5632512/10637090 (53%)] Loss: 1.2487 (1.183) Data (t): 0.001 Batch (t): 0.899, 570.889/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:31:55 | INFO | Train Epoch: 1 [ 5683712/10637090 (53%)] Loss: 1.2429 (1.183) Data (t): 0.001 Batch (t): 0.899, 571.607/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:33:25 | INFO | Train Epoch: 1 [ 5734912/10637090 (54%)] Loss: 1.0940 (1.183) Data (t): 0.001 Batch (t): 0.899, 568.140/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:34:57 | INFO | Train Epoch: 1 [ 5786112/10637090 (54%)] Loss: 1.0491 (1.181) Data (t): 0.001 Batch (t): 0.923, 572.406/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:36:29 | INFO | Train Epoch: 1 [ 5837312/10637090 (55%)] Loss: 1.1494 (1.181) Data (t): 0.001 Batch (t): 0.920, 573.115/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:38:00 | INFO | Train Epoch: 1 [ 5888512/10637090 (55%)] Loss: 1.0827 (1.180) Data (t): 0.001 Batch (t): 0.911, 569.390/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:39:30 | INFO | Train Epoch: 1 [ 5939712/10637090 (56%)] Loss: 1.2272 (1.181) Data (t): 0.001 Batch (t): 0.899, 569.449/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:41:00 | INFO | Train Epoch: 1 [ 5990912/10637090 (56%)] Loss: 1.2296 (1.181) Data (t): 0.001 Batch (t): 0.900, 569.298/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:42:32 | INFO | Train Epoch: 1 [ 6042112/10637090 (57%)] Loss: 1.1828 (1.181) Data (t): 0.001 Batch (t): 0.923, 569.099/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:44:04 | INFO | Train Epoch: 1 [ 6093312/10637090 (57%)] Loss: 1.1871 (1.181) Data (t): 0.001 Batch (t): 0.916, 569.842/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:45:35 | INFO | Train Epoch: 1 [ 6144512/10637090 (58%)] Loss: 1.1808 (1.181) Data (t): 0.001 Batch (t): 0.913, 570.897/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:47:05 | INFO | Train Epoch: 1 [ 6195712/10637090 (58%)] Loss: 1.2000 (1.181) Data (t): 0.001 Batch (t): 0.900, 569.644/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:48:35 | INFO | Train Epoch: 1 [ 6246912/10637090 (59%)] Loss: 1.3105 (1.182) Data (t): 0.001 Batch (t): 0.899, 569.345/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:50:06 | INFO | Train Epoch: 1 [ 6298112/10637090 (59%)] Loss: 1.0298 (1.181) Data (t): 0.001 Batch (t): 0.906, 569.081/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:51:38 | INFO | Train Epoch: 1 [ 6349312/10637090 (60%)] Loss: 1.0581 (1.180) Data (t): 0.001 Batch (t): 0.923, 569.032/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:53:11 | INFO | Train Epoch: 1 [ 6400512/10637090 (60%)] Loss: 1.1226 (1.180) Data (t): 0.001 Batch (t): 0.930, 573.111/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:54:41 | INFO | Train Epoch: 1 [ 6451712/10637090 (61%)] Loss: 1.0434 (1.179) Data (t): 0.001 Batch (t): 0.898, 570.992/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:56:11 | INFO | Train Epoch: 1 [ 6502912/10637090 (61%)] Loss: 1.1701 (1.179) Data (t): 0.001 Batch (t): 0.898, 569.764/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:57:41 | INFO | Train Epoch: 1 [ 6554112/10637090 (62%)] Loss: 1.0020 (1.177) Data (t): 0.001 Batch (t): 0.899, 568.551/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,21:59:13 | INFO | Train Epoch: 1 [ 6605312/10637090 (62%)] Loss: 1.1857 (1.177) Data (t): 0.001 Batch (t): 0.924, 568.507/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:00:47 | INFO | Train Epoch: 1 [ 6656512/10637090 (63%)] Loss: 1.1697 (1.177) Data (t): 0.001 Batch (t): 0.938, 571.196/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:02:17 | INFO | Train Epoch: 1 [ 6707712/10637090 (63%)] Loss: 1.3153 (1.178) Data (t): 0.001 Batch (t): 0.899, 570.651/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:03:47 | INFO | Train Epoch: 1 [ 6758912/10637090 (64%)] Loss: 1.2412 (1.179) Data (t): 0.001 Batch (t): 0.900, 566.658/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:05:16 | INFO | Train Epoch: 1 [ 6810112/10637090 (64%)] Loss: 1.2105 (1.179) Data (t): 0.001 Batch (t): 0.899, 569.025/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:06:49 | INFO | Train Epoch: 1 [ 6861312/10637090 (65%)] Loss: 1.2094 (1.179) Data (t): 0.001 Batch (t): 0.922, 572.277/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:08:22 | INFO | Train Epoch: 1 [ 6912512/10637090 (65%)] Loss: 1.1225 (1.179) Data (t): 0.001 Batch (t): 0.931, 569.517/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:09:52 | INFO | Train Epoch: 1 [ 6963712/10637090 (65%)] Loss: 1.2264 (1.179) Data (t): 0.001 Batch (t): 0.907, 566.350/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:11:22 | INFO | Train Epoch: 1 [ 7014912/10637090 (66%)] Loss: 1.1015 (1.179) Data (t): 0.001 Batch (t): 0.899, 568.496/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:12:52 | INFO | Train Epoch: 1 [ 7066112/10637090 (66%)] Loss: 1.1839 (1.179) Data (t): 0.001 Batch (t): 0.900, 568.224/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:14:25 | INFO | Train Epoch: 1 [ 7117312/10637090 (67%)] Loss: 1.2270 (1.179) Data (t): 0.001 Batch (t): 0.924, 569.192/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:15:57 | INFO | Train Epoch: 1 [ 7168512/10637090 (67%)] Loss: 1.1463 (1.179) Data (t): 0.001 Batch (t): 0.923, 569.831/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:17:28 | INFO | Train Epoch: 1 [ 7219712/10637090 (68%)] Loss: 1.0701 (1.178) Data (t): 0.001 Batch (t): 0.911, 571.012/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:18:58 | INFO | Train Epoch: 1 [ 7270912/10637090 (68%)] Loss: 1.1044 (1.177) Data (t): 0.001 Batch (t): 0.899, 567.547/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:20:28 | INFO | Train Epoch: 1 [ 7322112/10637090 (69%)] Loss: 1.2390 (1.178) Data (t): 0.001 Batch (t): 0.899, 566.791/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:21:59 | INFO | Train Epoch: 1 [ 7373312/10637090 (69%)] Loss: 1.1801 (1.178) Data (t): 0.001 Batch (t): 0.914, 569.889/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:23:33 | INFO | Train Epoch: 1 [ 7424512/10637090 (70%)] Loss: 1.1531 (1.178) Data (t): 0.001 Batch (t): 0.934, 570.462/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:25:04 | INFO | Train Epoch: 1 [ 7475712/10637090 (70%)] Loss: 1.2969 (1.179) Data (t): 0.001 Batch (t): 0.913, 570.918/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:26:34 | INFO | Train Epoch: 1 [ 7526912/10637090 (71%)] Loss: 1.1912 (1.179) Data (t): 0.001 Batch (t): 0.900, 570.391/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:28:04 | INFO | Train Epoch: 1 [ 7578112/10637090 (71%)] Loss: 1.1033 (1.178) Data (t): 0.001 Batch (t): 0.899, 570.299/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:29:35 | INFO | Train Epoch: 1 [ 7629312/10637090 (72%)] Loss: 1.0673 (1.177) Data (t): 0.001 Batch (t): 0.907, 570.048/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:31:07 | INFO | Train Epoch: 1 [ 7680512/10637090 (72%)] Loss: 1.1071 (1.177) Data (t): 0.001 Batch (t): 0.924, 567.562/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:32:40 | INFO | Train Epoch: 1 [ 7731712/10637090 (73%)] Loss: 1.1401 (1.177) Data (t): 0.001 Batch (t): 0.932, 570.268/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:34:10 | INFO | Train Epoch: 1 [ 7782912/10637090 (73%)] Loss: 1.0985 (1.176) Data (t): 0.001 Batch (t): 0.900, 568.763/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:35:40 | INFO | Train Epoch: 1 [ 7834112/10637090 (74%)] Loss: 1.2812 (1.177) Data (t): 0.001 Batch (t): 0.899, 570.524/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:37:10 | INFO | Train Epoch: 1 [ 7885312/10637090 (74%)] Loss: 1.2813 (1.178) Data (t): 0.001 Batch (t): 0.900, 569.218/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:38:43 | INFO | Train Epoch: 1 [ 7936512/10637090 (75%)] Loss: 1.2167 (1.178) Data (t): 0.001 Batch (t): 0.923, 568.970/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:40:17 | INFO | Train Epoch: 1 [ 7987712/10637090 (75%)] Loss: 1.0725 (1.177) Data (t): 0.001 Batch (t): 0.939, 569.014/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:41:46 | INFO | Train Epoch: 1 [ 8038912/10637090 (76%)] Loss: 1.1530 (1.177) Data (t): 0.001 Batch (t): 0.897, 569.460/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:43:16 | INFO | Train Epoch: 1 [ 8090112/10637090 (76%)] Loss: 1.1359 (1.177) Data (t): 0.001 Batch (t): 0.898, 568.609/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:44:46 | INFO | Train Epoch: 1 [ 8141312/10637090 (77%)] Loss: 1.3262 (1.178) Data (t): 0.001 Batch (t): 0.899, 567.700/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:46:18 | INFO | Train Epoch: 1 [ 8192512/10637090 (77%)] Loss: 1.3573 (1.179) Data (t): 0.001 Batch (t): 0.924, 571.296/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:47:51 | INFO | Train Epoch: 1 [ 8243712/10637090 (78%)] Loss: 1.3213 (1.180) Data (t): 0.001 Batch (t): 0.925, 570.037/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:49:22 | INFO | Train Epoch: 1 [ 8294912/10637090 (78%)] Loss: 1.2602 (1.180) Data (t): 0.001 Batch (t): 0.914, 570.789/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:50:52 | INFO | Train Epoch: 1 [ 8346112/10637090 (78%)] Loss: 1.1083 (1.180) Data (t): 0.001 Batch (t): 0.899, 568.988/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:52:22 | INFO | Train Epoch: 1 [ 8397312/10637090 (79%)] Loss: 1.1070 (1.179) Data (t): 0.001 Batch (t): 0.897, 571.238/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:53:53 | INFO | Train Epoch: 1 [ 8448512/10637090 (79%)] Loss: 1.3084 (1.180) Data (t): 0.001 Batch (t): 0.913, 569.511/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:55:26 | INFO | Train Epoch: 1 [ 8499712/10637090 (80%)] Loss: 1.2415 (1.180) Data (t): 0.001 Batch (t): 0.932, 570.018/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:56:58 | INFO | Train Epoch: 1 [ 8550912/10637090 (80%)] Loss: 1.1739 (1.180) Data (t): 0.001 Batch (t): 0.914, 565.601/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:58:28 | INFO | Train Epoch: 1 [ 8602112/10637090 (81%)] Loss: 1.2912 (1.181) Data (t): 0.001 Batch (t): 0.899, 567.853/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,22:59:58 | INFO | Train Epoch: 1 [ 8653312/10637090 (81%)] Loss: 1.0921 (1.181) Data (t): 0.001 Batch (t): 0.899, 573.454/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:01:29 | INFO | Train Epoch: 1 [ 8704512/10637090 (82%)] Loss: 1.1525 (1.180) Data (t): 0.001 Batch (t): 0.914, 571.670/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:03:02 | INFO | Train Epoch: 1 [ 8755712/10637090 (82%)] Loss: 1.1539 (1.180) Data (t): 0.001 Batch (t): 0.926, 262.009/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:04:34 | INFO | Train Epoch: 1 [ 8806912/10637090 (83%)] Loss: 1.2145 (1.180) Data (t): 0.001 Batch (t): 0.921, 570.100/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:06:04 | INFO | Train Epoch: 1 [ 8858112/10637090 (83%)] Loss: 1.0540 (1.180) Data (t): 0.001 Batch (t): 0.900, 567.721/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:07:34 | INFO | Train Epoch: 1 [ 8909312/10637090 (84%)] Loss: 1.2772 (1.180) Data (t): 0.001 Batch (t): 0.899, 569.287/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:09:04 | INFO | Train Epoch: 1 [ 8960512/10637090 (84%)] Loss: 1.2035 (1.180) Data (t): 0.001 Batch (t): 0.907, 569.264/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:10:37 | INFO | Train Epoch: 1 [ 9011712/10637090 (85%)] Loss: 1.2042 (1.180) Data (t): 0.001 Batch (t): 0.924, 568.662/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:12:10 | INFO | Train Epoch: 1 [ 9062912/10637090 (85%)] Loss: 1.0957 (1.180) Data (t): 0.001 Batch (t): 0.930, 569.348/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:13:39 | INFO | Train Epoch: 1 [ 9114112/10637090 (86%)] Loss: 1.2101 (1.180) Data (t): 0.001 Batch (t): 0.899, 572.392/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:15:09 | INFO | Train Epoch: 1 [ 9165312/10637090 (86%)] Loss: 1.0783 (1.180) Data (t): 0.001 Batch (t): 0.899, 570.228/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:16:39 | INFO | Train Epoch: 1 [ 9216512/10637090 (87%)] Loss: 1.2411 (1.180) Data (t): 0.001 Batch (t): 0.900, 562.989/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:18:12 | INFO | Train Epoch: 1 [ 9267712/10637090 (87%)] Loss: 1.2212 (1.180) Data (t): 0.001 Batch (t): 0.926, 567.625/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:19:46 | INFO | Train Epoch: 1 [ 9318912/10637090 (88%)] Loss: 1.2338 (1.180) Data (t): 0.001 Batch (t): 0.940, 569.754/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:21:16 | INFO | Train Epoch: 1 [ 9370112/10637090 (88%)] Loss: 1.2923 (1.181) Data (t): 0.001 Batch (t): 0.899, 567.528/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:22:46 | INFO | Train Epoch: 1 [ 9421312/10637090 (89%)] Loss: 1.0940 (1.181) Data (t): 0.001 Batch (t): 0.900, 569.919/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:24:16 | INFO | Train Epoch: 1 [ 9472512/10637090 (89%)] Loss: 1.2164 (1.181) Data (t): 0.001 Batch (t): 0.900, 570.008/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:25:47 | INFO | Train Epoch: 1 [ 9523712/10637090 (90%)] Loss: 1.0894 (1.180) Data (t): 0.001 Batch (t): 0.915, 568.146/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:27:21 | INFO | Train Epoch: 1 [ 9574912/10637090 (90%)] Loss: 1.1913 (1.180) Data (t): 0.001 Batch (t): 0.934, 569.573/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:28:52 | INFO | Train Epoch: 1 [ 9626112/10637090 (90%)] Loss: 1.0642 (1.180) Data (t): 0.001 Batch (t): 0.914, 568.430/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:30:22 | INFO | Train Epoch: 1 [ 9677312/10637090 (91%)] Loss: 1.1439 (1.180) Data (t): 0.001 Batch (t): 0.898, 572.201/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:31:52 | INFO | Train Epoch: 1 [ 9728512/10637090 (91%)] Loss: 1.1327 (1.179) Data (t): 0.001 Batch (t): 0.898, 570.895/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:33:23 | INFO | Train Epoch: 1 [ 9779712/10637090 (92%)] Loss: 1.1159 (1.179) Data (t): 0.001 Batch (t): 0.914, 570.508/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:34:57 | INFO | Train Epoch: 1 [ 9830912/10637090 (92%)] Loss: 1.2168 (1.179) Data (t): 0.001 Batch (t): 0.934, 567.726/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:36:28 | INFO | Train Epoch: 1 [ 9882112/10637090 (93%)] Loss: 1.1149 (1.179) Data (t): 0.001 Batch (t): 0.914, 570.335/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:37:58 | INFO | Train Epoch: 1 [ 9933312/10637090 (93%)] Loss: 1.1064 (1.178) Data (t): 0.001 Batch (t): 0.899, 568.050/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:39:28 | INFO | Train Epoch: 1 [ 9984512/10637090 (94%)] Loss: 1.1443 (1.178) Data (t): 0.001 Batch (t): 0.901, 568.709/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:40:59 | INFO | Train Epoch: 1 [10035712/10637090 (94%)] Loss: 1.2071 (1.178) Data (t): 0.001 Batch (t): 0.914, 569.486/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:42:31 | INFO | Train Epoch: 1 [10086912/10637090 (95%)] Loss: 1.2291 (1.179) Data (t): 0.001 Batch (t): 0.917, 568.210/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:44:04 | INFO | Train Epoch: 1 [10138112/10637090 (95%)] Loss: 0.94618 (1.178) Data (t): 0.001 Batch (t): 0.933, 566.726/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:45:34 | INFO | Train Epoch: 1 [10189312/10637090 (96%)] Loss: 1.1821 (1.178) Data (t): 0.001 Batch (t): 0.899, 567.719/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:47:04 | INFO | Train Epoch: 1 [10240512/10637090 (96%)] Loss: 1.1193 (1.177) Data (t): 0.001 Batch (t): 0.899, 567.518/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:48:35 | INFO | Train Epoch: 1 [10291712/10637090 (97%)] Loss: 1.0365 (1.177) Data (t): 0.001 Batch (t): 0.905, 571.630/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:50:06 | INFO | Train Epoch: 1 [10342912/10637090 (97%)] Loss: 1.0404 (1.176) Data (t): 0.001 Batch (t): 0.917, 569.601/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:51:40 | INFO | Train Epoch: 1 [10394112/10637090 (98%)] Loss: 1.1874 (1.176) Data (t): 0.001 Batch (t): 0.938, 567.593/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:53:10 | INFO | Train Epoch: 1 [10445312/10637090 (98%)] Loss: 1.0585 (1.175) Data (t): 0.001 Batch (t): 0.899, 568.669/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:54:40 | INFO | Train Epoch: 1 [10496512/10637090 (99%)] Loss: 1.1873 (1.175) Data (t): 0.001 Batch (t): 0.900, 565.194/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:56:10 | INFO | Train Epoch: 1 [10547712/10637090 (99%)] Loss: 1.0841 (1.175) Data (t): 0.001 Batch (t): 0.899, 570.082/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:57:42 | INFO | Train Epoch: 1 [10598912/10637090 (100%)] Loss: 0.96767 (1.174) Data (t): 0.001 Batch (t): 0.925, 570.929/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-26,23:58:51 | INFO | Train Epoch: 1 [10636800/10637090 (100%)] Loss: 1.2094 (1.174) Data (t): 0.002 Batch (t): 0.933, 573.472/s LR: 0.000000 Logit Scale: 100.000 - V4 diff --git a/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten_decimal2/2024_11_26-13_26_45-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/params.txt b/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten_decimal2/2024_11_26-13_26_45-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/params.txt deleted file mode 100644 index 1371a73a49bc0db1b450f9d199337ea24ff4b08c..0000000000000000000000000000000000000000 --- a/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten_decimal2/2024_11_26-13_26_45-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/params.txt +++ /dev/null @@ -1,67 +0,0 @@ -batch_size: 64 -beta1: 0.9 -beta2: 0.98 -checkpoint_path: data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten_decimal2/2024_11_26-13_26_45-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/checkpoints -copy_codebase: False -csv_caption_key: caption -csv_hard_captions_key: neg_caption -csv_img_key: img_path -csv_separator: , -dataset_resampled: False -dataset_type: csv -ddp_static_graph: False -debug: False -device: cuda:0 -dist_backend: nccl -dist_url: env:// -distributed: True -epochs: 2 -eps: 1e-06 -force_quick_gelu: True -gather_with_grad: False -grad_checkpointing: False -horovod: False -imagenet_v2: None -imagenet_val: None -local_loss: False -local_rank: 0 -lock_image: False -lock_image_freeze_bn_stats: False -lock_image_unlocked_groups: 0 -log_level: 20 -log_local: False -log_path: data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten_decimal2/2024_11_26-13_26_45-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/out.log -logs: data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten_decimal2 -lr: 1e-06 -model: ViT-L-14-336 -name: 2024_11_26-13_26_45-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp -no_set_device_rank: False -norm_gradient_clip: None -precision: amp -pretrained: data/openclip-vit-14-336/openclip_model.pt -pretrained_image: False -rank: 0 -report_to: wandb -resume: None -save_frequency: 1 -save_most_recent: False -seed: 0 -skip_scheduler: False -tensorboard: False -tensorboard_path: -torchscript: False -trace: False -train_data: csv_data/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten_decimal2.csv -train_num_samples: None -use_bn_sync: False -val_data: None -val_frequency: 1 -val_num_samples: None -wandb: True -wandb_notes: -wandb_project: neg-clip-plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten_decimal2 -warmup: 0 -wd: 0.1 -workers: 4 -world_size: 8 -zeroshot_frequency: 2 diff --git a/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten_decimal2/2024_11_26-23_59_33-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/checkpoints/epoch_1.pt b/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten_decimal2/2024_11_26-23_59_33-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/checkpoints/epoch_1.pt deleted file mode 100644 index d5007381ce74321c52b4c3519ac79101ef86b592..0000000000000000000000000000000000000000 --- a/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten_decimal2/2024_11_26-23_59_33-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/checkpoints/epoch_1.pt +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:05200113a7debf4bb778eb5be911589ec276996de31db68df3d2b817036d39d6 -size 5135890710 diff --git a/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten_decimal2/2024_11_26-23_59_33-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/checkpoints/epoch_2.pt b/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten_decimal2/2024_11_26-23_59_33-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/checkpoints/epoch_2.pt deleted file mode 100644 index 3e79fe517fb76c9e6cb9b0df4ae7c5a632e5302b..0000000000000000000000000000000000000000 --- a/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten_decimal2/2024_11_26-23_59_33-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/checkpoints/epoch_2.pt +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:513b1979b9b90c2d986e67257ccc243c080330b27f272ed625c65555970f6a4b -size 5135890710 diff --git a/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten_decimal2/2024_11_26-23_59_33-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/out.log b/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten_decimal2/2024_11_26-23_59_33-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/out.log deleted file mode 100644 index f4b3fdaf2ba2548cb1eb470872343637204cfed7..0000000000000000000000000000000000000000 --- a/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten_decimal2/2024_11_26-23_59_33-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/out.log +++ /dev/null @@ -1,534 +0,0 @@ -2024-11-26,23:59:33 | INFO | Running in distributed mode with multiple processes. Device: cuda:0.Process (global: 0, local 0), total 8. -2024-11-26,23:59:33 | INFO | Loading ViT-L-14-336 model config. -2024-11-26,23:59:36 | INFO | Loading pretrained ViT-L-14-336 weights (data/openclip-vit-14-336/openclip_model.pt). -2024-11-26,23:59:43 | INFO | Model: -2024-11-26,23:59:43 | INFO | CLIP( - (visual): VisualTransformer( - (conv1): Conv2d(3, 1024, kernel_size=(14, 14), stride=(14, 14), bias=False) - (ln_pre): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) - (transformer): Transformer( - (resblocks): ModuleList( - (0-23): 24 x ResidualAttentionBlock( - (attn): MultiheadAttention( - (out_proj): NonDynamicallyQuantizableLinear(in_features=1024, out_features=1024, bias=True) - ) - (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) - (mlp): Sequential( - (c_fc): Linear(in_features=1024, out_features=4096, bias=True) - (gelu): QuickGELU() - (c_proj): Linear(in_features=4096, out_features=1024, bias=True) - ) - (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) - ) - ) - ) - (ln_post): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) - ) - (transformer): Transformer( - (resblocks): ModuleList( - (0-11): 12 x ResidualAttentionBlock( - (attn): MultiheadAttention( - (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) - ) - (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) - (mlp): Sequential( - (c_fc): Linear(in_features=768, out_features=3072, bias=True) - (gelu): QuickGELU() - (c_proj): Linear(in_features=3072, out_features=768, bias=True) - ) - (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) - ) - ) - ) - (token_embedding): Embedding(49408, 768) - (ln_final): LayerNorm((768,), eps=1e-05, elementwise_affine=True) -) -2024-11-26,23:59:43 | INFO | Params: -2024-11-26,23:59:43 | INFO | batch_size: 64 -2024-11-26,23:59:43 | INFO | beta1: 0.9 -2024-11-26,23:59:43 | INFO | beta2: 0.98 -2024-11-26,23:59:43 | INFO | checkpoint_path: data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten_decimal2/2024_11_26-23_59_33-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/checkpoints -2024-11-26,23:59:43 | INFO | copy_codebase: False -2024-11-26,23:59:43 | INFO | csv_caption_key: caption -2024-11-26,23:59:43 | INFO | csv_hard_captions_key: neg_caption -2024-11-26,23:59:43 | INFO | csv_img_key: img_path -2024-11-26,23:59:43 | INFO | csv_separator: , -2024-11-26,23:59:43 | INFO | dataset_resampled: False -2024-11-26,23:59:43 | INFO | dataset_type: csv -2024-11-26,23:59:43 | INFO | ddp_static_graph: False -2024-11-26,23:59:43 | INFO | debug: False -2024-11-26,23:59:43 | INFO | device: cuda:0 -2024-11-26,23:59:43 | INFO | dist_backend: nccl -2024-11-26,23:59:43 | INFO | dist_url: env:// -2024-11-26,23:59:43 | INFO | distributed: True -2024-11-26,23:59:43 | INFO | epochs: 2 -2024-11-26,23:59:43 | INFO | eps: 1e-06 -2024-11-26,23:59:43 | INFO | force_quick_gelu: True -2024-11-26,23:59:43 | INFO | gather_with_grad: False -2024-11-26,23:59:43 | INFO | grad_checkpointing: False -2024-11-26,23:59:43 | INFO | horovod: False -2024-11-26,23:59:43 | INFO | imagenet_v2: None -2024-11-26,23:59:43 | INFO | imagenet_val: None -2024-11-26,23:59:43 | INFO | local_loss: False -2024-11-26,23:59:43 | INFO | local_rank: 0 -2024-11-26,23:59:43 | INFO | lock_image: False -2024-11-26,23:59:43 | INFO | lock_image_freeze_bn_stats: False -2024-11-26,23:59:43 | INFO | lock_image_unlocked_groups: 0 -2024-11-26,23:59:43 | INFO | log_level: 20 -2024-11-26,23:59:43 | INFO | log_local: False -2024-11-26,23:59:43 | INFO | log_path: data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten_decimal2/2024_11_26-23_59_33-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/out.log -2024-11-26,23:59:43 | INFO | logs: data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten_decimal2 -2024-11-26,23:59:43 | INFO | lr: 5e-06 -2024-11-26,23:59:43 | INFO | model: ViT-L-14-336 -2024-11-26,23:59:43 | INFO | name: 2024_11_26-23_59_33-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp -2024-11-26,23:59:43 | INFO | no_set_device_rank: False -2024-11-26,23:59:43 | INFO | norm_gradient_clip: None -2024-11-26,23:59:43 | INFO | precision: amp -2024-11-26,23:59:43 | INFO | pretrained: data/openclip-vit-14-336/openclip_model.pt -2024-11-26,23:59:43 | INFO | pretrained_image: False -2024-11-26,23:59:43 | INFO | rank: 0 -2024-11-26,23:59:43 | INFO | report_to: wandb -2024-11-26,23:59:43 | INFO | resume: None -2024-11-26,23:59:43 | INFO | save_frequency: 1 -2024-11-26,23:59:43 | INFO | save_most_recent: False -2024-11-26,23:59:43 | INFO | seed: 0 -2024-11-26,23:59:43 | INFO | skip_scheduler: False -2024-11-26,23:59:43 | INFO | tensorboard: False -2024-11-26,23:59:43 | INFO | tensorboard_path: -2024-11-26,23:59:43 | INFO | torchscript: False -2024-11-26,23:59:43 | INFO | trace: False -2024-11-26,23:59:43 | INFO | train_data: csv_data/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten_decimal2.csv -2024-11-26,23:59:43 | INFO | train_num_samples: None -2024-11-26,23:59:43 | INFO | use_bn_sync: False -2024-11-26,23:59:43 | INFO | val_data: None -2024-11-26,23:59:43 | INFO | val_frequency: 1 -2024-11-26,23:59:43 | INFO | val_num_samples: None -2024-11-26,23:59:43 | INFO | wandb: True -2024-11-26,23:59:43 | INFO | wandb_notes: -2024-11-26,23:59:43 | INFO | wandb_project: neg-clip-plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten_decimal2 -2024-11-26,23:59:43 | INFO | warmup: 0 -2024-11-26,23:59:43 | INFO | wd: 0.1 -2024-11-26,23:59:43 | INFO | workers: 4 -2024-11-26,23:59:43 | INFO | world_size: 8 -2024-11-26,23:59:43 | INFO | zeroshot_frequency: 2 -2024-11-27,00:00:38 | INFO | Init a wandb project! -2024-11-27,00:00:58 | INFO | Start epoch 0 -2024-11-27,00:01:05 | INFO | Train Epoch: 0 [ 512/10637090 (0%)] Loss: 6.0211 (6.021) Data (t): 2.743 Batch (t): 6.422, 79.7278/s LR: 0.000005 Logit Scale: 100.000 - V4 -2024-11-27,00:02:36 | INFO | Train Epoch: 0 [ 51712/10637090 (0%)] Loss: 2.0704 (4.046) Data (t): 0.001 Batch (t): 0.909, 567.485/s LR: 0.000005 Logit Scale: 99.995 - V4 -2024-11-27,00:04:06 | INFO | Train Epoch: 0 [ 102912/10637090 (1%)] Loss: 1.8651 (3.319) Data (t): 0.001 Batch (t): 0.900, 569.403/s LR: 0.000005 Logit Scale: 99.996 - V4 -2024-11-27,00:05:37 | INFO | Train Epoch: 0 [ 154112/10637090 (1%)] Loss: 1.6311 (2.897) Data (t): 0.001 Batch (t): 0.915, 569.471/s LR: 0.000005 Logit Scale: 99.994 - V4 -2024-11-27,00:07:12 | INFO | Train Epoch: 0 [ 205312/10637090 (2%)] Loss: 1.5378 (2.625) Data (t): 0.001 Batch (t): 0.944, 567.919/s LR: 0.000005 Logit Scale: 99.992 - V4 -2024-11-27,00:08:42 | INFO | Train Epoch: 0 [ 256512/10637090 (2%)] Loss: 1.4296 (2.426) Data (t): 0.001 Batch (t): 0.899, 567.674/s LR: 0.000005 Logit Scale: 99.991 - V4 -2024-11-27,00:10:12 | INFO | Train Epoch: 0 [ 307712/10637090 (3%)] Loss: 1.4987 (2.293) Data (t): 0.001 Batch (t): 0.899, 572.843/s LR: 0.000005 Logit Scale: 99.987 - V4 -2024-11-27,00:11:42 | INFO | Train Epoch: 0 [ 358912/10637090 (3%)] Loss: 1.4410 (2.187) Data (t): 0.001 Batch (t): 0.900, 568.004/s LR: 0.000005 Logit Scale: 99.984 - V4 -2024-11-27,00:13:12 | INFO | Train Epoch: 0 [ 410112/10637090 (4%)] Loss: 1.4968 (2.110) Data (t): 0.001 Batch (t): 0.899, 570.547/s LR: 0.000005 Logit Scale: 99.983 - V4 -2024-11-27,00:14:49 | INFO | Train Epoch: 0 [ 461312/10637090 (4%)] Loss: 1.6677 (2.066) Data (t): 0.001 Batch (t): 0.972, 570.981/s LR: 0.000005 Logit Scale: 99.978 - V4 -2024-11-27,00:16:19 | INFO | Train Epoch: 0 [ 512512/10637090 (5%)] Loss: 1.3984 (2.005) Data (t): 0.001 Batch (t): 0.900, 569.236/s LR: 0.000005 Logit Scale: 99.977 - V4 -2024-11-27,00:17:49 | INFO | Train Epoch: 0 [ 563712/10637090 (5%)] Loss: 1.4217 (1.957) Data (t): 0.001 Batch (t): 0.899, 568.284/s LR: 0.000005 Logit Scale: 99.972 - V4 -2024-11-27,00:19:18 | INFO | Train Epoch: 0 [ 614912/10637090 (6%)] Loss: 1.3985 (1.914) Data (t): 0.001 Batch (t): 0.898, 569.348/s LR: 0.000005 Logit Scale: 99.970 - V4 -2024-11-27,00:20:48 | INFO | Train Epoch: 0 [ 666112/10637090 (6%)] Loss: 1.3929 (1.876) Data (t): 0.001 Batch (t): 0.899, 569.799/s LR: 0.000005 Logit Scale: 99.967 - V4 -2024-11-27,00:22:25 | INFO | Train Epoch: 0 [ 717312/10637090 (7%)] Loss: 1.2314 (1.833) Data (t): 0.001 Batch (t): 0.962, 570.341/s LR: 0.000005 Logit Scale: 99.965 - V4 -2024-11-27,00:23:54 | INFO | Train Epoch: 0 [ 768512/10637090 (7%)] Loss: 1.4314 (1.808) Data (t): 0.001 Batch (t): 0.899, 569.148/s LR: 0.000005 Logit Scale: 99.961 - V4 -2024-11-27,00:25:24 | INFO | Train Epoch: 0 [ 819712/10637090 (8%)] Loss: 1.4184 (1.785) Data (t): 0.001 Batch (t): 0.899, 569.275/s LR: 0.000005 Logit Scale: 99.959 - V4 -2024-11-27,00:26:54 | INFO | Train Epoch: 0 [ 870912/10637090 (8%)] Loss: 1.4606 (1.767) Data (t): 0.001 Batch (t): 0.899, 567.158/s LR: 0.000005 Logit Scale: 99.955 - V4 -2024-11-27,00:28:24 | INFO | Train Epoch: 0 [ 922112/10637090 (9%)] Loss: 1.4578 (1.751) Data (t): 0.001 Batch (t): 0.899, 566.798/s LR: 0.000005 Logit Scale: 99.953 - V4 -2024-11-27,00:30:00 | INFO | Train Epoch: 0 [ 973312/10637090 (9%)] Loss: 1.3601 (1.732) Data (t): 0.001 Batch (t): 0.956, 571.441/s LR: 0.000005 Logit Scale: 99.948 - V4 -2024-11-27,00:31:30 | INFO | Train Epoch: 0 [ 1024512/10637090 (10%)] Loss: 1.3931 (1.715) Data (t): 0.001 Batch (t): 0.898, 569.813/s LR: 0.000005 Logit Scale: 99.944 - V4 -2024-11-27,00:33:00 | INFO | Train Epoch: 0 [ 1075712/10637090 (10%)] Loss: 1.3680 (1.700) Data (t): 0.001 Batch (t): 0.900, 566.470/s LR: 0.000005 Logit Scale: 99.943 - V4 -2024-11-27,00:34:30 | INFO | Train Epoch: 0 [ 1126912/10637090 (11%)] Loss: 1.4400 (1.688) Data (t): 0.001 Batch (t): 0.899, 569.038/s LR: 0.000005 Logit Scale: 99.939 - V4 -2024-11-27,00:36:00 | INFO | Train Epoch: 0 [ 1178112/10637090 (11%)] Loss: 1.1873 (1.667) Data (t): 0.001 Batch (t): 0.899, 570.775/s LR: 0.000005 Logit Scale: 99.936 - V4 -2024-11-27,00:37:34 | INFO | Train Epoch: 0 [ 1229312/10637090 (12%)] Loss: 1.1434 (1.647) Data (t): 0.001 Batch (t): 0.946, 326.849/s LR: 0.000005 Logit Scale: 99.932 - V4 -2024-11-27,00:39:06 | INFO | Train Epoch: 0 [ 1280512/10637090 (12%)] Loss: 1.1847 (1.629) Data (t): 0.001 Batch (t): 0.920, 569.612/s LR: 0.000005 Logit Scale: 99.930 - V4 -2024-11-27,00:40:36 | INFO | Train Epoch: 0 [ 1331712/10637090 (13%)] Loss: 1.3127 (1.617) Data (t): 0.001 Batch (t): 0.900, 567.596/s LR: 0.000005 Logit Scale: 99.928 - V4 -2024-11-27,00:42:06 | INFO | Train Epoch: 0 [ 1382912/10637090 (13%)] Loss: 1.5171 (1.613) Data (t): 0.001 Batch (t): 0.900, 566.739/s LR: 0.000005 Logit Scale: 99.926 - V4 -2024-11-27,00:43:36 | INFO | Train Epoch: 0 [ 1434112/10637090 (13%)] Loss: 1.2625 (1.601) Data (t): 0.001 Batch (t): 0.900, 567.925/s LR: 0.000005 Logit Scale: 99.922 - V4 -2024-11-27,00:45:07 | INFO | Train Epoch: 0 [ 1485312/10637090 (14%)] Loss: 1.2965 (1.591) Data (t): 0.001 Batch (t): 0.910, 567.549/s LR: 0.000005 Logit Scale: 99.920 - V4 -2024-11-27,00:46:42 | INFO | Train Epoch: 0 [ 1536512/10637090 (14%)] Loss: 1.3683 (1.584) Data (t): 0.001 Batch (t): 0.950, 569.403/s LR: 0.000005 Logit Scale: 99.915 - V4 -2024-11-27,00:48:12 | INFO | Train Epoch: 0 [ 1587712/10637090 (15%)] Loss: 1.3209 (1.576) Data (t): 0.001 Batch (t): 0.898, 571.054/s LR: 0.000005 Logit Scale: 99.913 - V4 -2024-11-27,00:49:42 | INFO | Train Epoch: 0 [ 1638912/10637090 (15%)] Loss: 1.2952 (1.567) Data (t): 0.001 Batch (t): 0.897, 570.095/s LR: 0.000005 Logit Scale: 99.910 - V4 -2024-11-27,00:51:11 | INFO | Train Epoch: 0 [ 1690112/10637090 (16%)] Loss: 1.1427 (1.555) Data (t): 0.001 Batch (t): 0.897, 569.632/s LR: 0.000005 Logit Scale: 99.905 - V4 -2024-11-27,00:52:41 | INFO | Train Epoch: 0 [ 1741312/10637090 (16%)] Loss: 1.3267 (1.548) Data (t): 0.001 Batch (t): 0.898, 570.207/s LR: 0.000005 Logit Scale: 99.903 - V4 -2024-11-27,00:54:18 | INFO | Train Epoch: 0 [ 1792512/10637090 (17%)] Loss: 1.1937 (1.538) Data (t): 0.001 Batch (t): 0.965, 570.586/s LR: 0.000005 Logit Scale: 99.901 - V4 -2024-11-27,00:55:48 | INFO | Train Epoch: 0 [ 1843712/10637090 (17%)] Loss: 1.2775 (1.531) Data (t): 0.001 Batch (t): 0.899, 568.347/s LR: 0.000005 Logit Scale: 99.897 - V4 -2024-11-27,00:57:17 | INFO | Train Epoch: 0 [ 1894912/10637090 (18%)] Loss: 1.1753 (1.522) Data (t): 0.001 Batch (t): 0.899, 569.093/s LR: 0.000005 Logit Scale: 99.893 - V4 -2024-11-27,00:58:47 | INFO | Train Epoch: 0 [ 1946112/10637090 (18%)] Loss: 1.2124 (1.514) Data (t): 0.001 Batch (t): 0.898, 568.568/s LR: 0.000005 Logit Scale: 99.893 - V4 -2024-11-27,01:00:17 | INFO | Train Epoch: 0 [ 1997312/10637090 (19%)] Loss: 1.2806 (1.508) Data (t): 0.001 Batch (t): 0.898, 570.066/s LR: 0.000005 Logit Scale: 99.890 - V4 -2024-11-27,01:01:54 | INFO | Train Epoch: 0 [ 2048512/10637090 (19%)] Loss: 1.3294 (1.504) Data (t): 0.001 Batch (t): 0.966, 571.047/s LR: 0.000005 Logit Scale: 99.886 - V4 -2024-11-27,01:03:23 | INFO | Train Epoch: 0 [ 2099712/10637090 (20%)] Loss: 1.3688 (1.501) Data (t): 0.001 Batch (t): 0.897, 571.014/s LR: 0.000005 Logit Scale: 99.883 - V4 -2024-11-27,01:04:53 | INFO | Train Epoch: 0 [ 2150912/10637090 (20%)] Loss: 1.2393 (1.495) Data (t): 0.001 Batch (t): 0.898, 570.310/s LR: 0.000005 Logit Scale: 99.881 - V4 -2024-11-27,01:06:23 | INFO | Train Epoch: 0 [ 2202112/10637090 (21%)] Loss: 1.2518 (1.489) Data (t): 0.001 Batch (t): 0.898, 572.764/s LR: 0.000005 Logit Scale: 99.877 - V4 -2024-11-27,01:07:53 | INFO | Train Epoch: 0 [ 2253312/10637090 (21%)] Loss: 1.3710 (1.486) Data (t): 0.001 Batch (t): 0.898, 571.602/s LR: 0.000005 Logit Scale: 99.874 - V4 -2024-11-27,01:09:27 | INFO | Train Epoch: 0 [ 2304512/10637090 (22%)] Loss: 1.3049 (1.482) Data (t): 0.001 Batch (t): 0.945, 572.675/s LR: 0.000005 Logit Scale: 99.870 - V4 -2024-11-27,01:10:59 | INFO | Train Epoch: 0 [ 2355712/10637090 (22%)] Loss: 1.3114 (1.479) Data (t): 0.001 Batch (t): 0.919, 569.923/s LR: 0.000005 Logit Scale: 99.868 - V4 -2024-11-27,01:12:29 | INFO | Train Epoch: 0 [ 2406912/10637090 (23%)] Loss: 1.3374 (1.476) Data (t): 0.001 Batch (t): 0.898, 568.928/s LR: 0.000005 Logit Scale: 99.865 - V4 -2024-11-27,01:13:59 | INFO | Train Epoch: 0 [ 2458112/10637090 (23%)] Loss: 1.4219 (1.475) Data (t): 0.001 Batch (t): 0.897, 572.496/s LR: 0.000005 Logit Scale: 99.861 - V4 -2024-11-27,01:15:28 | INFO | Train Epoch: 0 [ 2509312/10637090 (24%)] Loss: 1.2642 (1.471) Data (t): 0.001 Batch (t): 0.896, 572.387/s LR: 0.000005 Logit Scale: 99.863 - V4 -2024-11-27,01:17:02 | INFO | Train Epoch: 0 [ 2560512/10637090 (24%)] Loss: 1.2675 (1.467) Data (t): 0.001 Batch (t): 0.936, 571.857/s LR: 0.000005 Logit Scale: 99.860 - V4 -2024-11-27,01:18:34 | INFO | Train Epoch: 0 [ 2611712/10637090 (25%)] Loss: 1.1737 (1.461) Data (t): 0.001 Batch (t): 0.923, 570.747/s LR: 0.000005 Logit Scale: 99.857 - V4 -2024-11-27,01:20:04 | INFO | Train Epoch: 0 [ 2662912/10637090 (25%)] Loss: 1.1781 (1.456) Data (t): 0.001 Batch (t): 0.897, 571.081/s LR: 0.000005 Logit Scale: 99.856 - V4 -2024-11-27,01:21:34 | INFO | Train Epoch: 0 [ 2714112/10637090 (26%)] Loss: 1.1610 (1.450) Data (t): 0.001 Batch (t): 0.897, 571.302/s LR: 0.000005 Logit Scale: 99.855 - V4 -2024-11-27,01:23:03 | INFO | Train Epoch: 0 [ 2765312/10637090 (26%)] Loss: 1.2686 (1.447) Data (t): 0.001 Batch (t): 0.897, 569.776/s LR: 0.000005 Logit Scale: 99.853 - V4 -2024-11-27,01:24:33 | INFO | Train Epoch: 0 [ 2816512/10637090 (26%)] Loss: 1.1229 (1.441) Data (t): 0.001 Batch (t): 0.897, 570.995/s LR: 0.000005 Logit Scale: 99.849 - V4 -2024-11-27,01:26:09 | INFO | Train Epoch: 0 [ 2867712/10637090 (27%)] Loss: 1.2637 (1.438) Data (t): 0.001 Batch (t): 0.963, 571.722/s LR: 0.000005 Logit Scale: 99.846 - V4 -2024-11-27,01:27:39 | INFO | Train Epoch: 0 [ 2918912/10637090 (27%)] Loss: 1.2818 (1.435) Data (t): 0.001 Batch (t): 0.897, 571.497/s LR: 0.000005 Logit Scale: 99.848 - V4 -2024-11-27,01:29:09 | INFO | Train Epoch: 0 [ 2970112/10637090 (28%)] Loss: 1.1737 (1.431) Data (t): 0.001 Batch (t): 0.897, 571.149/s LR: 0.000005 Logit Scale: 99.843 - V4 -2024-11-27,01:30:38 | INFO | Train Epoch: 0 [ 3021312/10637090 (28%)] Loss: 1.1583 (1.426) Data (t): 0.001 Batch (t): 0.897, 571.518/s LR: 0.000005 Logit Scale: 99.843 - V4 -2024-11-27,01:32:08 | INFO | Train Epoch: 0 [ 3072512/10637090 (29%)] Loss: 1.0435 (1.420) Data (t): 0.001 Batch (t): 0.896, 572.025/s LR: 0.000005 Logit Scale: 99.839 - V4 -2024-11-27,01:33:44 | INFO | Train Epoch: 0 [ 3123712/10637090 (29%)] Loss: 1.2389 (1.417) Data (t): 0.001 Batch (t): 0.958, 571.597/s LR: 0.000005 Logit Scale: 99.836 - V4 -2024-11-27,01:35:13 | INFO | Train Epoch: 0 [ 3174912/10637090 (30%)] Loss: 1.2484 (1.414) Data (t): 0.001 Batch (t): 0.896, 569.564/s LR: 0.000005 Logit Scale: 99.836 - V4 -2024-11-27,01:36:43 | INFO | Train Epoch: 0 [ 3226112/10637090 (30%)] Loss: 1.1787 (1.411) Data (t): 0.001 Batch (t): 0.896, 570.934/s LR: 0.000005 Logit Scale: 99.835 - V4 -2024-11-27,01:38:12 | INFO | Train Epoch: 0 [ 3277312/10637090 (31%)] Loss: 1.1424 (1.407) Data (t): 0.001 Batch (t): 0.895, 572.160/s LR: 0.000005 Logit Scale: 99.833 - V4 -2024-11-27,01:39:42 | INFO | Train Epoch: 0 [ 3328512/10637090 (31%)] Loss: 1.2771 (1.405) Data (t): 0.001 Batch (t): 0.896, 570.350/s LR: 0.000005 Logit Scale: 99.833 - V4 -2024-11-27,01:41:16 | INFO | Train Epoch: 0 [ 3379712/10637090 (32%)] Loss: 1.2625 (1.403) Data (t): 0.001 Batch (t): 0.936, 568.358/s LR: 0.000005 Logit Scale: 99.832 - V4 -2024-11-27,01:42:47 | INFO | Train Epoch: 0 [ 3430912/10637090 (32%)] Loss: 1.1774 (1.399) Data (t): 0.001 Batch (t): 0.916, 575.051/s LR: 0.000005 Logit Scale: 99.831 - V4 -2024-11-27,01:44:17 | INFO | Train Epoch: 0 [ 3482112/10637090 (33%)] Loss: 1.2596 (1.397) Data (t): 0.001 Batch (t): 0.897, 569.664/s LR: 0.000005 Logit Scale: 99.826 - V4 -2024-11-27,01:45:47 | INFO | Train Epoch: 0 [ 3533312/10637090 (33%)] Loss: 1.1953 (1.394) Data (t): 0.001 Batch (t): 0.897, 570.688/s LR: 0.000005 Logit Scale: 99.827 - V4 -2024-11-27,01:47:16 | INFO | Train Epoch: 0 [ 3584512/10637090 (34%)] Loss: 1.1942 (1.392) Data (t): 0.001 Batch (t): 0.896, 570.153/s LR: 0.000005 Logit Scale: 99.825 - V4 -2024-11-27,01:48:51 | INFO | Train Epoch: 0 [ 3635712/10637090 (34%)] Loss: 1.2278 (1.389) Data (t): 0.001 Batch (t): 0.943, 572.123/s LR: 0.000005 Logit Scale: 99.824 - V4 -2024-11-27,01:50:22 | INFO | Train Epoch: 0 [ 3686912/10637090 (35%)] Loss: 1.3798 (1.389) Data (t): 0.001 Batch (t): 0.916, 568.780/s LR: 0.000005 Logit Scale: 99.822 - V4 -2024-11-27,01:51:52 | INFO | Train Epoch: 0 [ 3738112/10637090 (35%)] Loss: 1.1573 (1.386) Data (t): 0.001 Batch (t): 0.897, 570.548/s LR: 0.000005 Logit Scale: 99.822 - V4 -2024-11-27,01:53:21 | INFO | Train Epoch: 0 [ 3789312/10637090 (36%)] Loss: 1.0215 (1.381) Data (t): 0.001 Batch (t): 0.896, 572.590/s LR: 0.000005 Logit Scale: 99.818 - V4 -2024-11-27,01:54:51 | INFO | Train Epoch: 0 [ 3840512/10637090 (36%)] Loss: 1.2224 (1.379) Data (t): 0.001 Batch (t): 0.896, 569.029/s LR: 0.000005 Logit Scale: 99.817 - V4 -2024-11-27,01:56:24 | INFO | Train Epoch: 0 [ 3891712/10637090 (37%)] Loss: 1.1264 (1.376) Data (t): 0.001 Batch (t): 0.926, 324.695/s LR: 0.000005 Logit Scale: 99.818 - V4 -2024-11-27,01:57:57 | INFO | Train Epoch: 0 [ 3942912/10637090 (37%)] Loss: 1.1852 (1.373) Data (t): 0.001 Batch (t): 0.934, 569.099/s LR: 0.000005 Logit Scale: 99.819 - V4 -2024-11-27,01:59:27 | INFO | Train Epoch: 0 [ 3994112/10637090 (38%)] Loss: 1.1099 (1.370) Data (t): 0.001 Batch (t): 0.896, 573.960/s LR: 0.000005 Logit Scale: 99.817 - V4 -2024-11-27,02:00:56 | INFO | Train Epoch: 0 [ 4045312/10637090 (38%)] Loss: 1.1226 (1.367) Data (t): 0.001 Batch (t): 0.897, 573.014/s LR: 0.000005 Logit Scale: 99.818 - V4 -2024-11-27,02:02:26 | INFO | Train Epoch: 0 [ 4096512/10637090 (39%)] Loss: 1.1286 (1.364) Data (t): 0.001 Batch (t): 0.898, 570.082/s LR: 0.000005 Logit Scale: 99.815 - V4 -2024-11-27,02:03:56 | INFO | Train Epoch: 0 [ 4147712/10637090 (39%)] Loss: 1.1437 (1.361) Data (t): 0.001 Batch (t): 0.896, 571.589/s LR: 0.000005 Logit Scale: 99.816 - V4 -2024-11-27,02:05:32 | INFO | Train Epoch: 0 [ 4198912/10637090 (39%)] Loss: 1.1229 (1.358) Data (t): 0.001 Batch (t): 0.961, 571.753/s LR: 0.000005 Logit Scale: 99.813 - V4 -2024-11-27,02:07:02 | INFO | Train Epoch: 0 [ 4250112/10637090 (40%)] Loss: 1.2819 (1.357) Data (t): 0.001 Batch (t): 0.896, 569.461/s LR: 0.000005 Logit Scale: 99.811 - V4 -2024-11-27,02:08:31 | INFO | Train Epoch: 0 [ 4301312/10637090 (40%)] Loss: 1.3135 (1.357) Data (t): 0.001 Batch (t): 0.896, 570.928/s LR: 0.000005 Logit Scale: 99.812 - V4 -2024-11-27,02:10:01 | INFO | Train Epoch: 0 [ 4352512/10637090 (41%)] Loss: 1.1356 (1.354) Data (t): 0.001 Batch (t): 0.895, 571.161/s LR: 0.000005 Logit Scale: 99.809 - V4 -2024-11-27,02:11:30 | INFO | Train Epoch: 0 [ 4403712/10637090 (41%)] Loss: 1.1187 (1.352) Data (t): 0.001 Batch (t): 0.895, 570.798/s LR: 0.000004 Logit Scale: 99.809 - V4 -2024-11-27,02:13:05 | INFO | Train Epoch: 0 [ 4454912/10637090 (42%)] Loss: 1.1785 (1.350) Data (t): 0.001 Batch (t): 0.943, 572.874/s LR: 0.000004 Logit Scale: 99.808 - V4 -2024-11-27,02:14:36 | INFO | Train Epoch: 0 [ 4506112/10637090 (42%)] Loss: 1.1717 (1.348) Data (t): 0.001 Batch (t): 0.917, 571.405/s LR: 0.000004 Logit Scale: 99.806 - V4 -2024-11-27,02:16:06 | INFO | Train Epoch: 0 [ 4557312/10637090 (43%)] Loss: 1.2548 (1.347) Data (t): 0.001 Batch (t): 0.895, 569.395/s LR: 0.000004 Logit Scale: 99.806 - V4 -2024-11-27,02:17:36 | INFO | Train Epoch: 0 [ 4608512/10637090 (43%)] Loss: 1.0788 (1.344) Data (t): 0.001 Batch (t): 0.896, 571.157/s LR: 0.000004 Logit Scale: 99.807 - V4 -2024-11-27,02:19:05 | INFO | Train Epoch: 0 [ 4659712/10637090 (44%)] Loss: 1.2446 (1.343) Data (t): 0.001 Batch (t): 0.896, 569.782/s LR: 0.000004 Logit Scale: 99.806 - V4 -2024-11-27,02:20:40 | INFO | Train Epoch: 0 [ 4710912/10637090 (44%)] Loss: 1.2120 (1.341) Data (t): 0.001 Batch (t): 0.945, 571.213/s LR: 0.000004 Logit Scale: 99.805 - V4 -2024-11-27,02:22:11 | INFO | Train Epoch: 0 [ 4762112/10637090 (45%)] Loss: 1.3526 (1.341) Data (t): 0.001 Batch (t): 0.917, 573.227/s LR: 0.000004 Logit Scale: 99.804 - V4 -2024-11-27,02:23:41 | INFO | Train Epoch: 0 [ 4813312/10637090 (45%)] Loss: 1.1652 (1.340) Data (t): 0.001 Batch (t): 0.896, 573.041/s LR: 0.000004 Logit Scale: 99.804 - V4 -2024-11-27,02:25:11 | INFO | Train Epoch: 0 [ 4864512/10637090 (46%)] Loss: 1.2001 (1.338) Data (t): 0.001 Batch (t): 0.897, 571.774/s LR: 0.000004 Logit Scale: 99.801 - V4 -2024-11-27,02:26:40 | INFO | Train Epoch: 0 [ 4915712/10637090 (46%)] Loss: 1.3084 (1.338) Data (t): 0.001 Batch (t): 0.896, 571.180/s LR: 0.000004 Logit Scale: 99.802 - V4 -2024-11-27,02:28:13 | INFO | Train Epoch: 0 [ 4966912/10637090 (47%)] Loss: 1.1183 (1.336) Data (t): 0.001 Batch (t): 0.934, 571.497/s LR: 0.000004 Logit Scale: 99.802 - V4 -2024-11-27,02:29:46 | INFO | Train Epoch: 0 [ 5018112/10637090 (47%)] Loss: 1.1509 (1.334) Data (t): 0.001 Batch (t): 0.927, 571.698/s LR: 0.000004 Logit Scale: 99.802 - V4 -2024-11-27,02:31:16 | INFO | Train Epoch: 0 [ 5069312/10637090 (48%)] Loss: 1.0520 (1.331) Data (t): 0.001 Batch (t): 0.897, 571.711/s LR: 0.000004 Logit Scale: 99.802 - V4 -2024-11-27,02:32:46 | INFO | Train Epoch: 0 [ 5120512/10637090 (48%)] Loss: 1.2611 (1.330) Data (t): 0.001 Batch (t): 0.896, 576.107/s LR: 0.000004 Logit Scale: 99.802 - V4 -2024-11-27,02:34:15 | INFO | Train Epoch: 0 [ 5171712/10637090 (49%)] Loss: 1.2434 (1.329) Data (t): 0.001 Batch (t): 0.896, 570.625/s LR: 0.000004 Logit Scale: 99.801 - V4 -2024-11-27,02:35:47 | INFO | Train Epoch: 0 [ 5222912/10637090 (49%)] Loss: 1.1106 (1.327) Data (t): 0.001 Batch (t): 0.920, 324.289/s LR: 0.000004 Logit Scale: 99.804 - V4 -2024-11-27,02:37:21 | INFO | Train Epoch: 0 [ 5274112/10637090 (50%)] Loss: 1.1516 (1.325) Data (t): 0.001 Batch (t): 0.941, 571.862/s LR: 0.000004 Logit Scale: 99.803 - V4 -2024-11-27,02:38:51 | INFO | Train Epoch: 0 [ 5325312/10637090 (50%)] Loss: 1.1647 (1.324) Data (t): 0.001 Batch (t): 0.896, 572.345/s LR: 0.000004 Logit Scale: 99.804 - V4 -2024-11-27,02:40:20 | INFO | Train Epoch: 0 [ 5376512/10637090 (51%)] Loss: 1.1783 (1.323) Data (t): 0.001 Batch (t): 0.896, 571.826/s LR: 0.000004 Logit Scale: 99.803 - V4 -2024-11-27,02:41:50 | INFO | Train Epoch: 0 [ 5427712/10637090 (51%)] Loss: 1.1427 (1.321) Data (t): 0.001 Batch (t): 0.897, 572.677/s LR: 0.000004 Logit Scale: 99.801 - V4 -2024-11-27,02:43:21 | INFO | Train Epoch: 0 [ 5478912/10637090 (52%)] Loss: 1.1340 (1.319) Data (t): 0.001 Batch (t): 0.906, 574.483/s LR: 0.000004 Logit Scale: 99.802 - V4 -2024-11-27,02:44:54 | INFO | Train Epoch: 0 [ 5530112/10637090 (52%)] Loss: 1.2224 (1.318) Data (t): 0.001 Batch (t): 0.934, 570.175/s LR: 0.000004 Logit Scale: 99.803 - V4 -2024-11-27,02:46:26 | INFO | Train Epoch: 0 [ 5581312/10637090 (52%)] Loss: 1.2584 (1.318) Data (t): 0.001 Batch (t): 0.917, 570.760/s LR: 0.000004 Logit Scale: 99.802 - V4 -2024-11-27,02:47:55 | INFO | Train Epoch: 0 [ 5632512/10637090 (53%)] Loss: 1.1864 (1.317) Data (t): 0.001 Batch (t): 0.896, 569.898/s LR: 0.000004 Logit Scale: 99.803 - V4 -2024-11-27,02:49:25 | INFO | Train Epoch: 0 [ 5683712/10637090 (53%)] Loss: 1.1288 (1.315) Data (t): 0.001 Batch (t): 0.896, 570.907/s LR: 0.000004 Logit Scale: 99.803 - V4 -2024-11-27,02:50:55 | INFO | Train Epoch: 0 [ 5734912/10637090 (54%)] Loss: 1.1840 (1.314) Data (t): 0.001 Batch (t): 0.897, 573.630/s LR: 0.000004 Logit Scale: 99.803 - V4 -2024-11-27,02:52:29 | INFO | Train Epoch: 0 [ 5786112/10637090 (54%)] Loss: 1.1072 (1.312) Data (t): 0.001 Batch (t): 0.944, 573.707/s LR: 0.000004 Logit Scale: 99.802 - V4 -2024-11-27,02:54:01 | INFO | Train Epoch: 0 [ 5837312/10637090 (55%)] Loss: 1.1986 (1.311) Data (t): 0.001 Batch (t): 0.917, 572.057/s LR: 0.000004 Logit Scale: 99.803 - V4 -2024-11-27,02:55:30 | INFO | Train Epoch: 0 [ 5888512/10637090 (55%)] Loss: 1.1697 (1.310) Data (t): 0.001 Batch (t): 0.896, 572.526/s LR: 0.000004 Logit Scale: 99.809 - V4 -2024-11-27,02:57:00 | INFO | Train Epoch: 0 [ 5939712/10637090 (56%)] Loss: 1.1323 (1.308) Data (t): 0.001 Batch (t): 0.896, 571.471/s LR: 0.000004 Logit Scale: 99.809 - V4 -2024-11-27,02:58:30 | INFO | Train Epoch: 0 [ 5990912/10637090 (56%)] Loss: 1.1166 (1.307) Data (t): 0.001 Batch (t): 0.897, 572.541/s LR: 0.000004 Logit Scale: 99.808 - V4 -2024-11-27,03:00:03 | INFO | Train Epoch: 0 [ 6042112/10637090 (57%)] Loss: 1.1826 (1.306) Data (t): 0.001 Batch (t): 0.935, 570.517/s LR: 0.000004 Logit Scale: 99.810 - V4 -2024-11-27,03:01:36 | INFO | Train Epoch: 0 [ 6093312/10637090 (57%)] Loss: 1.1965 (1.305) Data (t): 0.001 Batch (t): 0.927, 570.458/s LR: 0.000004 Logit Scale: 99.813 - V4 -2024-11-27,03:03:06 | INFO | Train Epoch: 0 [ 6144512/10637090 (58%)] Loss: 1.0597 (1.303) Data (t): 0.001 Batch (t): 0.897, 569.976/s LR: 0.000004 Logit Scale: 99.814 - V4 -2024-11-27,03:04:35 | INFO | Train Epoch: 0 [ 6195712/10637090 (58%)] Loss: 1.2225 (1.302) Data (t): 0.001 Batch (t): 0.895, 572.179/s LR: 0.000004 Logit Scale: 99.816 - V4 -2024-11-27,03:06:05 | INFO | Train Epoch: 0 [ 6246912/10637090 (59%)] Loss: 1.0985 (1.300) Data (t): 0.001 Batch (t): 0.896, 569.476/s LR: 0.000004 Logit Scale: 99.816 - V4 -2024-11-27,03:07:37 | INFO | Train Epoch: 0 [ 6298112/10637090 (59%)] Loss: 1.0678 (1.298) Data (t): 0.001 Batch (t): 0.927, 571.894/s LR: 0.000004 Logit Scale: 99.813 - V4 -2024-11-27,03:09:11 | INFO | Train Epoch: 0 [ 6349312/10637090 (60%)] Loss: 1.1614 (1.297) Data (t): 0.001 Batch (t): 0.935, 571.609/s LR: 0.000004 Logit Scale: 99.814 - V4 -2024-11-27,03:10:40 | INFO | Train Epoch: 0 [ 6400512/10637090 (60%)] Loss: 1.0997 (1.296) Data (t): 0.001 Batch (t): 0.896, 573.898/s LR: 0.000004 Logit Scale: 99.814 - V4 -2024-11-27,03:12:10 | INFO | Train Epoch: 0 [ 6451712/10637090 (61%)] Loss: 1.2194 (1.295) Data (t): 0.001 Batch (t): 0.896, 568.655/s LR: 0.000004 Logit Scale: 99.815 - V4 -2024-11-27,03:13:40 | INFO | Train Epoch: 0 [ 6502912/10637090 (61%)] Loss: 1.0856 (1.293) Data (t): 0.001 Batch (t): 0.895, 572.735/s LR: 0.000004 Logit Scale: 99.817 - V4 -2024-11-27,03:15:11 | INFO | Train Epoch: 0 [ 6554112/10637090 (62%)] Loss: 1.1479 (1.292) Data (t): 0.001 Batch (t): 0.914, 318.806/s LR: 0.000004 Logit Scale: 99.817 - V4 -2024-11-27,03:16:43 | INFO | Train Epoch: 0 [ 6605312/10637090 (62%)] Loss: 1.0956 (1.291) Data (t): 0.001 Batch (t): 0.920, 573.559/s LR: 0.000004 Logit Scale: 99.818 - V4 -2024-11-27,03:18:15 | INFO | Train Epoch: 0 [ 6656512/10637090 (63%)] Loss: 1.2245 (1.290) Data (t): 0.001 Batch (t): 0.917, 572.504/s LR: 0.000004 Logit Scale: 99.817 - V4 -2024-11-27,03:19:44 | INFO | Train Epoch: 0 [ 6707712/10637090 (63%)] Loss: 1.2737 (1.290) Data (t): 0.001 Batch (t): 0.896, 571.761/s LR: 0.000004 Logit Scale: 99.817 - V4 -2024-11-27,03:21:14 | INFO | Train Epoch: 0 [ 6758912/10637090 (64%)] Loss: 1.0758 (1.289) Data (t): 0.001 Batch (t): 0.895, 573.015/s LR: 0.000004 Logit Scale: 99.817 - V4 -2024-11-27,03:22:43 | INFO | Train Epoch: 0 [ 6810112/10637090 (64%)] Loss: 1.1316 (1.287) Data (t): 0.001 Batch (t): 0.897, 570.041/s LR: 0.000004 Logit Scale: 99.819 - V4 -2024-11-27,03:24:17 | INFO | Train Epoch: 0 [ 6861312/10637090 (65%)] Loss: 1.1231 (1.286) Data (t): 0.001 Batch (t): 0.938, 572.175/s LR: 0.000004 Logit Scale: 99.822 - V4 -2024-11-27,03:25:49 | INFO | Train Epoch: 0 [ 6912512/10637090 (65%)] Loss: 1.0891 (1.285) Data (t): 0.001 Batch (t): 0.917, 572.771/s LR: 0.000004 Logit Scale: 99.823 - V4 -2024-11-27,03:27:18 | INFO | Train Epoch: 0 [ 6963712/10637090 (65%)] Loss: 1.1356 (1.284) Data (t): 0.001 Batch (t): 0.895, 571.532/s LR: 0.000004 Logit Scale: 99.823 - V4 -2024-11-27,03:28:48 | INFO | Train Epoch: 0 [ 7014912/10637090 (66%)] Loss: 1.3145 (1.284) Data (t): 0.001 Batch (t): 0.896, 572.996/s LR: 0.000004 Logit Scale: 99.825 - V4 -2024-11-27,03:30:17 | INFO | Train Epoch: 0 [ 7066112/10637090 (66%)] Loss: 1.0851 (1.282) Data (t): 0.001 Batch (t): 0.896, 572.070/s LR: 0.000004 Logit Scale: 99.826 - V4 -2024-11-27,03:31:49 | INFO | Train Epoch: 0 [ 7117312/10637090 (67%)] Loss: 1.1289 (1.281) Data (t): 0.001 Batch (t): 0.920, 571.412/s LR: 0.000004 Logit Scale: 99.830 - V4 -2024-11-27,03:33:22 | INFO | Train Epoch: 0 [ 7168512/10637090 (67%)] Loss: 1.1010 (1.280) Data (t): 0.001 Batch (t): 0.927, 570.866/s LR: 0.000004 Logit Scale: 99.830 - V4 -2024-11-27,03:34:52 | INFO | Train Epoch: 0 [ 7219712/10637090 (68%)] Loss: 1.0506 (1.278) Data (t): 0.001 Batch (t): 0.896, 571.527/s LR: 0.000004 Logit Scale: 99.832 - V4 -2024-11-27,03:36:21 | INFO | Train Epoch: 0 [ 7270912/10637090 (68%)] Loss: 1.2806 (1.278) Data (t): 0.001 Batch (t): 0.895, 572.979/s LR: 0.000004 Logit Scale: 99.833 - V4 -2024-11-27,03:37:51 | INFO | Train Epoch: 0 [ 7322112/10637090 (69%)] Loss: 0.96458 (1.276) Data (t): 0.001 Batch (t): 0.895, 570.327/s LR: 0.000004 Logit Scale: 99.834 - V4 -2024-11-27,03:39:24 | INFO | Train Epoch: 0 [ 7373312/10637090 (69%)] Loss: 1.2042 (1.276) Data (t): 0.001 Batch (t): 0.928, 571.858/s LR: 0.000004 Logit Scale: 99.834 - V4 -2024-11-27,03:40:55 | INFO | Train Epoch: 0 [ 7424512/10637090 (70%)] Loss: 1.0352 (1.274) Data (t): 0.001 Batch (t): 0.917, 570.552/s LR: 0.000004 Logit Scale: 99.833 - V4 -2024-11-27,03:42:26 | INFO | Train Epoch: 0 [ 7475712/10637090 (70%)] Loss: 1.0972 (1.273) Data (t): 0.001 Batch (t): 0.907, 572.454/s LR: 0.000004 Logit Scale: 99.835 - V4 -2024-11-27,03:43:55 | INFO | Train Epoch: 0 [ 7526912/10637090 (71%)] Loss: 1.0199 (1.271) Data (t): 0.001 Batch (t): 0.894, 572.540/s LR: 0.000004 Logit Scale: 99.838 - V4 -2024-11-27,03:45:25 | INFO | Train Epoch: 0 [ 7578112/10637090 (71%)] Loss: 1.1705 (1.271) Data (t): 0.001 Batch (t): 0.896, 573.302/s LR: 0.000004 Logit Scale: 99.840 - V4 -2024-11-27,03:46:57 | INFO | Train Epoch: 0 [ 7629312/10637090 (72%)] Loss: 0.96109 (1.269) Data (t): 0.001 Batch (t): 0.921, 573.336/s LR: 0.000004 Logit Scale: 99.841 - V4 -2024-11-27,03:48:29 | INFO | Train Epoch: 0 [ 7680512/10637090 (72%)] Loss: 1.0640 (1.267) Data (t): 0.001 Batch (t): 0.924, 574.690/s LR: 0.000004 Logit Scale: 99.843 - V4 -2024-11-27,03:50:00 | INFO | Train Epoch: 0 [ 7731712/10637090 (73%)] Loss: 1.1384 (1.266) Data (t): 0.001 Batch (t): 0.906, 573.956/s LR: 0.000004 Logit Scale: 99.845 - V4 -2024-11-27,03:51:29 | INFO | Train Epoch: 0 [ 7782912/10637090 (73%)] Loss: 1.1253 (1.265) Data (t): 0.001 Batch (t): 0.895, 570.350/s LR: 0.000004 Logit Scale: 99.846 - V4 -2024-11-27,03:52:59 | INFO | Train Epoch: 0 [ 7834112/10637090 (74%)] Loss: 1.1012 (1.264) Data (t): 0.001 Batch (t): 0.895, 570.958/s LR: 0.000004 Logit Scale: 99.849 - V4 -2024-11-27,03:54:30 | INFO | Train Epoch: 0 [ 7885312/10637090 (74%)] Loss: 1.2335 (1.264) Data (t): 0.001 Batch (t): 0.914, 572.382/s LR: 0.000003 Logit Scale: 99.850 - V4 -2024-11-27,03:56:03 | INFO | Train Epoch: 0 [ 7936512/10637090 (75%)] Loss: 1.1014 (1.263) Data (t): 0.001 Batch (t): 0.928, 570.918/s LR: 0.000003 Logit Scale: 99.853 - V4 -2024-11-27,03:57:35 | INFO | Train Epoch: 0 [ 7987712/10637090 (75%)] Loss: 1.1607 (1.262) Data (t): 0.001 Batch (t): 0.916, 568.421/s LR: 0.000003 Logit Scale: 99.855 - V4 -2024-11-27,03:59:04 | INFO | Train Epoch: 0 [ 8038912/10637090 (76%)] Loss: 1.0089 (1.261) Data (t): 0.001 Batch (t): 0.895, 571.523/s LR: 0.000003 Logit Scale: 99.857 - V4 -2024-11-27,04:00:34 | INFO | Train Epoch: 0 [ 8090112/10637090 (76%)] Loss: 1.1753 (1.260) Data (t): 0.001 Batch (t): 0.896, 572.515/s LR: 0.000003 Logit Scale: 99.857 - V4 -2024-11-27,04:02:04 | INFO | Train Epoch: 0 [ 8141312/10637090 (77%)] Loss: 1.0299 (1.259) Data (t): 0.001 Batch (t): 0.897, 573.351/s LR: 0.000003 Logit Scale: 99.861 - V4 -2024-11-27,04:03:38 | INFO | Train Epoch: 0 [ 8192512/10637090 (77%)] Loss: 1.1359 (1.258) Data (t): 0.001 Batch (t): 0.946, 258.268/s LR: 0.000003 Logit Scale: 99.863 - V4 -2024-11-27,04:05:10 | INFO | Train Epoch: 0 [ 8243712/10637090 (78%)] Loss: 1.0495 (1.257) Data (t): 0.001 Batch (t): 0.917, 572.484/s LR: 0.000003 Logit Scale: 99.865 - V4 -2024-11-27,04:06:40 | INFO | Train Epoch: 0 [ 8294912/10637090 (78%)] Loss: 1.1191 (1.256) Data (t): 0.001 Batch (t): 0.897, 569.789/s LR: 0.000003 Logit Scale: 99.868 - V4 -2024-11-27,04:08:09 | INFO | Train Epoch: 0 [ 8346112/10637090 (78%)] Loss: 1.0729 (1.255) Data (t): 0.001 Batch (t): 0.897, 569.753/s LR: 0.000003 Logit Scale: 99.870 - V4 -2024-11-27,04:09:39 | INFO | Train Epoch: 0 [ 8397312/10637090 (79%)] Loss: 1.0643 (1.254) Data (t): 0.001 Batch (t): 0.896, 570.698/s LR: 0.000003 Logit Scale: 99.873 - V4 -2024-11-27,04:11:12 | INFO | Train Epoch: 0 [ 8448512/10637090 (79%)] Loss: 1.1415 (1.253) Data (t): 0.001 Batch (t): 0.934, 570.096/s LR: 0.000003 Logit Scale: 99.874 - V4 -2024-11-27,04:12:44 | INFO | Train Epoch: 0 [ 8499712/10637090 (80%)] Loss: 1.0682 (1.252) Data (t): 0.001 Batch (t): 0.917, 570.128/s LR: 0.000003 Logit Scale: 99.877 - V4 -2024-11-27,04:14:15 | INFO | Train Epoch: 0 [ 8550912/10637090 (80%)] Loss: 1.0792 (1.251) Data (t): 0.001 Batch (t): 0.907, 569.003/s LR: 0.000003 Logit Scale: 99.882 - V4 -2024-11-27,04:15:44 | INFO | Train Epoch: 0 [ 8602112/10637090 (81%)] Loss: 1.0108 (1.249) Data (t): 0.001 Batch (t): 0.897, 572.395/s LR: 0.000003 Logit Scale: 99.886 - V4 -2024-11-27,04:17:14 | INFO | Train Epoch: 0 [ 8653312/10637090 (81%)] Loss: 1.1330 (1.249) Data (t): 0.001 Batch (t): 0.897, 570.980/s LR: 0.000003 Logit Scale: 99.886 - V4 -2024-11-27,04:18:48 | INFO | Train Epoch: 0 [ 8704512/10637090 (82%)] Loss: 1.2478 (1.249) Data (t): 0.001 Batch (t): 0.937, 569.938/s LR: 0.000003 Logit Scale: 99.892 - V4 -2024-11-27,04:20:20 | INFO | Train Epoch: 0 [ 8755712/10637090 (82%)] Loss: 1.0364 (1.248) Data (t): 0.001 Batch (t): 0.917, 570.768/s LR: 0.000003 Logit Scale: 99.893 - V4 -2024-11-27,04:21:50 | INFO | Train Epoch: 0 [ 8806912/10637090 (83%)] Loss: 1.1142 (1.247) Data (t): 0.001 Batch (t): 0.907, 570.615/s LR: 0.000003 Logit Scale: 99.896 - V4 -2024-11-27,04:23:20 | INFO | Train Epoch: 0 [ 8858112/10637090 (83%)] Loss: 1.3697 (1.247) Data (t): 0.001 Batch (t): 0.896, 569.593/s LR: 0.000003 Logit Scale: 99.903 - V4 -2024-11-27,04:24:49 | INFO | Train Epoch: 0 [ 8909312/10637090 (84%)] Loss: 1.2082 (1.247) Data (t): 0.001 Batch (t): 0.895, 571.698/s LR: 0.000003 Logit Scale: 99.905 - V4 -2024-11-27,04:26:22 | INFO | Train Epoch: 0 [ 8960512/10637090 (84%)] Loss: 1.1149 (1.246) Data (t): 0.001 Batch (t): 0.930, 571.555/s LR: 0.000003 Logit Scale: 99.909 - V4 -2024-11-27,04:27:54 | INFO | Train Epoch: 0 [ 9011712/10637090 (85%)] Loss: 1.3058 (1.247) Data (t): 0.001 Batch (t): 0.915, 572.179/s LR: 0.000003 Logit Scale: 99.912 - V4 -2024-11-27,04:29:26 | INFO | Train Epoch: 0 [ 9062912/10637090 (85%)] Loss: 1.2046 (1.247) Data (t): 0.001 Batch (t): 0.918, 570.659/s LR: 0.000003 Logit Scale: 99.917 - V4 -2024-11-27,04:30:55 | INFO | Train Epoch: 0 [ 9114112/10637090 (86%)] Loss: 1.3685 (1.247) Data (t): 0.001 Batch (t): 0.895, 572.701/s LR: 0.000003 Logit Scale: 99.917 - V4 -2024-11-27,04:32:25 | INFO | Train Epoch: 0 [ 9165312/10637090 (86%)] Loss: 1.0527 (1.246) Data (t): 0.001 Batch (t): 0.895, 570.638/s LR: 0.000003 Logit Scale: 99.922 - V4 -2024-11-27,04:33:56 | INFO | Train Epoch: 0 [ 9216512/10637090 (87%)] Loss: 1.1506 (1.246) Data (t): 0.001 Batch (t): 0.914, 571.971/s LR: 0.000003 Logit Scale: 99.923 - V4 -2024-11-27,04:35:29 | INFO | Train Epoch: 0 [ 9267712/10637090 (87%)] Loss: 1.1005 (1.245) Data (t): 0.001 Batch (t): 0.928, 573.551/s LR: 0.000003 Logit Scale: 99.927 - V4 -2024-11-27,04:37:00 | INFO | Train Epoch: 0 [ 9318912/10637090 (88%)] Loss: 1.0531 (1.244) Data (t): 0.001 Batch (t): 0.917, 572.713/s LR: 0.000003 Logit Scale: 99.929 - V4 -2024-11-27,04:38:30 | INFO | Train Epoch: 0 [ 9370112/10637090 (88%)] Loss: 1.3258 (1.244) Data (t): 0.001 Batch (t): 0.895, 573.920/s LR: 0.000003 Logit Scale: 99.931 - V4 -2024-11-27,04:40:00 | INFO | Train Epoch: 0 [ 9421312/10637090 (89%)] Loss: 0.87377 (1.242) Data (t): 0.001 Batch (t): 0.895, 572.551/s LR: 0.000003 Logit Scale: 99.933 - V4 -2024-11-27,04:41:29 | INFO | Train Epoch: 0 [ 9472512/10637090 (89%)] Loss: 1.0942 (1.241) Data (t): 0.001 Batch (t): 0.895, 571.491/s LR: 0.000003 Logit Scale: 99.938 - V4 -2024-11-27,04:43:03 | INFO | Train Epoch: 0 [ 9523712/10637090 (90%)] Loss: 1.1874 (1.241) Data (t): 0.001 Batch (t): 0.936, 571.812/s LR: 0.000003 Logit Scale: 99.944 - V4 -2024-11-27,04:44:34 | INFO | Train Epoch: 0 [ 9574912/10637090 (90%)] Loss: 1.1915 (1.241) Data (t): 0.001 Batch (t): 0.917, 569.109/s LR: 0.000003 Logit Scale: 99.944 - V4 -2024-11-27,04:46:05 | INFO | Train Epoch: 0 [ 9626112/10637090 (90%)] Loss: 1.1215 (1.240) Data (t): 0.001 Batch (t): 0.905, 572.676/s LR: 0.000003 Logit Scale: 99.947 - V4 -2024-11-27,04:47:34 | INFO | Train Epoch: 0 [ 9677312/10637090 (91%)] Loss: 1.1917 (1.240) Data (t): 0.001 Batch (t): 0.895, 575.117/s LR: 0.000003 Logit Scale: 99.952 - V4 -2024-11-27,04:49:04 | INFO | Train Epoch: 0 [ 9728512/10637090 (91%)] Loss: 1.0777 (1.239) Data (t): 0.001 Batch (t): 0.895, 571.019/s LR: 0.000003 Logit Scale: 99.953 - V4 -2024-11-27,04:50:37 | INFO | Train Epoch: 0 [ 9779712/10637090 (92%)] Loss: 1.0869 (1.238) Data (t): 0.001 Batch (t): 0.935, 574.195/s LR: 0.000003 Logit Scale: 99.956 - V4 -2024-11-27,04:52:09 | INFO | Train Epoch: 0 [ 9830912/10637090 (92%)] Loss: 1.1759 (1.238) Data (t): 0.001 Batch (t): 0.916, 571.018/s LR: 0.000003 Logit Scale: 99.959 - V4 -2024-11-27,04:53:39 | INFO | Train Epoch: 0 [ 9882112/10637090 (93%)] Loss: 1.1170 (1.237) Data (t): 0.001 Batch (t): 0.904, 571.626/s LR: 0.000003 Logit Scale: 99.961 - V4 -2024-11-27,04:55:09 | INFO | Train Epoch: 0 [ 9933312/10637090 (93%)] Loss: 1.1807 (1.237) Data (t): 0.001 Batch (t): 0.895, 570.554/s LR: 0.000003 Logit Scale: 99.964 - V4 -2024-11-27,04:56:38 | INFO | Train Epoch: 0 [ 9984512/10637090 (94%)] Loss: 1.1203 (1.237) Data (t): 0.001 Batch (t): 0.894, 575.774/s LR: 0.000003 Logit Scale: 99.969 - V4 -2024-11-27,04:58:12 | INFO | Train Epoch: 0 [10035712/10637090 (94%)] Loss: 1.1971 (1.236) Data (t): 0.001 Batch (t): 0.936, 572.666/s LR: 0.000003 Logit Scale: 99.976 - V4 -2024-11-27,04:59:43 | INFO | Train Epoch: 0 [10086912/10637090 (95%)] Loss: 1.0003 (1.235) Data (t): 0.001 Batch (t): 0.907, 573.248/s LR: 0.000003 Logit Scale: 99.980 - V4 -2024-11-27,05:01:14 | INFO | Train Epoch: 0 [10138112/10637090 (95%)] Loss: 1.2638 (1.235) Data (t): 0.001 Batch (t): 0.917, 571.226/s LR: 0.000003 Logit Scale: 99.984 - V4 -2024-11-27,05:02:44 | INFO | Train Epoch: 0 [10189312/10637090 (96%)] Loss: 1.1377 (1.235) Data (t): 0.001 Batch (t): 0.896, 573.971/s LR: 0.000003 Logit Scale: 99.986 - V4 -2024-11-27,05:04:13 | INFO | Train Epoch: 0 [10240512/10637090 (96%)] Loss: 1.1627 (1.234) Data (t): 0.001 Batch (t): 0.895, 570.690/s LR: 0.000003 Logit Scale: 99.990 - V4 -2024-11-27,05:05:46 | INFO | Train Epoch: 0 [10291712/10637090 (97%)] Loss: 1.1491 (1.234) Data (t): 0.001 Batch (t): 0.922, 569.910/s LR: 0.000003 Logit Scale: 99.994 - V4 -2024-11-27,05:07:17 | INFO | Train Epoch: 0 [10342912/10637090 (97%)] Loss: 1.0414 (1.233) Data (t): 0.001 Batch (t): 0.910, 573.756/s LR: 0.000003 Logit Scale: 99.998 - V4 -2024-11-27,05:08:50 | INFO | Train Epoch: 0 [10394112/10637090 (98%)] Loss: 1.1568 (1.233) Data (t): 0.001 Batch (t): 0.929, 570.643/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,05:10:19 | INFO | Train Epoch: 0 [10445312/10637090 (98%)] Loss: 1.2443 (1.233) Data (t): 0.001 Batch (t): 0.895, 570.889/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,05:11:49 | INFO | Train Epoch: 0 [10496512/10637090 (99%)] Loss: 0.99356 (1.232) Data (t): 0.001 Batch (t): 0.896, 570.394/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,05:13:20 | INFO | Train Epoch: 0 [10547712/10637090 (99%)] Loss: 1.2998 (1.232) Data (t): 0.001 Batch (t): 0.916, 253.427/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,05:14:52 | INFO | Train Epoch: 0 [10598912/10637090 (100%)] Loss: 1.0761 (1.231) Data (t): 0.001 Batch (t): 0.919, 571.106/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,05:16:01 | INFO | Train Epoch: 0 [10636800/10637090 (100%)] Loss: 1.0335 (1.230) Data (t): 0.002 Batch (t): 0.926, 574.966/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,05:16:08 | INFO | Start epoch 1 -2024-11-27,05:16:12 | INFO | Train Epoch: 1 [ 512/10637090 (0%)] Loss: 1.0128 (1.013) Data (t): 2.981 Batch (t): 3.918, 130.688/s LR: 0.000003 Logit Scale: 100.000 - V4 -2024-11-27,05:17:43 | INFO | Train Epoch: 1 [ 51712/10637090 (0%)] Loss: 1.2033 (1.108) Data (t): 0.001 Batch (t): 0.909, 571.364/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,05:19:13 | INFO | Train Epoch: 1 [ 102912/10637090 (1%)] Loss: 1.2030 (1.140) Data (t): 0.001 Batch (t): 0.898, 572.273/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,05:20:42 | INFO | Train Epoch: 1 [ 154112/10637090 (1%)] Loss: 1.1652 (1.146) Data (t): 0.001 Batch (t): 0.898, 572.128/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,05:22:16 | INFO | Train Epoch: 1 [ 205312/10637090 (2%)] Loss: 1.0050 (1.118) Data (t): 0.001 Batch (t): 0.940, 573.144/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,05:23:48 | INFO | Train Epoch: 1 [ 256512/10637090 (2%)] Loss: 0.99667 (1.098) Data (t): 0.001 Batch (t): 0.918, 574.014/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,05:25:19 | INFO | Train Epoch: 1 [ 307712/10637090 (3%)] Loss: 1.0535 (1.091) Data (t): 0.001 Batch (t): 0.906, 568.583/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,05:26:49 | INFO | Train Epoch: 1 [ 358912/10637090 (3%)] Loss: 1.0576 (1.087) Data (t): 0.001 Batch (t): 0.897, 571.214/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,05:28:18 | INFO | Train Epoch: 1 [ 410112/10637090 (4%)] Loss: 1.0211 (1.080) Data (t): 0.001 Batch (t): 0.897, 569.649/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,05:29:51 | INFO | Train Epoch: 1 [ 461312/10637090 (4%)] Loss: 1.1738 (1.089) Data (t): 0.001 Batch (t): 0.926, 572.019/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,05:31:22 | INFO | Train Epoch: 1 [ 512512/10637090 (5%)] Loss: 1.0125 (1.082) Data (t): 0.001 Batch (t): 0.913, 571.222/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,05:32:54 | INFO | Train Epoch: 1 [ 563712/10637090 (5%)] Loss: 1.0086 (1.076) Data (t): 0.001 Batch (t): 0.916, 570.775/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,05:34:24 | INFO | Train Epoch: 1 [ 614912/10637090 (6%)] Loss: 1.1774 (1.084) Data (t): 0.001 Batch (t): 0.898, 568.716/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,05:35:53 | INFO | Train Epoch: 1 [ 666112/10637090 (6%)] Loss: 0.98214 (1.077) Data (t): 0.001 Batch (t): 0.898, 571.983/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,05:37:25 | INFO | Train Epoch: 1 [ 717312/10637090 (7%)] Loss: 1.0125 (1.072) Data (t): 0.001 Batch (t): 0.913, 574.215/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,05:38:57 | INFO | Train Epoch: 1 [ 768512/10637090 (7%)] Loss: 0.96567 (1.066) Data (t): 0.001 Batch (t): 0.926, 568.692/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,05:40:29 | INFO | Train Epoch: 1 [ 819712/10637090 (8%)] Loss: 1.1290 (1.069) Data (t): 0.001 Batch (t): 0.917, 567.079/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,05:41:59 | INFO | Train Epoch: 1 [ 870912/10637090 (8%)] Loss: 0.95952 (1.063) Data (t): 0.001 Batch (t): 0.899, 565.774/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,05:43:29 | INFO | Train Epoch: 1 [ 922112/10637090 (9%)] Loss: 1.2495 (1.073) Data (t): 0.001 Batch (t): 0.897, 567.043/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,05:44:59 | INFO | Train Epoch: 1 [ 973312/10637090 (9%)] Loss: 1.0532 (1.072) Data (t): 0.001 Batch (t): 0.904, 571.326/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,05:46:33 | INFO | Train Epoch: 1 [ 1024512/10637090 (10%)] Loss: 0.96439 (1.067) Data (t): 0.001 Batch (t): 0.936, 566.503/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,05:48:03 | INFO | Train Epoch: 1 [ 1075712/10637090 (10%)] Loss: 1.0617 (1.067) Data (t): 0.001 Batch (t): 0.906, 571.041/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,05:49:34 | INFO | Train Epoch: 1 [ 1126912/10637090 (11%)] Loss: 0.95454 (1.062) Data (t): 0.001 Batch (t): 0.906, 571.611/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,05:51:03 | INFO | Train Epoch: 1 [ 1178112/10637090 (11%)] Loss: 1.0707 (1.062) Data (t): 0.001 Batch (t): 0.896, 569.365/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,05:52:33 | INFO | Train Epoch: 1 [ 1229312/10637090 (12%)] Loss: 1.0439 (1.061) Data (t): 0.001 Batch (t): 0.897, 572.121/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,05:54:06 | INFO | Train Epoch: 1 [ 1280512/10637090 (12%)] Loss: 1.1100 (1.063) Data (t): 0.001 Batch (t): 0.933, 574.313/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,05:55:38 | INFO | Train Epoch: 1 [ 1331712/10637090 (13%)] Loss: 1.0108 (1.061) Data (t): 0.001 Batch (t): 0.915, 571.737/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,05:57:08 | INFO | Train Epoch: 1 [ 1382912/10637090 (13%)] Loss: 1.1779 (1.066) Data (t): 0.001 Batch (t): 0.905, 570.526/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,05:58:38 | INFO | Train Epoch: 1 [ 1434112/10637090 (13%)] Loss: 0.96992 (1.062) Data (t): 0.001 Batch (t): 0.894, 572.490/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,06:00:07 | INFO | Train Epoch: 1 [ 1485312/10637090 (14%)] Loss: 1.0453 (1.062) Data (t): 0.001 Batch (t): 0.895, 572.927/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,06:01:40 | INFO | Train Epoch: 1 [ 1536512/10637090 (14%)] Loss: 1.0654 (1.062) Data (t): 0.001 Batch (t): 0.924, 570.176/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,06:03:12 | INFO | Train Epoch: 1 [ 1587712/10637090 (15%)] Loss: 1.0905 (1.063) Data (t): 0.001 Batch (t): 0.922, 571.839/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,06:04:42 | INFO | Train Epoch: 1 [ 1638912/10637090 (15%)] Loss: 1.1550 (1.066) Data (t): 0.001 Batch (t): 0.905, 570.747/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,06:06:12 | INFO | Train Epoch: 1 [ 1690112/10637090 (16%)] Loss: 1.0767 (1.066) Data (t): 0.001 Batch (t): 0.896, 572.266/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,06:07:42 | INFO | Train Epoch: 1 [ 1741312/10637090 (16%)] Loss: 1.1005 (1.067) Data (t): 0.001 Batch (t): 0.895, 568.226/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,06:09:14 | INFO | Train Epoch: 1 [ 1792512/10637090 (17%)] Loss: 1.1870 (1.070) Data (t): 0.001 Batch (t): 0.925, 570.328/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,06:10:45 | INFO | Train Epoch: 1 [ 1843712/10637090 (17%)] Loss: 1.0125 (1.069) Data (t): 0.001 Batch (t): 0.912, 571.452/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,06:12:17 | INFO | Train Epoch: 1 [ 1894912/10637090 (18%)] Loss: 0.95886 (1.066) Data (t): 0.001 Batch (t): 0.916, 573.938/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,06:13:46 | INFO | Train Epoch: 1 [ 1946112/10637090 (18%)] Loss: 1.1727 (1.068) Data (t): 0.001 Batch (t): 0.896, 567.277/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,06:15:16 | INFO | Train Epoch: 1 [ 1997312/10637090 (19%)] Loss: 1.0444 (1.068) Data (t): 0.001 Batch (t): 0.896, 572.636/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,06:16:47 | INFO | Train Epoch: 1 [ 2048512/10637090 (19%)] Loss: 1.0698 (1.068) Data (t): 0.001 Batch (t): 0.912, 573.693/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,06:18:20 | INFO | Train Epoch: 1 [ 2099712/10637090 (20%)] Loss: 0.84103 (1.063) Data (t): 0.001 Batch (t): 0.926, 572.319/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,06:19:51 | INFO | Train Epoch: 1 [ 2150912/10637090 (20%)] Loss: 0.98125 (1.061) Data (t): 0.001 Batch (t): 0.914, 573.110/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,06:21:21 | INFO | Train Epoch: 1 [ 2202112/10637090 (21%)] Loss: 0.98067 (1.059) Data (t): 0.001 Batch (t): 0.895, 571.854/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,06:22:50 | INFO | Train Epoch: 1 [ 2253312/10637090 (21%)] Loss: 0.97608 (1.057) Data (t): 0.001 Batch (t): 0.895, 571.749/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,06:24:20 | INFO | Train Epoch: 1 [ 2304512/10637090 (22%)] Loss: 1.1066 (1.058) Data (t): 0.001 Batch (t): 0.902, 573.417/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,06:25:53 | INFO | Train Epoch: 1 [ 2355712/10637090 (22%)] Loss: 1.0162 (1.057) Data (t): 0.001 Batch (t): 0.924, 572.695/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,06:27:24 | INFO | Train Epoch: 1 [ 2406912/10637090 (23%)] Loss: 1.0336 (1.057) Data (t): 0.001 Batch (t): 0.914, 573.991/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,06:28:55 | INFO | Train Epoch: 1 [ 2458112/10637090 (23%)] Loss: 1.2035 (1.060) Data (t): 0.001 Batch (t): 0.904, 573.854/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,06:30:24 | INFO | Train Epoch: 1 [ 2509312/10637090 (24%)] Loss: 1.0746 (1.060) Data (t): 0.001 Batch (t): 0.894, 569.583/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,06:31:54 | INFO | Train Epoch: 1 [ 2560512/10637090 (24%)] Loss: 1.1349 (1.061) Data (t): 0.001 Batch (t): 0.895, 573.191/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,06:33:27 | INFO | Train Epoch: 1 [ 2611712/10637090 (25%)] Loss: 1.0716 (1.062) Data (t): 0.001 Batch (t): 0.931, 571.037/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,06:34:58 | INFO | Train Epoch: 1 [ 2662912/10637090 (25%)] Loss: 1.0197 (1.061) Data (t): 0.001 Batch (t): 0.914, 573.285/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,06:36:29 | INFO | Train Epoch: 1 [ 2714112/10637090 (26%)] Loss: 1.0800 (1.061) Data (t): 0.001 Batch (t): 0.905, 570.613/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,06:37:58 | INFO | Train Epoch: 1 [ 2765312/10637090 (26%)] Loss: 1.0601 (1.061) Data (t): 0.001 Batch (t): 0.896, 572.227/s LR: 0.000002 Logit Scale: 100.000 - V4 -2024-11-27,06:39:28 | INFO | Train Epoch: 1 [ 2816512/10637090 (26%)] Loss: 1.0374 (1.061) Data (t): 0.001 Batch (t): 0.896, 574.116/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,06:41:00 | INFO | Train Epoch: 1 [ 2867712/10637090 (27%)] Loss: 1.0835 (1.061) Data (t): 0.001 Batch (t): 0.925, 573.652/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,06:42:32 | INFO | Train Epoch: 1 [ 2918912/10637090 (27%)] Loss: 1.0796 (1.061) Data (t): 0.001 Batch (t): 0.921, 279.225/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,06:44:03 | INFO | Train Epoch: 1 [ 2970112/10637090 (28%)] Loss: 1.0603 (1.061) Data (t): 0.001 Batch (t): 0.905, 570.632/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,06:45:32 | INFO | Train Epoch: 1 [ 3021312/10637090 (28%)] Loss: 1.1166 (1.062) Data (t): 0.001 Batch (t): 0.896, 571.055/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,06:47:02 | INFO | Train Epoch: 1 [ 3072512/10637090 (29%)] Loss: 0.90431 (1.060) Data (t): 0.001 Batch (t): 0.896, 567.176/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,06:48:33 | INFO | Train Epoch: 1 [ 3123712/10637090 (29%)] Loss: 1.0275 (1.059) Data (t): 0.001 Batch (t): 0.912, 570.255/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,06:50:06 | INFO | Train Epoch: 1 [ 3174912/10637090 (30%)] Loss: 1.1707 (1.061) Data (t): 0.001 Batch (t): 0.925, 569.632/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,06:51:37 | INFO | Train Epoch: 1 [ 3226112/10637090 (30%)] Loss: 1.1179 (1.062) Data (t): 0.001 Batch (t): 0.915, 569.527/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,06:53:07 | INFO | Train Epoch: 1 [ 3277312/10637090 (31%)] Loss: 1.0479 (1.062) Data (t): 0.001 Batch (t): 0.895, 568.036/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,06:54:36 | INFO | Train Epoch: 1 [ 3328512/10637090 (31%)] Loss: 1.1544 (1.063) Data (t): 0.001 Batch (t): 0.894, 571.927/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,06:56:07 | INFO | Train Epoch: 1 [ 3379712/10637090 (32%)] Loss: 1.0102 (1.062) Data (t): 0.001 Batch (t): 0.913, 571.492/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,06:57:39 | INFO | Train Epoch: 1 [ 3430912/10637090 (32%)] Loss: 1.0633 (1.062) Data (t): 0.001 Batch (t): 0.916, 572.763/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,06:59:11 | INFO | Train Epoch: 1 [ 3482112/10637090 (33%)] Loss: 0.98734 (1.061) Data (t): 0.001 Batch (t): 0.925, 278.302/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:00:41 | INFO | Train Epoch: 1 [ 3533312/10637090 (33%)] Loss: 1.1443 (1.062) Data (t): 0.001 Batch (t): 0.896, 569.593/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:02:11 | INFO | Train Epoch: 1 [ 3584512/10637090 (34%)] Loss: 1.0147 (1.062) Data (t): 0.001 Batch (t): 0.896, 574.240/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:03:40 | INFO | Train Epoch: 1 [ 3635712/10637090 (34%)] Loss: 1.1478 (1.063) Data (t): 0.001 Batch (t): 0.897, 571.050/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:05:14 | INFO | Train Epoch: 1 [ 3686912/10637090 (35%)] Loss: 0.99775 (1.062) Data (t): 0.001 Batch (t): 0.933, 569.403/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:06:45 | INFO | Train Epoch: 1 [ 3738112/10637090 (35%)] Loss: 0.89432 (1.060) Data (t): 0.001 Batch (t): 0.915, 571.482/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:08:16 | INFO | Train Epoch: 1 [ 3789312/10637090 (36%)] Loss: 1.0577 (1.060) Data (t): 0.001 Batch (t): 0.905, 573.403/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:09:45 | INFO | Train Epoch: 1 [ 3840512/10637090 (36%)] Loss: 1.0405 (1.060) Data (t): 0.001 Batch (t): 0.896, 571.636/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:11:15 | INFO | Train Epoch: 1 [ 3891712/10637090 (37%)] Loss: 1.0512 (1.059) Data (t): 0.001 Batch (t): 0.895, 573.732/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:12:48 | INFO | Train Epoch: 1 [ 3942912/10637090 (37%)] Loss: 0.98693 (1.058) Data (t): 0.001 Batch (t): 0.931, 568.375/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:14:20 | INFO | Train Epoch: 1 [ 3994112/10637090 (38%)] Loss: 1.0355 (1.058) Data (t): 0.001 Batch (t): 0.921, 566.626/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:15:50 | INFO | Train Epoch: 1 [ 4045312/10637090 (38%)] Loss: 1.2874 (1.061) Data (t): 0.001 Batch (t): 0.905, 572.765/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:17:20 | INFO | Train Epoch: 1 [ 4096512/10637090 (39%)] Loss: 1.0966 (1.061) Data (t): 0.001 Batch (t): 0.897, 572.618/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:18:50 | INFO | Train Epoch: 1 [ 4147712/10637090 (39%)] Loss: 1.0360 (1.061) Data (t): 0.001 Batch (t): 0.895, 569.975/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:20:22 | INFO | Train Epoch: 1 [ 4198912/10637090 (39%)] Loss: 1.1183 (1.062) Data (t): 0.001 Batch (t): 0.919, 569.986/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:21:53 | INFO | Train Epoch: 1 [ 4250112/10637090 (40%)] Loss: 1.0804 (1.062) Data (t): 0.001 Batch (t): 0.918, 574.977/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:23:25 | INFO | Train Epoch: 1 [ 4301312/10637090 (40%)] Loss: 1.0074 (1.061) Data (t): 0.001 Batch (t): 0.914, 571.915/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:24:54 | INFO | Train Epoch: 1 [ 4352512/10637090 (41%)] Loss: 1.0147 (1.061) Data (t): 0.001 Batch (t): 0.895, 570.253/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:26:24 | INFO | Train Epoch: 1 [ 4403712/10637090 (41%)] Loss: 0.98719 (1.060) Data (t): 0.001 Batch (t): 0.895, 572.913/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:27:55 | INFO | Train Epoch: 1 [ 4454912/10637090 (42%)] Loss: 1.0311 (1.060) Data (t): 0.001 Batch (t): 0.911, 570.976/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:29:27 | INFO | Train Epoch: 1 [ 4506112/10637090 (42%)] Loss: 1.0330 (1.059) Data (t): 0.001 Batch (t): 0.919, 572.207/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:30:59 | INFO | Train Epoch: 1 [ 4557312/10637090 (43%)] Loss: 1.0003 (1.059) Data (t): 0.001 Batch (t): 0.924, 572.237/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:32:29 | INFO | Train Epoch: 1 [ 4608512/10637090 (43%)] Loss: 1.0510 (1.059) Data (t): 0.001 Batch (t): 0.894, 572.512/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:33:58 | INFO | Train Epoch: 1 [ 4659712/10637090 (44%)] Loss: 1.0159 (1.058) Data (t): 0.001 Batch (t): 0.895, 574.088/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:35:29 | INFO | Train Epoch: 1 [ 4710912/10637090 (44%)] Loss: 1.1518 (1.059) Data (t): 0.001 Batch (t): 0.911, 573.055/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:37:01 | INFO | Train Epoch: 1 [ 4762112/10637090 (45%)] Loss: 1.0437 (1.059) Data (t): 0.001 Batch (t): 0.917, 572.585/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:38:32 | INFO | Train Epoch: 1 [ 4813312/10637090 (45%)] Loss: 0.98672 (1.058) Data (t): 0.001 Batch (t): 0.913, 570.547/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:40:03 | INFO | Train Epoch: 1 [ 4864512/10637090 (46%)] Loss: 1.0978 (1.059) Data (t): 0.001 Batch (t): 0.904, 572.213/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:41:32 | INFO | Train Epoch: 1 [ 4915712/10637090 (46%)] Loss: 0.89812 (1.057) Data (t): 0.001 Batch (t): 0.894, 571.949/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:43:02 | INFO | Train Epoch: 1 [ 4966912/10637090 (47%)] Loss: 1.0941 (1.057) Data (t): 0.001 Batch (t): 0.895, 571.482/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:44:35 | INFO | Train Epoch: 1 [ 5018112/10637090 (47%)] Loss: 1.0506 (1.057) Data (t): 0.001 Batch (t): 0.935, 569.850/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:46:06 | INFO | Train Epoch: 1 [ 5069312/10637090 (48%)] Loss: 0.99763 (1.057) Data (t): 0.001 Batch (t): 0.914, 575.887/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:47:37 | INFO | Train Epoch: 1 [ 5120512/10637090 (48%)] Loss: 0.87316 (1.055) Data (t): 0.001 Batch (t): 0.904, 568.556/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:49:07 | INFO | Train Epoch: 1 [ 5171712/10637090 (49%)] Loss: 1.1561 (1.056) Data (t): 0.001 Batch (t): 0.896, 572.086/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:50:36 | INFO | Train Epoch: 1 [ 5222912/10637090 (49%)] Loss: 1.0303 (1.056) Data (t): 0.001 Batch (t): 0.896, 573.661/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:52:08 | INFO | Train Epoch: 1 [ 5274112/10637090 (50%)] Loss: 1.0331 (1.055) Data (t): 0.001 Batch (t): 0.919, 572.625/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:53:40 | INFO | Train Epoch: 1 [ 5325312/10637090 (50%)] Loss: 0.96590 (1.055) Data (t): 0.001 Batch (t): 0.923, 270.099/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:55:12 | INFO | Train Epoch: 1 [ 5376512/10637090 (51%)] Loss: 1.1712 (1.056) Data (t): 0.001 Batch (t): 0.915, 574.077/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:56:41 | INFO | Train Epoch: 1 [ 5427712/10637090 (51%)] Loss: 1.0271 (1.055) Data (t): 0.001 Batch (t): 0.895, 570.777/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:58:11 | INFO | Train Epoch: 1 [ 5478912/10637090 (52%)] Loss: 1.1131 (1.056) Data (t): 0.001 Batch (t): 0.896, 570.787/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,07:59:43 | INFO | Train Epoch: 1 [ 5530112/10637090 (52%)] Loss: 1.0604 (1.056) Data (t): 0.001 Batch (t): 0.920, 570.413/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,08:01:14 | INFO | Train Epoch: 1 [ 5581312/10637090 (52%)] Loss: 1.0678 (1.056) Data (t): 0.001 Batch (t): 0.914, 572.048/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,08:02:47 | INFO | Train Epoch: 1 [ 5632512/10637090 (53%)] Loss: 1.1293 (1.057) Data (t): 0.001 Batch (t): 0.924, 572.418/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,08:04:16 | INFO | Train Epoch: 1 [ 5683712/10637090 (53%)] Loss: 1.1303 (1.057) Data (t): 0.001 Batch (t): 0.896, 570.208/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,08:05:46 | INFO | Train Epoch: 1 [ 5734912/10637090 (54%)] Loss: 0.95027 (1.056) Data (t): 0.001 Batch (t): 0.896, 568.880/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,08:07:17 | INFO | Train Epoch: 1 [ 5786112/10637090 (54%)] Loss: 0.92660 (1.055) Data (t): 0.001 Batch (t): 0.913, 571.644/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,08:08:49 | INFO | Train Epoch: 1 [ 5837312/10637090 (55%)] Loss: 1.0374 (1.055) Data (t): 0.001 Batch (t): 0.920, 571.733/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,08:10:22 | INFO | Train Epoch: 1 [ 5888512/10637090 (55%)] Loss: 0.95528 (1.054) Data (t): 0.001 Batch (t): 0.927, 571.842/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,08:11:51 | INFO | Train Epoch: 1 [ 5939712/10637090 (56%)] Loss: 1.0986 (1.055) Data (t): 0.001 Batch (t): 0.897, 571.042/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,08:13:21 | INFO | Train Epoch: 1 [ 5990912/10637090 (56%)] Loss: 1.1118 (1.055) Data (t): 0.001 Batch (t): 0.896, 572.127/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,08:14:52 | INFO | Train Epoch: 1 [ 6042112/10637090 (57%)] Loss: 1.0610 (1.055) Data (t): 0.001 Batch (t): 0.912, 571.703/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,08:16:24 | INFO | Train Epoch: 1 [ 6093312/10637090 (57%)] Loss: 1.0524 (1.055) Data (t): 0.001 Batch (t): 0.921, 574.197/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,08:17:56 | INFO | Train Epoch: 1 [ 6144512/10637090 (58%)] Loss: 1.0528 (1.055) Data (t): 0.001 Batch (t): 0.915, 571.926/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,08:19:27 | INFO | Train Epoch: 1 [ 6195712/10637090 (58%)] Loss: 1.0440 (1.055) Data (t): 0.001 Batch (t): 0.906, 570.911/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,08:20:56 | INFO | Train Epoch: 1 [ 6246912/10637090 (59%)] Loss: 1.1593 (1.056) Data (t): 0.001 Batch (t): 0.895, 568.834/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-11-27,08:22:26 | INFO | Train Epoch: 1 [ 6298112/10637090 (59%)] Loss: 0.92146 (1.055) Data (t): 0.001 Batch (t): 0.895, 568.399/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,08:23:58 | INFO | Train Epoch: 1 [ 6349312/10637090 (60%)] Loss: 0.93241 (1.054) Data (t): 0.001 Batch (t): 0.920, 572.968/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,08:25:30 | INFO | Train Epoch: 1 [ 6400512/10637090 (60%)] Loss: 0.99950 (1.053) Data (t): 0.001 Batch (t): 0.922, 571.263/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,08:27:01 | INFO | Train Epoch: 1 [ 6451712/10637090 (61%)] Loss: 0.87824 (1.052) Data (t): 0.001 Batch (t): 0.915, 572.957/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,08:28:31 | INFO | Train Epoch: 1 [ 6502912/10637090 (61%)] Loss: 1.0529 (1.052) Data (t): 0.001 Batch (t): 0.895, 572.566/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,08:30:00 | INFO | Train Epoch: 1 [ 6554112/10637090 (62%)] Loss: 0.84983 (1.051) Data (t): 0.001 Batch (t): 0.895, 572.458/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,08:31:32 | INFO | Train Epoch: 1 [ 6605312/10637090 (62%)] Loss: 1.0097 (1.050) Data (t): 0.001 Batch (t): 0.920, 572.206/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,08:33:04 | INFO | Train Epoch: 1 [ 6656512/10637090 (63%)] Loss: 1.0204 (1.050) Data (t): 0.001 Batch (t): 0.912, 572.053/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,08:34:36 | INFO | Train Epoch: 1 [ 6707712/10637090 (63%)] Loss: 1.1329 (1.051) Data (t): 0.001 Batch (t): 0.924, 571.449/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,08:36:05 | INFO | Train Epoch: 1 [ 6758912/10637090 (64%)] Loss: 1.1183 (1.051) Data (t): 0.001 Batch (t): 0.894, 573.877/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,08:37:35 | INFO | Train Epoch: 1 [ 6810112/10637090 (64%)] Loss: 1.0636 (1.051) Data (t): 0.001 Batch (t): 0.894, 571.467/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,08:39:07 | INFO | Train Epoch: 1 [ 6861312/10637090 (65%)] Loss: 1.0463 (1.051) Data (t): 0.001 Batch (t): 0.918, 573.057/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,08:40:38 | INFO | Train Epoch: 1 [ 6912512/10637090 (65%)] Loss: 1.0026 (1.051) Data (t): 0.001 Batch (t): 0.911, 577.593/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,08:42:10 | INFO | Train Epoch: 1 [ 6963712/10637090 (65%)] Loss: 1.0808 (1.051) Data (t): 0.000 Batch (t): 0.923, 571.991/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,08:43:40 | INFO | Train Epoch: 1 [ 7014912/10637090 (66%)] Loss: 0.97769 (1.051) Data (t): 0.001 Batch (t): 0.895, 570.742/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,08:45:09 | INFO | Train Epoch: 1 [ 7066112/10637090 (66%)] Loss: 1.0644 (1.051) Data (t): 0.001 Batch (t): 0.895, 571.450/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,08:46:40 | INFO | Train Epoch: 1 [ 7117312/10637090 (67%)] Loss: 1.0796 (1.051) Data (t): 0.001 Batch (t): 0.912, 570.051/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,08:48:12 | INFO | Train Epoch: 1 [ 7168512/10637090 (67%)] Loss: 0.99263 (1.050) Data (t): 0.001 Batch (t): 0.918, 564.585/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,08:49:43 | INFO | Train Epoch: 1 [ 7219712/10637090 (68%)] Loss: 0.97418 (1.050) Data (t): 0.001 Batch (t): 0.913, 574.592/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,08:51:13 | INFO | Train Epoch: 1 [ 7270912/10637090 (68%)] Loss: 0.97256 (1.049) Data (t): 0.001 Batch (t): 0.901, 574.151/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,08:52:43 | INFO | Train Epoch: 1 [ 7322112/10637090 (69%)] Loss: 1.0953 (1.050) Data (t): 0.001 Batch (t): 0.892, 574.728/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,08:54:14 | INFO | Train Epoch: 1 [ 7373312/10637090 (69%)] Loss: 1.0402 (1.050) Data (t): 0.001 Batch (t): 0.910, 574.869/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,08:55:44 | INFO | Train Epoch: 1 [ 7424512/10637090 (70%)] Loss: 1.0422 (1.050) Data (t): 0.001 Batch (t): 0.907, 574.034/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,08:57:16 | INFO | Train Epoch: 1 [ 7475712/10637090 (70%)] Loss: 1.1315 (1.050) Data (t): 0.001 Batch (t): 0.912, 575.577/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,08:58:47 | INFO | Train Epoch: 1 [ 7526912/10637090 (71%)] Loss: 1.0247 (1.050) Data (t): 0.001 Batch (t): 0.913, 574.663/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:00:16 | INFO | Train Epoch: 1 [ 7578112/10637090 (71%)] Loss: 0.96505 (1.049) Data (t): 0.001 Batch (t): 0.893, 572.353/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:01:46 | INFO | Train Epoch: 1 [ 7629312/10637090 (72%)] Loss: 0.89964 (1.048) Data (t): 0.001 Batch (t): 0.893, 573.886/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:03:17 | INFO | Train Epoch: 1 [ 7680512/10637090 (72%)] Loss: 0.94812 (1.048) Data (t): 0.001 Batch (t): 0.919, 572.963/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:04:49 | INFO | Train Epoch: 1 [ 7731712/10637090 (73%)] Loss: 1.0112 (1.047) Data (t): 0.001 Batch (t): 0.912, 573.004/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:06:21 | INFO | Train Epoch: 1 [ 7782912/10637090 (73%)] Loss: 0.96896 (1.047) Data (t): 0.001 Batch (t): 0.923, 573.496/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:07:50 | INFO | Train Epoch: 1 [ 7834112/10637090 (74%)] Loss: 1.1535 (1.048) Data (t): 0.001 Batch (t): 0.893, 571.504/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:09:20 | INFO | Train Epoch: 1 [ 7885312/10637090 (74%)] Loss: 1.1186 (1.048) Data (t): 0.001 Batch (t): 0.892, 574.372/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:10:51 | INFO | Train Epoch: 1 [ 7936512/10637090 (75%)] Loss: 1.0656 (1.048) Data (t): 0.001 Batch (t): 0.917, 571.524/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:12:22 | INFO | Train Epoch: 1 [ 7987712/10637090 (75%)] Loss: 0.94982 (1.048) Data (t): 0.001 Batch (t): 0.910, 574.255/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:13:54 | INFO | Train Epoch: 1 [ 8038912/10637090 (76%)] Loss: 1.0177 (1.047) Data (t): 0.001 Batch (t): 0.922, 574.910/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:15:24 | INFO | Train Epoch: 1 [ 8090112/10637090 (76%)] Loss: 0.94190 (1.047) Data (t): 0.001 Batch (t): 0.893, 574.880/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:16:53 | INFO | Train Epoch: 1 [ 8141312/10637090 (77%)] Loss: 1.1812 (1.048) Data (t): 0.001 Batch (t): 0.892, 573.457/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:18:24 | INFO | Train Epoch: 1 [ 8192512/10637090 (77%)] Loss: 1.1853 (1.048) Data (t): 0.001 Batch (t): 0.910, 575.807/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:19:56 | INFO | Train Epoch: 1 [ 8243712/10637090 (78%)] Loss: 1.1836 (1.049) Data (t): 0.001 Batch (t): 0.917, 574.010/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:21:28 | INFO | Train Epoch: 1 [ 8294912/10637090 (78%)] Loss: 1.1244 (1.050) Data (t): 0.001 Batch (t): 0.923, 574.645/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:22:57 | INFO | Train Epoch: 1 [ 8346112/10637090 (78%)] Loss: 0.97844 (1.049) Data (t): 0.001 Batch (t): 0.894, 571.009/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:24:27 | INFO | Train Epoch: 1 [ 8397312/10637090 (79%)] Loss: 0.95430 (1.049) Data (t): 0.001 Batch (t): 0.894, 569.645/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:25:58 | INFO | Train Epoch: 1 [ 8448512/10637090 (79%)] Loss: 1.1523 (1.049) Data (t): 0.001 Batch (t): 0.910, 574.517/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:27:28 | INFO | Train Epoch: 1 [ 8499712/10637090 (80%)] Loss: 1.0875 (1.050) Data (t): 0.001 Batch (t): 0.908, 573.476/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:29:00 | INFO | Train Epoch: 1 [ 8550912/10637090 (80%)] Loss: 1.0188 (1.049) Data (t): 0.001 Batch (t): 0.915, 573.363/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:30:31 | INFO | Train Epoch: 1 [ 8602112/10637090 (81%)] Loss: 1.1405 (1.050) Data (t): 0.001 Batch (t): 0.915, 573.662/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:32:01 | INFO | Train Epoch: 1 [ 8653312/10637090 (81%)] Loss: 0.93292 (1.049) Data (t): 0.001 Batch (t): 0.895, 573.662/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:33:31 | INFO | Train Epoch: 1 [ 8704512/10637090 (82%)] Loss: 1.0593 (1.049) Data (t): 0.001 Batch (t): 0.903, 574.119/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:35:03 | INFO | Train Epoch: 1 [ 8755712/10637090 (82%)] Loss: 1.0038 (1.049) Data (t): 0.001 Batch (t): 0.920, 574.029/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:36:35 | INFO | Train Epoch: 1 [ 8806912/10637090 (83%)] Loss: 1.0629 (1.049) Data (t): 0.001 Batch (t): 0.916, 266.849/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:38:06 | INFO | Train Epoch: 1 [ 8858112/10637090 (83%)] Loss: 0.94171 (1.048) Data (t): 0.001 Batch (t): 0.915, 572.994/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:39:36 | INFO | Train Epoch: 1 [ 8909312/10637090 (84%)] Loss: 1.1578 (1.049) Data (t): 0.001 Batch (t): 0.894, 573.463/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:41:05 | INFO | Train Epoch: 1 [ 8960512/10637090 (84%)] Loss: 1.0284 (1.049) Data (t): 0.001 Batch (t): 0.895, 570.680/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:42:37 | INFO | Train Epoch: 1 [ 9011712/10637090 (85%)] Loss: 1.0820 (1.049) Data (t): 0.001 Batch (t): 0.920, 573.570/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:44:08 | INFO | Train Epoch: 1 [ 9062912/10637090 (85%)] Loss: 0.97469 (1.049) Data (t): 0.001 Batch (t): 0.913, 570.541/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:45:41 | INFO | Train Epoch: 1 [ 9114112/10637090 (86%)] Loss: 1.0719 (1.049) Data (t): 0.001 Batch (t): 0.925, 571.838/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:47:10 | INFO | Train Epoch: 1 [ 9165312/10637090 (86%)] Loss: 0.97399 (1.048) Data (t): 0.001 Batch (t): 0.894, 571.676/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:48:40 | INFO | Train Epoch: 1 [ 9216512/10637090 (87%)] Loss: 1.1124 (1.049) Data (t): 0.001 Batch (t): 0.894, 570.389/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:50:11 | INFO | Train Epoch: 1 [ 9267712/10637090 (87%)] Loss: 1.0737 (1.049) Data (t): 0.001 Batch (t): 0.918, 575.484/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:51:43 | INFO | Train Epoch: 1 [ 9318912/10637090 (88%)] Loss: 1.0940 (1.049) Data (t): 0.001 Batch (t): 0.913, 573.371/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:53:15 | INFO | Train Epoch: 1 [ 9370112/10637090 (88%)] Loss: 1.1696 (1.050) Data (t): 0.001 Batch (t): 0.925, 572.882/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:54:45 | INFO | Train Epoch: 1 [ 9421312/10637090 (89%)] Loss: 0.97639 (1.049) Data (t): 0.001 Batch (t): 0.896, 571.202/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:56:14 | INFO | Train Epoch: 1 [ 9472512/10637090 (89%)] Loss: 1.0731 (1.050) Data (t): 0.001 Batch (t): 0.896, 571.435/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:57:46 | INFO | Train Epoch: 1 [ 9523712/10637090 (90%)] Loss: 0.95788 (1.049) Data (t): 0.001 Batch (t): 0.912, 574.514/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,09:59:17 | INFO | Train Epoch: 1 [ 9574912/10637090 (90%)] Loss: 1.0155 (1.049) Data (t): 0.001 Batch (t): 0.911, 568.433/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,10:00:48 | INFO | Train Epoch: 1 [ 9626112/10637090 (90%)] Loss: 0.95145 (1.048) Data (t): 0.001 Batch (t): 0.916, 573.715/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,10:02:20 | INFO | Train Epoch: 1 [ 9677312/10637090 (91%)] Loss: 1.0364 (1.048) Data (t): 0.001 Batch (t): 0.916, 572.356/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,10:03:49 | INFO | Train Epoch: 1 [ 9728512/10637090 (91%)] Loss: 1.0150 (1.048) Data (t): 0.001 Batch (t): 0.896, 572.428/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,10:05:21 | INFO | Train Epoch: 1 [ 9779712/10637090 (92%)] Loss: 0.99979 (1.048) Data (t): 0.001 Batch (t): 0.913, 573.510/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,10:06:52 | INFO | Train Epoch: 1 [ 9830912/10637090 (92%)] Loss: 1.0624 (1.048) Data (t): 0.001 Batch (t): 0.910, 572.486/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,10:08:23 | INFO | Train Epoch: 1 [ 9882112/10637090 (93%)] Loss: 0.97977 (1.048) Data (t): 0.001 Batch (t): 0.915, 570.660/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,10:09:55 | INFO | Train Epoch: 1 [ 9933312/10637090 (93%)] Loss: 0.97347 (1.047) Data (t): 0.001 Batch (t): 0.914, 573.422/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,10:11:24 | INFO | Train Epoch: 1 [ 9984512/10637090 (94%)] Loss: 1.0213 (1.047) Data (t): 0.001 Batch (t): 0.893, 574.399/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,10:12:54 | INFO | Train Epoch: 1 [10035712/10637090 (94%)] Loss: 1.0902 (1.047) Data (t): 0.001 Batch (t): 0.902, 572.747/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,10:14:26 | INFO | Train Epoch: 1 [10086912/10637090 (95%)] Loss: 1.0825 (1.048) Data (t): 0.001 Batch (t): 0.920, 571.481/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,10:15:57 | INFO | Train Epoch: 1 [10138112/10637090 (95%)] Loss: 0.83598 (1.046) Data (t): 0.001 Batch (t): 0.905, 568.170/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,10:17:29 | INFO | Train Epoch: 1 [10189312/10637090 (96%)] Loss: 1.0455 (1.046) Data (t): 0.001 Batch (t): 0.925, 571.571/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,10:18:59 | INFO | Train Epoch: 1 [10240512/10637090 (96%)] Loss: 0.98382 (1.046) Data (t): 0.001 Batch (t): 0.894, 573.749/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,10:20:28 | INFO | Train Epoch: 1 [10291712/10637090 (97%)] Loss: 0.88477 (1.045) Data (t): 0.001 Batch (t): 0.894, 573.371/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,10:22:00 | INFO | Train Epoch: 1 [10342912/10637090 (97%)] Loss: 0.92619 (1.045) Data (t): 0.001 Batch (t): 0.921, 571.458/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,10:23:31 | INFO | Train Epoch: 1 [10394112/10637090 (98%)] Loss: 1.0476 (1.045) Data (t): 0.001 Batch (t): 0.914, 572.968/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,10:25:03 | INFO | Train Epoch: 1 [10445312/10637090 (98%)] Loss: 0.92127 (1.044) Data (t): 0.001 Batch (t): 0.916, 573.688/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,10:26:33 | INFO | Train Epoch: 1 [10496512/10637090 (99%)] Loss: 1.0239 (1.044) Data (t): 0.001 Batch (t): 0.904, 571.659/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,10:28:03 | INFO | Train Epoch: 1 [10547712/10637090 (99%)] Loss: 0.99158 (1.044) Data (t): 0.001 Batch (t): 0.894, 571.625/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,10:29:35 | INFO | Train Epoch: 1 [10598912/10637090 (100%)] Loss: 0.84240 (1.043) Data (t): 0.001 Batch (t): 0.920, 573.355/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-11-27,10:30:42 | INFO | Train Epoch: 1 [10636800/10637090 (100%)] Loss: 1.0801 (1.043) Data (t): 0.002 Batch (t): 0.905, 575.829/s LR: 0.000000 Logit Scale: 100.000 - V4 diff --git a/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten_decimal2/2024_11_26-23_59_33-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/params.txt b/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten_decimal2/2024_11_26-23_59_33-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/params.txt deleted file mode 100644 index f8bbaf2d971e85204132b108bdf36e6d356dc27a..0000000000000000000000000000000000000000 --- a/data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten_decimal2/2024_11_26-23_59_33-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/params.txt +++ /dev/null @@ -1,67 +0,0 @@ -batch_size: 64 -beta1: 0.9 -beta2: 0.98 -checkpoint_path: data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten_decimal2/2024_11_26-23_59_33-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/checkpoints -copy_codebase: False -csv_caption_key: caption -csv_hard_captions_key: neg_caption -csv_img_key: img_path -csv_separator: , -dataset_resampled: False -dataset_type: csv -ddp_static_graph: False -debug: False -device: cuda:0 -dist_backend: nccl -dist_url: env:// -distributed: True -epochs: 2 -eps: 1e-06 -force_quick_gelu: True -gather_with_grad: False -grad_checkpointing: False -horovod: False -imagenet_v2: None -imagenet_val: None -local_loss: False -local_rank: 0 -lock_image: False -lock_image_freeze_bn_stats: False -lock_image_unlocked_groups: 0 -log_level: 20 -log_local: False -log_path: data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten_decimal2/2024_11_26-23_59_33-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp/out.log -logs: data/trained_openclip/negative_logs/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten_decimal2 -lr: 5e-06 -model: ViT-L-14-336 -name: 2024_11_26-23_59_33-model_ViT-L-14-336-lr_5e-06-b_64-j_4-p_amp -no_set_device_rank: False -norm_gradient_clip: None -precision: amp -pretrained: data/openclip-vit-14-336/openclip_model.pt -pretrained_image: False -rank: 0 -report_to: wandb -resume: None -save_frequency: 1 -save_most_recent: False -seed: 0 -skip_scheduler: False -tensorboard: False -tensorboard_path: -torchscript: False -trace: False -train_data: csv_data/plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten_decimal2.csv -train_num_samples: None -use_bn_sync: False -val_data: None -val_frequency: 1 -val_num_samples: None -wandb: True -wandb_notes: -wandb_project: neg-clip-plotqa_train_only_qa_v2_5false_formated_sampled_fixed_flaten_decimal2 -warmup: 0 -wd: 0.1 -workers: 4 -world_size: 8 -zeroshot_frequency: 2 diff --git a/data/trained_openclip/negative_logs/plotqa_v2/2024_09_02-19_36_58-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/checkpoints/epoch_1.pt b/data/trained_openclip/negative_logs/plotqa_v2/2024_09_02-19_36_58-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/checkpoints/epoch_1.pt deleted file mode 100644 index 16f15a2b6203a0ae8bd83fa6eca850a0e9de1919..0000000000000000000000000000000000000000 --- a/data/trained_openclip/negative_logs/plotqa_v2/2024_09_02-19_36_58-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/checkpoints/epoch_1.pt +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:4fa61f66e5d6ebdbcf9adc05ef5d3c7ea3be2084b43ee1bc23ab857b7e65bc6d -size 5135890710 diff --git a/data/trained_openclip/negative_logs/plotqa_v2/2024_09_02-19_36_58-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/checkpoints/epoch_2.pt b/data/trained_openclip/negative_logs/plotqa_v2/2024_09_02-19_36_58-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/checkpoints/epoch_2.pt deleted file mode 100644 index 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sha256:ec5f7ec1f08fbc956c82d8f0574da0712b16d36627c6e17e30282cf7a7935eb1 -size 5135890710 diff --git a/data/trained_openclip/negative_logs/plotqa_v2/2024_09_02-19_36_58-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/out.log b/data/trained_openclip/negative_logs/plotqa_v2/2024_09_02-19_36_58-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/out.log deleted file mode 100644 index a0b8e445b7a96fbd896d34453eeda202ecea142e..0000000000000000000000000000000000000000 --- a/data/trained_openclip/negative_logs/plotqa_v2/2024_09_02-19_36_58-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/out.log +++ /dev/null @@ -1,549 +0,0 @@ -2024-09-02,19:36:58 | INFO | Running in distributed mode with multiple processes. Device: cuda:0.Process (global: 0, local 0), total 4. -2024-09-02,19:36:58 | INFO | Loading ViT-L-14-336 model config. -2024-09-02,19:37:00 | INFO | Loading pretrained ViT-L-14-336 weights (/project/deemreason/junteng/Vision4Math/data/openclip-vit-14-336/openclip_model.pt). -2024-09-02,19:37:02 | INFO | Model: -2024-09-02,19:37:02 | INFO | CLIP( - (visual): VisualTransformer( - (conv1): Conv2d(3, 1024, kernel_size=(14, 14), stride=(14, 14), bias=False) - (ln_pre): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) - (transformer): Transformer( - (resblocks): ModuleList( - (0-23): 24 x ResidualAttentionBlock( - (attn): MultiheadAttention( - (out_proj): NonDynamicallyQuantizableLinear(in_features=1024, out_features=1024, bias=True) - ) - (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) - (mlp): Sequential( - (c_fc): Linear(in_features=1024, out_features=4096, bias=True) - (gelu): QuickGELU() - (c_proj): Linear(in_features=4096, out_features=1024, bias=True) - ) - (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) - ) - ) - ) - (ln_post): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) - ) - (transformer): Transformer( - (resblocks): ModuleList( - (0-11): 12 x ResidualAttentionBlock( - (attn): MultiheadAttention( - (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) - ) - (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) - (mlp): Sequential( - (c_fc): Linear(in_features=768, out_features=3072, bias=True) - (gelu): QuickGELU() - (c_proj): Linear(in_features=3072, out_features=768, bias=True) - ) - (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) - ) - ) - ) - (token_embedding): Embedding(49408, 768) - (ln_final): LayerNorm((768,), eps=1e-05, elementwise_affine=True) -) -2024-09-02,19:37:02 | INFO | Params: -2024-09-02,19:37:02 | INFO | batch_size: 64 -2024-09-02,19:37:02 | INFO | beta1: 0.9 -2024-09-02,19:37:02 | INFO | beta2: 0.98 -2024-09-02,19:37:02 | INFO | checkpoint_path: /project/deemreason/junteng/Vision4Math/train_clip/negative_logs/plotqa_v2/2024_09_02-19_36_58-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/checkpoints -2024-09-02,19:37:02 | INFO | copy_codebase: False -2024-09-02,19:37:02 | INFO | csv_caption_key: caption -2024-09-02,19:37:02 | INFO | csv_hard_captions_key: neg_caption -2024-09-02,19:37:02 | INFO | csv_img_key: img_path -2024-09-02,19:37:02 | INFO | csv_separator: , -2024-09-02,19:37:02 | INFO | dataset_resampled: False -2024-09-02,19:37:02 | INFO | dataset_type: csv -2024-09-02,19:37:02 | INFO | ddp_static_graph: False -2024-09-02,19:37:02 | INFO | debug: False -2024-09-02,19:37:02 | INFO | device: cuda:0 -2024-09-02,19:37:02 | INFO | dist_backend: nccl -2024-09-02,19:37:02 | INFO | dist_url: env:// -2024-09-02,19:37:02 | INFO | distributed: True -2024-09-02,19:37:02 | INFO | epochs: 3 -2024-09-02,19:37:02 | INFO | eps: 1e-06 -2024-09-02,19:37:02 | INFO | force_quick_gelu: True -2024-09-02,19:37:02 | INFO | gather_with_grad: False -2024-09-02,19:37:02 | INFO | grad_checkpointing: False -2024-09-02,19:37:02 | INFO | horovod: False -2024-09-02,19:37:02 | INFO | imagenet_v2: None -2024-09-02,19:37:02 | INFO | imagenet_val: None -2024-09-02,19:37:02 | INFO | local_loss: False -2024-09-02,19:37:02 | INFO | local_rank: 0 -2024-09-02,19:37:02 | INFO | lock_image: False -2024-09-02,19:37:02 | INFO | lock_image_freeze_bn_stats: False -2024-09-02,19:37:02 | INFO | lock_image_unlocked_groups: 0 -2024-09-02,19:37:02 | INFO | log_level: 20 -2024-09-02,19:37:02 | INFO | log_local: False -2024-09-02,19:37:02 | INFO | log_path: /project/deemreason/junteng/Vision4Math/train_clip/negative_logs/plotqa_v2/2024_09_02-19_36_58-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/out.log -2024-09-02,19:37:02 | INFO | logs: /project/deemreason/junteng/Vision4Math/train_clip/negative_logs/plotqa_v2 -2024-09-02,19:37:02 | INFO | lr: 1e-06 -2024-09-02,19:37:02 | INFO | model: ViT-L-14-336 -2024-09-02,19:37:02 | INFO | name: 2024_09_02-19_36_58-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp -2024-09-02,19:37:02 | INFO | no_set_device_rank: False -2024-09-02,19:37:02 | INFO | norm_gradient_clip: None -2024-09-02,19:37:02 | INFO | precision: amp -2024-09-02,19:37:02 | INFO | pretrained: /project/deemreason/junteng/Vision4Math/data/openclip-vit-14-336/openclip_model.pt -2024-09-02,19:37:02 | INFO | pretrained_image: False -2024-09-02,19:37:02 | INFO | rank: 0 -2024-09-02,19:37:02 | INFO | report_to: wandb -2024-09-02,19:37:02 | INFO | resume: None -2024-09-02,19:37:02 | INFO | save_frequency: 1 -2024-09-02,19:37:02 | INFO | save_most_recent: False -2024-09-02,19:37:02 | INFO | seed: 0 -2024-09-02,19:37:02 | INFO | skip_scheduler: False -2024-09-02,19:37:02 | INFO | tensorboard: False -2024-09-02,19:37:02 | INFO | tensorboard_path: -2024-09-02,19:37:02 | INFO | torchscript: False -2024-09-02,19:37:02 | INFO | trace: False -2024-09-02,19:37:02 | INFO | train_data: /project/deemreason/junteng/Vision4Math/csv_data/plotqa_train_v2.csv -2024-09-02,19:37:02 | INFO | train_num_samples: None -2024-09-02,19:37:02 | INFO | use_bn_sync: False -2024-09-02,19:37:02 | INFO | val_data: None -2024-09-02,19:37:02 | INFO | val_frequency: 1 -2024-09-02,19:37:02 | INFO | val_num_samples: None -2024-09-02,19:37:02 | INFO | wandb: True -2024-09-02,19:37:02 | INFO | wandb_notes: -2024-09-02,19:37:02 | INFO | wandb_project: open-clip-sum -2024-09-02,19:37:02 | INFO | warmup: 0 -2024-09-02,19:37:02 | INFO | wd: 0.1 -2024-09-02,19:37:02 | INFO | workers: 4 -2024-09-02,19:37:02 | INFO | world_size: 4 -2024-09-02,19:37:02 | INFO | zeroshot_frequency: 2 -2024-09-02,19:37:11 | INFO | Init a wandb project! -2024-09-02,19:37:16 | INFO | Start epoch 0 -2024-09-02,19:37:19 | INFO | Train Epoch: 0 [ 256/3655823 (0%)] Loss: 5.5831 (5.583) Data (t): 1.467 Batch (t): 3.462, 73.9382/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,19:38:01 | INFO | Train Epoch: 0 [ 25856/3655823 (1%)] Loss: 3.2637 (4.423) Data (t): 0.000 Batch (t): 0.414, 627.302/s LR: 0.000001 Logit Scale: 99.997 - V4 -2024-09-02,19:38:42 | INFO | Train Epoch: 0 [ 51456/3655823 (1%)] Loss: 2.7307 (3.859) Data (t): 0.000 Batch (t): 0.407, 627.869/s LR: 0.000001 Logit Scale: 99.997 - V4 -2024-09-02,19:39:23 | INFO | Train Epoch: 0 [ 77056/3655823 (2%)] Loss: 2.4601 (3.509) Data (t): 0.000 Batch (t): 0.411, 629.819/s LR: 0.000001 Logit Scale: 99.998 - V4 -2024-09-02,19:40:04 | INFO | Train Epoch: 0 [ 102656/3655823 (3%)] Loss: 2.4481 (3.297) Data (t): 0.001 Batch (t): 0.415, 627.474/s LR: 0.000001 Logit Scale: 99.999 - V4 -2024-09-02,19:40:45 | INFO | Train Epoch: 0 [ 128256/3655823 (4%)] Loss: 2.3653 (3.142) Data (t): 0.000 Batch (t): 0.408, 645.102/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,19:41:26 | INFO | Train Epoch: 0 [ 153856/3655823 (4%)] Loss: 2.4479 (3.043) Data (t): 0.000 Batch (t): 0.408, 627.314/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,19:42:07 | INFO | Train Epoch: 0 [ 179456/3655823 (5%)] Loss: 2.4120 (2.964) Data (t): 0.000 Batch (t): 0.408, 627.802/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,19:42:48 | INFO | Train Epoch: 0 [ 205056/3655823 (6%)] Loss: 2.1145 (2.869) Data (t): 0.000 Batch (t): 0.410, 627.061/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,19:43:29 | INFO | Train Epoch: 0 [ 230656/3655823 (6%)] Loss: 2.1470 (2.797) Data (t): 0.000 Batch (t): 0.412, 627.200/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,19:44:10 | INFO | Train Epoch: 0 [ 256256/3655823 (7%)] Loss: 2.1355 (2.737) Data (t): 0.000 Batch (t): 0.407, 627.290/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,19:44:50 | INFO | Train Epoch: 0 [ 281856/3655823 (8%)] Loss: 1.9676 (2.673) Data (t): 0.001 Batch (t): 0.407, 627.746/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,19:45:31 | INFO | Train Epoch: 0 [ 307456/3655823 (8%)] Loss: 1.9708 (2.619) Data (t): 0.000 Batch (t): 0.407, 631.575/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,19:46:12 | INFO | Train Epoch: 0 [ 333056/3655823 (9%)] Loss: 1.8963 (2.567) Data (t): 0.000 Batch (t): 0.407, 628.187/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,19:46:53 | INFO | Train Epoch: 0 [ 358656/3655823 (10%)] Loss: 1.7813 (2.515) Data (t): 0.000 Batch (t): 0.416, 630.738/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,19:47:34 | INFO | Train Epoch: 0 [ 384256/3655823 (11%)] Loss: 1.9189 (2.478) Data (t): 0.000 Batch (t): 0.407, 628.081/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,19:48:15 | INFO | Train Epoch: 0 [ 409856/3655823 (11%)] Loss: 2.0016 (2.450) Data (t): 0.000 Batch (t): 0.407, 629.155/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,19:48:56 | INFO | Train Epoch: 0 [ 435456/3655823 (12%)] Loss: 1.8773 (2.418) Data (t): 0.000 Batch (t): 0.408, 626.731/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,19:49:36 | INFO | Train Epoch: 0 [ 461056/3655823 (13%)] Loss: 1.8882 (2.390) Data (t): 0.000 Batch (t): 0.408, 628.219/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,19:50:18 | INFO | Train Epoch: 0 [ 486656/3655823 (13%)] Loss: 2.1099 (2.376) Data (t): 0.000 Batch (t): 0.416, 630.040/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,19:50:59 | INFO | Train Epoch: 0 [ 512256/3655823 (14%)] Loss: 1.8348 (2.350) Data (t): 0.000 Batch (t): 0.407, 629.517/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,19:51:39 | INFO | Train Epoch: 0 [ 537856/3655823 (15%)] Loss: 1.9693 (2.333) Data (t): 0.000 Batch (t): 0.408, 630.885/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,19:52:20 | INFO | Train Epoch: 0 [ 563456/3655823 (15%)] Loss: 1.9957 (2.318) Data (t): 0.000 Batch (t): 0.408, 628.967/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,19:53:01 | INFO | Train Epoch: 0 [ 589056/3655823 (16%)] Loss: 1.8148 (2.297) Data (t): 0.001 Batch (t): 0.408, 628.276/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,19:53:43 | INFO | Train Epoch: 0 [ 614656/3655823 (17%)] Loss: 1.7455 (2.275) Data (t): 0.000 Batch (t): 0.416, 629.975/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,19:54:23 | INFO | Train Epoch: 0 [ 640256/3655823 (18%)] Loss: 1.8137 (2.257) Data (t): 0.000 Batch (t): 0.407, 630.226/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,19:55:04 | INFO | Train Epoch: 0 [ 665856/3655823 (18%)] Loss: 2.0148 (2.248) Data (t): 0.000 Batch (t): 0.407, 627.805/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,19:55:45 | INFO | Train Epoch: 0 [ 691456/3655823 (19%)] Loss: 1.9422 (2.238) Data (t): 0.000 Batch (t): 0.407, 627.260/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,19:56:26 | INFO | Train Epoch: 0 [ 717056/3655823 (20%)] Loss: 1.7487 (2.221) Data (t): 0.000 Batch (t): 0.407, 627.539/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,19:57:07 | INFO | Train Epoch: 0 [ 742656/3655823 (20%)] Loss: 1.7226 (2.204) Data (t): 0.000 Batch (t): 0.412, 629.291/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,19:57:48 | INFO | Train Epoch: 0 [ 768256/3655823 (21%)] Loss: 1.9393 (2.196) Data (t): 0.000 Batch (t): 0.409, 630.002/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,19:58:28 | INFO | Train Epoch: 0 [ 793856/3655823 (22%)] Loss: 1.7361 (2.181) Data (t): 0.000 Batch (t): 0.407, 628.506/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,19:59:09 | INFO | Train Epoch: 0 [ 819456/3655823 (22%)] Loss: 1.9322 (2.174) Data (t): 0.001 Batch (t): 0.407, 629.090/s LR: 0.000001 Logit Scale: 99.999 - V4 -2024-09-02,19:59:50 | INFO | Train Epoch: 0 [ 845056/3655823 (23%)] Loss: 1.9691 (2.168) Data (t): 0.000 Batch (t): 0.407, 628.442/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:00:31 | INFO | Train Epoch: 0 [ 870656/3655823 (24%)] Loss: 1.7442 (2.156) Data (t): 0.000 Batch (t): 0.410, 628.569/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:01:12 | INFO | Train Epoch: 0 [ 896256/3655823 (25%)] Loss: 1.5916 (2.140) Data (t): 0.000 Batch (t): 0.414, 630.010/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:01:53 | INFO | Train Epoch: 0 [ 921856/3655823 (25%)] Loss: 1.5470 (2.124) Data (t): 0.000 Batch (t): 0.407, 627.363/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:02:34 | INFO | Train Epoch: 0 [ 947456/3655823 (26%)] Loss: 1.9370 (2.119) Data (t): 0.000 Batch (t): 0.407, 631.444/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:03:14 | INFO | Train Epoch: 0 [ 973056/3655823 (27%)] Loss: 1.6025 (2.106) Data (t): 0.000 Batch (t): 0.407, 627.173/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:03:55 | INFO | Train Epoch: 0 [ 998656/3655823 (27%)] Loss: 1.9974 (2.103) Data (t): 0.000 Batch (t): 0.410, 627.276/s LR: 0.000001 Logit Scale: 99.999 - V4 -2024-09-02,20:04:37 | INFO | Train Epoch: 0 [1024256/3655823 (28%)] Loss: 1.7604 (2.095) Data (t): 0.000 Batch (t): 0.412, 629.020/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:05:17 | INFO | Train Epoch: 0 [1049856/3655823 (29%)] Loss: 1.7433 (2.086) Data (t): 0.001 Batch (t): 0.407, 630.089/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:05:58 | INFO | Train Epoch: 0 [1075456/3655823 (29%)] Loss: 1.7398 (2.078) Data (t): 0.001 Batch (t): 0.407, 629.147/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:06:39 | INFO | Train Epoch: 0 [1101056/3655823 (30%)] Loss: 1.7736 (2.071) Data (t): 0.000 Batch (t): 0.407, 627.362/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:07:20 | INFO | Train Epoch: 0 [1126656/3655823 (31%)] Loss: 1.7673 (2.065) Data (t): 0.001 Batch (t): 0.407, 628.700/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:08:01 | INFO | Train Epoch: 0 [1152256/3655823 (32%)] Loss: 1.7794 (2.058) Data (t): 0.001 Batch (t): 0.416, 629.627/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:08:42 | INFO | Train Epoch: 0 [1177856/3655823 (32%)] Loss: 1.7664 (2.052) Data (t): 0.001 Batch (t): 0.407, 627.477/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:09:23 | INFO | Train Epoch: 0 [1203456/3655823 (33%)] Loss: 1.9198 (2.049) Data (t): 0.000 Batch (t): 0.407, 628.791/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:10:03 | INFO | Train Epoch: 0 [1229056/3655823 (34%)] Loss: 1.7759 (2.044) Data (t): 0.000 Batch (t): 0.407, 628.510/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:10:44 | INFO | Train Epoch: 0 [1254656/3655823 (34%)] Loss: 1.5365 (2.034) Data (t): 0.001 Batch (t): 0.408, 628.667/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:11:26 | INFO | Train Epoch: 0 [1280256/3655823 (35%)] Loss: 1.8680 (2.030) Data (t): 0.000 Batch (t): 0.415, 627.007/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:12:06 | INFO | Train Epoch: 0 [1305856/3655823 (36%)] Loss: 1.6181 (2.022) Data (t): 0.001 Batch (t): 0.407, 627.473/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:12:47 | INFO | Train Epoch: 0 [1331456/3655823 (36%)] Loss: 1.8238 (2.019) Data (t): 0.000 Batch (t): 0.407, 628.349/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:13:28 | INFO | Train Epoch: 0 [1357056/3655823 (37%)] Loss: 1.5133 (2.009) Data (t): 0.000 Batch (t): 0.408, 629.197/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:14:09 | INFO | Train Epoch: 0 [1382656/3655823 (38%)] Loss: 1.6781 (2.003) Data (t): 0.000 Batch (t): 0.408, 629.223/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:14:50 | INFO | Train Epoch: 0 [1408256/3655823 (39%)] Loss: 1.7688 (1.999) Data (t): 0.000 Batch (t): 0.417, 626.672/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:15:31 | INFO | Train Epoch: 0 [1433856/3655823 (39%)] Loss: 1.8756 (1.997) Data (t): 0.000 Batch (t): 0.408, 628.428/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:16:12 | INFO | Train Epoch: 0 [1459456/3655823 (40%)] Loss: 1.7711 (1.993) Data (t): 0.000 Batch (t): 0.408, 628.928/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:16:53 | INFO | Train Epoch: 0 [1485056/3655823 (41%)] Loss: 1.7487 (1.989) Data (t): 0.000 Batch (t): 0.408, 628.357/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:17:33 | INFO | Train Epoch: 0 [1510656/3655823 (41%)] Loss: 1.5975 (1.982) Data (t): 0.000 Batch (t): 0.408, 628.963/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:18:15 | INFO | Train Epoch: 0 [1536256/3655823 (42%)] Loss: 1.3927 (1.973) Data (t): 0.000 Batch (t): 0.414, 628.931/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:18:56 | INFO | Train Epoch: 0 [1561856/3655823 (43%)] Loss: 1.6046 (1.967) Data (t): 0.000 Batch (t): 0.409, 629.768/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:19:36 | INFO | Train Epoch: 0 [1587456/3655823 (43%)] Loss: 1.6262 (1.961) Data (t): 0.000 Batch (t): 0.408, 620.834/s LR: 0.000001 Logit Scale: 99.999 - V4 -2024-09-02,20:20:17 | INFO | Train Epoch: 0 [1613056/3655823 (44%)] Loss: 1.5664 (1.955) Data (t): 0.000 Batch (t): 0.407, 629.223/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:20:58 | INFO | Train Epoch: 0 [1638656/3655823 (45%)] Loss: 1.7675 (1.952) Data (t): 0.000 Batch (t): 0.408, 628.223/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:21:39 | INFO | Train Epoch: 0 [1664256/3655823 (46%)] Loss: 1.6215 (1.947) Data (t): 0.000 Batch (t): 0.410, 628.139/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:22:20 | INFO | Train Epoch: 0 [1689856/3655823 (46%)] Loss: 1.9395 (1.947) Data (t): 0.000 Batch (t): 0.412, 627.024/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:23:01 | INFO | Train Epoch: 0 [1715456/3655823 (47%)] Loss: 1.5725 (1.942) Data (t): 0.000 Batch (t): 0.408, 627.084/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:23:42 | INFO | Train Epoch: 0 [1741056/3655823 (48%)] Loss: 1.4810 (1.935) Data (t): 0.000 Batch (t): 0.408, 627.155/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:24:22 | INFO | Train Epoch: 0 [1766656/3655823 (48%)] Loss: 1.7223 (1.932) Data (t): 0.000 Batch (t): 0.408, 627.320/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:25:03 | INFO | Train Epoch: 0 [1792256/3655823 (49%)] Loss: 1.4262 (1.925) Data (t): 0.000 Batch (t): 0.410, 628.574/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:25:45 | INFO | Train Epoch: 0 [1817856/3655823 (50%)] Loss: 1.5906 (1.920) Data (t): 0.000 Batch (t): 0.415, 628.732/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:26:26 | INFO | Train Epoch: 0 [1843456/3655823 (50%)] Loss: 1.8560 (1.919) Data (t): 0.000 Batch (t): 0.408, 630.705/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:27:06 | INFO | Train Epoch: 0 [1869056/3655823 (51%)] Loss: 1.5540 (1.914) Data (t): 0.000 Batch (t): 0.408, 627.215/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:27:47 | INFO | Train Epoch: 0 [1894656/3655823 (52%)] Loss: 1.5654 (1.910) Data (t): 0.000 Batch (t): 0.407, 626.752/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:28:28 | INFO | Train Epoch: 0 [1920256/3655823 (53%)] Loss: 1.4253 (1.903) Data (t): 0.000 Batch (t): 0.407, 628.087/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:29:09 | INFO | Train Epoch: 0 [1945856/3655823 (53%)] Loss: 1.6971 (1.901) Data (t): 0.000 Batch (t): 0.414, 629.113/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:29:50 | INFO | Train Epoch: 0 [1971456/3655823 (54%)] Loss: 1.7810 (1.899) Data (t): 0.000 Batch (t): 0.407, 627.489/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:30:31 | INFO | Train Epoch: 0 [1997056/3655823 (55%)] Loss: 1.5387 (1.895) Data (t): 0.000 Batch (t): 0.407, 627.345/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:31:11 | INFO | Train Epoch: 0 [2022656/3655823 (55%)] Loss: 1.4292 (1.889) Data (t): 0.000 Batch (t): 0.408, 628.520/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:31:52 | INFO | Train Epoch: 0 [2048256/3655823 (56%)] Loss: 1.7634 (1.887) Data (t): 0.000 Batch (t): 0.407, 628.523/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:32:34 | INFO | Train Epoch: 0 [2073856/3655823 (57%)] Loss: 1.4560 (1.882) Data (t): 0.000 Batch (t): 0.414, 629.622/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:33:14 | INFO | Train Epoch: 0 [2099456/3655823 (57%)] Loss: 1.4923 (1.877) Data (t): 0.000 Batch (t): 0.407, 628.921/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:33:55 | INFO | Train Epoch: 0 [2125056/3655823 (58%)] Loss: 1.6012 (1.874) Data (t): 0.000 Batch (t): 0.407, 628.008/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:34:36 | INFO | Train Epoch: 0 [2150656/3655823 (59%)] Loss: 1.5860 (1.871) Data (t): 0.000 Batch (t): 0.407, 629.937/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:35:17 | INFO | Train Epoch: 0 [2176256/3655823 (60%)] Loss: 1.6384 (1.868) Data (t): 0.000 Batch (t): 0.407, 628.786/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:35:58 | INFO | Train Epoch: 0 [2201856/3655823 (60%)] Loss: 1.7724 (1.867) Data (t): 0.000 Batch (t): 0.416, 629.551/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:36:39 | INFO | Train Epoch: 0 [2227456/3655823 (61%)] Loss: 1.6790 (1.865) Data (t): 0.000 Batch (t): 0.408, 627.271/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:37:20 | INFO | Train Epoch: 0 [2253056/3655823 (62%)] Loss: 1.5074 (1.861) Data (t): 0.000 Batch (t): 0.408, 629.176/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:38:00 | INFO | Train Epoch: 0 [2278656/3655823 (62%)] Loss: 1.5656 (1.857) Data (t): 0.000 Batch (t): 0.408, 624.286/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:38:41 | INFO | Train Epoch: 0 [2304256/3655823 (63%)] Loss: 1.3563 (1.852) Data (t): 0.000 Batch (t): 0.409, 624.346/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:39:23 | INFO | Train Epoch: 0 [2329856/3655823 (64%)] Loss: 1.5710 (1.849) Data (t): 0.000 Batch (t): 0.417, 628.211/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:40:04 | INFO | Train Epoch: 0 [2355456/3655823 (64%)] Loss: 1.7251 (1.847) Data (t): 0.000 Batch (t): 0.408, 626.981/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:40:45 | INFO | Train Epoch: 0 [2381056/3655823 (65%)] Loss: 1.5758 (1.845) Data (t): 0.000 Batch (t): 0.408, 628.556/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:41:25 | INFO | Train Epoch: 0 [2406656/3655823 (66%)] Loss: 1.6105 (1.842) Data (t): 0.000 Batch (t): 0.408, 627.109/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:42:06 | INFO | Train Epoch: 0 [2432256/3655823 (67%)] Loss: 1.7807 (1.841) Data (t): 0.000 Batch (t): 0.407, 628.508/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:42:47 | INFO | Train Epoch: 0 [2457856/3655823 (67%)] Loss: 1.5765 (1.839) Data (t): 0.000 Batch (t): 0.408, 622.872/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:43:29 | INFO | Train Epoch: 0 [2483456/3655823 (68%)] Loss: 1.9540 (1.840) Data (t): 0.000 Batch (t): 0.418, 628.559/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:44:10 | INFO | Train Epoch: 0 [2509056/3655823 (69%)] Loss: 1.5541 (1.837) Data (t): 0.000 Batch (t): 0.408, 627.764/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:44:50 | INFO | Train Epoch: 0 [2534656/3655823 (69%)] Loss: 1.8295 (1.837) Data (t): 0.000 Batch (t): 0.408, 626.775/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:45:31 | INFO | Train Epoch: 0 [2560256/3655823 (70%)] Loss: 1.6062 (1.835) Data (t): 0.000 Batch (t): 0.408, 627.681/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:46:12 | INFO | Train Epoch: 0 [2585856/3655823 (71%)] Loss: 1.5546 (1.832) Data (t): 0.000 Batch (t): 0.408, 627.299/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:46:54 | INFO | Train Epoch: 0 [2611456/3655823 (71%)] Loss: 1.6402 (1.830) Data (t): 0.000 Batch (t): 0.416, 628.529/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:47:34 | INFO | Train Epoch: 0 [2637056/3655823 (72%)] Loss: 1.6546 (1.828) Data (t): 0.000 Batch (t): 0.407, 629.015/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:48:15 | INFO | Train Epoch: 0 [2662656/3655823 (73%)] Loss: 1.7634 (1.828) Data (t): 0.000 Batch (t): 0.408, 627.676/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:48:56 | INFO | Train Epoch: 0 [2688256/3655823 (74%)] Loss: 1.5918 (1.826) Data (t): 0.000 Batch (t): 0.407, 625.348/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:49:37 | INFO | Train Epoch: 0 [2713856/3655823 (74%)] Loss: 1.5016 (1.823) Data (t): 0.000 Batch (t): 0.407, 630.085/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:50:18 | INFO | Train Epoch: 0 [2739456/3655823 (75%)] Loss: 1.7166 (1.822) Data (t): 0.000 Batch (t): 0.417, 629.134/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:50:59 | INFO | Train Epoch: 0 [2765056/3655823 (76%)] Loss: 1.7674 (1.821) Data (t): 0.000 Batch (t): 0.408, 626.911/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:51:40 | INFO | Train Epoch: 0 [2790656/3655823 (76%)] Loss: 1.7379 (1.820) Data (t): 0.000 Batch (t): 0.408, 628.030/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:52:21 | INFO | Train Epoch: 0 [2816256/3655823 (77%)] Loss: 1.9221 (1.821) Data (t): 0.000 Batch (t): 0.408, 626.829/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:53:01 | INFO | Train Epoch: 0 [2841856/3655823 (78%)] Loss: 1.8331 (1.821) Data (t): 0.000 Batch (t): 0.408, 628.023/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:53:43 | INFO | Train Epoch: 0 [2867456/3655823 (78%)] Loss: 1.4906 (1.818) Data (t): 0.000 Batch (t): 0.417, 630.973/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:54:24 | INFO | Train Epoch: 0 [2893056/3655823 (79%)] Loss: 1.6156 (1.817) Data (t): 0.000 Batch (t): 0.408, 627.483/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:55:05 | INFO | Train Epoch: 0 [2918656/3655823 (80%)] Loss: 1.7688 (1.816) Data (t): 0.000 Batch (t): 0.408, 626.426/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:55:45 | INFO | Train Epoch: 0 [2944256/3655823 (81%)] Loss: 1.6564 (1.815) Data (t): 0.000 Batch (t): 0.408, 628.140/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:56:26 | INFO | Train Epoch: 0 [2969856/3655823 (81%)] Loss: 1.5896 (1.813) Data (t): 0.000 Batch (t): 0.408, 628.286/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:57:08 | INFO | Train Epoch: 0 [2995456/3655823 (82%)] Loss: 1.7447 (1.812) Data (t): 0.000 Batch (t): 0.415, 631.621/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:57:49 | INFO | Train Epoch: 0 [3021056/3655823 (83%)] Loss: 1.7545 (1.812) Data (t): 0.000 Batch (t): 0.410, 628.349/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:58:29 | INFO | Train Epoch: 0 [3046656/3655823 (83%)] Loss: 1.5513 (1.810) Data (t): 0.000 Batch (t): 0.407, 626.934/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:59:10 | INFO | Train Epoch: 0 [3072256/3655823 (84%)] Loss: 1.6052 (1.808) Data (t): 0.000 Batch (t): 0.407, 628.211/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,20:59:51 | INFO | Train Epoch: 0 [3097856/3655823 (85%)] Loss: 1.5626 (1.806) Data (t): 0.000 Batch (t): 0.408, 628.421/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:00:32 | INFO | Train Epoch: 0 [3123456/3655823 (85%)] Loss: 1.3925 (1.803) Data (t): 0.000 Batch (t): 0.410, 627.462/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:01:13 | INFO | Train Epoch: 0 [3149056/3655823 (86%)] Loss: 1.4453 (1.800) Data (t): 0.000 Batch (t): 0.415, 626.779/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:01:54 | INFO | Train Epoch: 0 [3174656/3655823 (87%)] Loss: 1.7288 (1.799) Data (t): 0.000 Batch (t): 0.408, 626.773/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:02:35 | INFO | Train Epoch: 0 [3200256/3655823 (88%)] Loss: 1.6877 (1.798) Data (t): 0.000 Batch (t): 0.408, 627.124/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:03:16 | INFO | Train Epoch: 0 [3225856/3655823 (88%)] Loss: 1.5236 (1.796) Data (t): 0.000 Batch (t): 0.408, 628.616/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:03:56 | INFO | Train Epoch: 0 [3251456/3655823 (89%)] Loss: 1.5898 (1.794) Data (t): 0.000 Batch (t): 0.408, 627.820/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:04:38 | INFO | Train Epoch: 0 [3277056/3655823 (90%)] Loss: 1.7068 (1.794) Data (t): 0.000 Batch (t): 0.417, 629.565/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:05:19 | INFO | Train Epoch: 0 [3302656/3655823 (90%)] Loss: 1.9002 (1.795) Data (t): 0.000 Batch (t): 0.408, 626.629/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:06:00 | INFO | Train Epoch: 0 [3328256/3655823 (91%)] Loss: 1.5467 (1.793) Data (t): 0.000 Batch (t): 0.408, 627.183/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:06:41 | INFO | Train Epoch: 0 [3353856/3655823 (92%)] Loss: 1.4131 (1.790) Data (t): 0.000 Batch (t): 0.408, 629.609/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:07:21 | INFO | Train Epoch: 0 [3379456/3655823 (92%)] Loss: 1.4317 (1.787) Data (t): 0.000 Batch (t): 0.408, 628.202/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:08:03 | INFO | Train Epoch: 0 [3405056/3655823 (93%)] Loss: 1.4983 (1.785) Data (t): 0.000 Batch (t): 0.416, 629.785/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:08:44 | INFO | Train Epoch: 0 [3430656/3655823 (94%)] Loss: 1.5149 (1.783) Data (t): 0.000 Batch (t): 0.408, 628.086/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:09:24 | INFO | Train Epoch: 0 [3456256/3655823 (95%)] Loss: 1.6370 (1.782) Data (t): 0.000 Batch (t): 0.408, 627.030/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:10:05 | INFO | Train Epoch: 0 [3481856/3655823 (95%)] Loss: 1.4425 (1.779) Data (t): 0.000 Batch (t): 0.408, 628.987/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:10:46 | INFO | Train Epoch: 0 [3507456/3655823 (96%)] Loss: 1.8596 (1.780) Data (t): 0.000 Batch (t): 0.407, 627.770/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:11:28 | INFO | Train Epoch: 0 [3533056/3655823 (97%)] Loss: 1.5420 (1.778) Data (t): 0.000 Batch (t): 0.417, 630.324/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:12:08 | INFO | Train Epoch: 0 [3558656/3655823 (97%)] Loss: 1.6101 (1.777) Data (t): 0.000 Batch (t): 0.407, 627.708/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:12:49 | INFO | Train Epoch: 0 [3584256/3655823 (98%)] Loss: 1.8772 (1.778) Data (t): 0.000 Batch (t): 0.408, 628.813/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:13:30 | INFO | Train Epoch: 0 [3609856/3655823 (99%)] Loss: 1.5998 (1.777) Data (t): 0.000 Batch (t): 0.408, 626.051/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:14:11 | INFO | Train Epoch: 0 [3635456/3655823 (99%)] Loss: 1.4080 (1.774) Data (t): 0.000 Batch (t): 0.407, 629.103/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:14:43 | INFO | Train Epoch: 0 [3655680/3655823 (100%)] Loss: 1.6069 (1.773) Data (t): 0.001 Batch (t): 0.412, 634.622/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:14:52 | INFO | Start epoch 1 -2024-09-02,21:14:54 | INFO | Train Epoch: 1 [ 256/3655823 (0%)] Loss: 1.5133 (1.513) Data (t): 1.453 Batch (t): 1.874, 136.629/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:15:35 | INFO | Train Epoch: 1 [ 25856/3655823 (1%)] Loss: 1.5142 (1.514) Data (t): 0.000 Batch (t): 0.410, 630.191/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:16:16 | INFO | Train Epoch: 1 [ 51456/3655823 (1%)] Loss: 1.5544 (1.527) Data (t): 0.001 Batch (t): 0.407, 627.702/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:16:56 | INFO | Train Epoch: 1 [ 77056/3655823 (2%)] Loss: 1.7478 (1.582) Data (t): 0.000 Batch (t): 0.407, 629.082/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:17:37 | INFO | Train Epoch: 1 [ 102656/3655823 (3%)] Loss: 1.3177 (1.529) Data (t): 0.000 Batch (t): 0.408, 628.874/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:18:18 | INFO | Train Epoch: 1 [ 128256/3655823 (4%)] Loss: 1.6253 (1.545) Data (t): 0.000 Batch (t): 0.410, 630.915/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:19:00 | INFO | Train Epoch: 1 [ 153856/3655823 (4%)] Loss: 1.3942 (1.524) Data (t): 0.000 Batch (t): 0.414, 625.376/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:19:40 | INFO | Train Epoch: 1 [ 179456/3655823 (5%)] Loss: 1.4307 (1.512) Data (t): 0.000 Batch (t): 0.408, 628.076/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:20:21 | INFO | Train Epoch: 1 [ 205056/3655823 (6%)] Loss: 1.5396 (1.515) Data (t): 0.000 Batch (t): 0.408, 627.494/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:21:02 | INFO | Train Epoch: 1 [ 230656/3655823 (6%)] Loss: 1.7573 (1.539) Data (t): 0.000 Batch (t): 0.407, 627.089/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:21:43 | INFO | Train Epoch: 1 [ 256256/3655823 (7%)] Loss: 1.5486 (1.540) Data (t): 0.000 Batch (t): 0.407, 628.877/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:22:24 | INFO | Train Epoch: 1 [ 281856/3655823 (8%)] Loss: 1.3610 (1.525) Data (t): 0.000 Batch (t): 0.417, 628.258/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:23:05 | INFO | Train Epoch: 1 [ 307456/3655823 (8%)] Loss: 1.7699 (1.544) Data (t): 0.000 Batch (t): 0.408, 629.525/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:23:46 | INFO | Train Epoch: 1 [ 333056/3655823 (9%)] Loss: 1.7270 (1.557) Data (t): 0.000 Batch (t): 0.408, 628.146/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:24:27 | INFO | Train Epoch: 1 [ 358656/3655823 (10%)] Loss: 1.5177 (1.555) Data (t): 0.001 Batch (t): 0.408, 627.366/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:25:07 | INFO | Train Epoch: 1 [ 384256/3655823 (11%)] Loss: 1.6982 (1.564) Data (t): 0.000 Batch (t): 0.407, 627.758/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:25:49 | INFO | Train Epoch: 1 [ 409856/3655823 (11%)] Loss: 1.5244 (1.561) Data (t): 0.000 Batch (t): 0.416, 628.672/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:26:30 | INFO | Train Epoch: 1 [ 435456/3655823 (12%)] Loss: 1.4700 (1.556) Data (t): 0.000 Batch (t): 0.407, 628.596/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:27:10 | INFO | Train Epoch: 1 [ 461056/3655823 (13%)] Loss: 1.4835 (1.552) Data (t): 0.000 Batch (t): 0.407, 628.762/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:27:51 | INFO | Train Epoch: 1 [ 486656/3655823 (13%)] Loss: 1.5312 (1.551) Data (t): 0.000 Batch (t): 0.407, 629.042/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:28:32 | INFO | Train Epoch: 1 [ 512256/3655823 (14%)] Loss: 1.3163 (1.540) Data (t): 0.000 Batch (t): 0.407, 628.484/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:29:13 | INFO | Train Epoch: 1 [ 537856/3655823 (15%)] Loss: 1.6038 (1.543) Data (t): 0.000 Batch (t): 0.416, 627.924/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:29:54 | INFO | Train Epoch: 1 [ 563456/3655823 (15%)] Loss: 1.4335 (1.538) Data (t): 0.000 Batch (t): 0.407, 628.898/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:30:35 | INFO | Train Epoch: 1 [ 589056/3655823 (16%)] Loss: 1.6874 (1.544) Data (t): 0.000 Batch (t): 0.407, 627.771/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:31:16 | INFO | Train Epoch: 1 [ 614656/3655823 (17%)] Loss: 1.6103 (1.547) Data (t): 0.000 Batch (t): 0.407, 622.736/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:31:56 | INFO | Train Epoch: 1 [ 640256/3655823 (18%)] Loss: 1.4827 (1.545) Data (t): 0.000 Batch (t): 0.407, 627.594/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:32:38 | INFO | Train Epoch: 1 [ 665856/3655823 (18%)] Loss: 1.5158 (1.544) Data (t): 0.000 Batch (t): 0.416, 631.575/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:33:19 | INFO | Train Epoch: 1 [ 691456/3655823 (19%)] Loss: 1.4753 (1.541) Data (t): 0.000 Batch (t): 0.407, 626.767/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:33:59 | INFO | Train Epoch: 1 [ 717056/3655823 (20%)] Loss: 1.6635 (1.545) Data (t): 0.000 Batch (t): 0.407, 628.659/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:34:40 | INFO | Train Epoch: 1 [ 742656/3655823 (20%)] Loss: 1.5481 (1.545) Data (t): 0.000 Batch (t): 0.407, 629.260/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:35:21 | INFO | Train Epoch: 1 [ 768256/3655823 (21%)] Loss: 1.3125 (1.538) Data (t): 0.000 Batch (t): 0.408, 627.366/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:36:02 | INFO | Train Epoch: 1 [ 793856/3655823 (22%)] Loss: 1.5203 (1.537) Data (t): 0.000 Batch (t): 0.414, 626.264/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:36:43 | INFO | Train Epoch: 1 [ 819456/3655823 (22%)] Loss: 1.6740 (1.542) Data (t): 0.000 Batch (t): 0.410, 628.629/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:37:24 | INFO | Train Epoch: 1 [ 845056/3655823 (23%)] Loss: 1.4071 (1.538) Data (t): 0.000 Batch (t): 0.407, 629.449/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:38:05 | INFO | Train Epoch: 1 [ 870656/3655823 (24%)] Loss: 1.5560 (1.538) Data (t): 0.000 Batch (t): 0.407, 630.243/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:38:45 | INFO | Train Epoch: 1 [ 896256/3655823 (25%)] Loss: 1.2346 (1.530) Data (t): 0.000 Batch (t): 0.407, 627.806/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:39:26 | INFO | Train Epoch: 1 [ 921856/3655823 (25%)] Loss: 1.4627 (1.528) Data (t): 0.000 Batch (t): 0.407, 630.867/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:40:08 | INFO | Train Epoch: 1 [ 947456/3655823 (26%)] Loss: 1.2894 (1.522) Data (t): 0.000 Batch (t): 0.417, 629.256/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:40:49 | INFO | Train Epoch: 1 [ 973056/3655823 (27%)] Loss: 1.5275 (1.522) Data (t): 0.000 Batch (t): 0.408, 626.342/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:41:29 | INFO | Train Epoch: 1 [ 998656/3655823 (27%)] Loss: 1.5467 (1.522) Data (t): 0.000 Batch (t): 0.407, 627.584/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:42:10 | INFO | Train Epoch: 1 [1024256/3655823 (28%)] Loss: 1.4740 (1.521) Data (t): 0.000 Batch (t): 0.408, 628.038/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:42:51 | INFO | Train Epoch: 1 [1049856/3655823 (29%)] Loss: 1.3797 (1.518) Data (t): 0.000 Batch (t): 0.407, 627.314/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:43:33 | INFO | Train Epoch: 1 [1075456/3655823 (29%)] Loss: 1.3461 (1.514) Data (t): 0.000 Batch (t): 0.417, 627.971/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:44:13 | INFO | Train Epoch: 1 [1101056/3655823 (30%)] Loss: 1.4789 (1.513) Data (t): 0.000 Batch (t): 0.408, 626.148/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:44:54 | INFO | Train Epoch: 1 [1126656/3655823 (31%)] Loss: 1.3646 (1.510) Data (t): 0.000 Batch (t): 0.407, 629.987/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:45:35 | INFO | Train Epoch: 1 [1152256/3655823 (32%)] Loss: 1.5140 (1.510) Data (t): 0.000 Batch (t): 0.407, 628.557/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:46:16 | INFO | Train Epoch: 1 [1177856/3655823 (32%)] Loss: 1.6683 (1.513) Data (t): 0.000 Batch (t): 0.407, 628.529/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:46:57 | INFO | Train Epoch: 1 [1203456/3655823 (33%)] Loss: 1.6725 (1.517) Data (t): 0.000 Batch (t): 0.417, 629.136/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:47:38 | INFO | Train Epoch: 1 [1229056/3655823 (34%)] Loss: 1.4948 (1.516) Data (t): 0.000 Batch (t): 0.407, 628.064/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:48:19 | INFO | Train Epoch: 1 [1254656/3655823 (34%)] Loss: 1.5949 (1.518) Data (t): 0.000 Batch (t): 0.407, 629.528/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:48:59 | INFO | Train Epoch: 1 [1280256/3655823 (35%)] Loss: 1.3180 (1.514) Data (t): 0.000 Batch (t): 0.408, 628.289/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:49:40 | INFO | Train Epoch: 1 [1305856/3655823 (36%)] Loss: 1.5062 (1.514) Data (t): 0.000 Batch (t): 0.407, 627.331/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:50:22 | INFO | Train Epoch: 1 [1331456/3655823 (36%)] Loss: 1.4016 (1.511) Data (t): 0.000 Batch (t): 0.417, 627.811/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:51:03 | INFO | Train Epoch: 1 [1357056/3655823 (37%)] Loss: 1.7230 (1.515) Data (t): 0.000 Batch (t): 0.407, 630.733/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:51:43 | INFO | Train Epoch: 1 [1382656/3655823 (38%)] Loss: 1.5396 (1.516) Data (t): 0.000 Batch (t): 0.407, 627.915/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:52:24 | INFO | Train Epoch: 1 [1408256/3655823 (39%)] Loss: 1.7449 (1.520) Data (t): 0.000 Batch (t): 0.407, 629.595/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:53:05 | INFO | Train Epoch: 1 [1433856/3655823 (39%)] Loss: 1.2845 (1.516) Data (t): 0.000 Batch (t): 0.407, 630.280/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:53:46 | INFO | Train Epoch: 1 [1459456/3655823 (40%)] Loss: 1.6151 (1.517) Data (t): 0.000 Batch (t): 0.413, 628.299/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:54:27 | INFO | Train Epoch: 1 [1485056/3655823 (41%)] Loss: 1.5740 (1.518) Data (t): 0.000 Batch (t): 0.409, 629.952/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:55:08 | INFO | Train Epoch: 1 [1510656/3655823 (41%)] Loss: 1.5132 (1.518) Data (t): 0.000 Batch (t): 0.407, 629.294/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:55:48 | INFO | Train Epoch: 1 [1536256/3655823 (42%)] Loss: 1.4155 (1.517) Data (t): 0.000 Batch (t): 0.407, 628.534/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:56:29 | INFO | Train Epoch: 1 [1561856/3655823 (43%)] Loss: 1.3554 (1.514) Data (t): 0.000 Batch (t): 0.407, 629.873/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:57:10 | INFO | Train Epoch: 1 [1587456/3655823 (43%)] Loss: 1.4103 (1.512) Data (t): 0.000 Batch (t): 0.411, 629.178/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:57:51 | INFO | Train Epoch: 1 [1613056/3655823 (44%)] Loss: 1.4684 (1.512) Data (t): 0.000 Batch (t): 0.412, 629.864/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:58:32 | INFO | Train Epoch: 1 [1638656/3655823 (45%)] Loss: 1.5633 (1.513) Data (t): 0.000 Batch (t): 0.407, 629.829/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:59:13 | INFO | Train Epoch: 1 [1664256/3655823 (46%)] Loss: 1.4915 (1.512) Data (t): 0.000 Batch (t): 0.407, 627.540/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,21:59:53 | INFO | Train Epoch: 1 [1689856/3655823 (46%)] Loss: 1.4593 (1.511) Data (t): 0.000 Batch (t): 0.407, 629.971/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,22:00:34 | INFO | Train Epoch: 1 [1715456/3655823 (47%)] Loss: 1.2689 (1.508) Data (t): 0.000 Batch (t): 0.407, 628.891/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,22:01:16 | INFO | Train Epoch: 1 [1741056/3655823 (48%)] Loss: 1.5227 (1.508) Data (t): 0.000 Batch (t): 0.416, 628.608/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,22:01:56 | INFO | Train Epoch: 1 [1766656/3655823 (48%)] Loss: 1.5385 (1.509) Data (t): 0.000 Batch (t): 0.407, 627.905/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,22:02:37 | INFO | Train Epoch: 1 [1792256/3655823 (49%)] Loss: 1.6290 (1.510) Data (t): 0.000 Batch (t): 0.407, 631.010/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,22:03:18 | INFO | Train Epoch: 1 [1817856/3655823 (50%)] Loss: 1.5324 (1.511) Data (t): 0.000 Batch (t): 0.407, 630.453/s LR: 0.000001 Logit Scale: 100.000 - V4 -2024-09-02,22:03:59 | INFO | Train Epoch: 1 [1843456/3655823 (50%)] Loss: 1.5188 (1.511) Data (t): 0.000 Batch (t): 0.407, 628.681/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:04:40 | INFO | Train Epoch: 1 [1869056/3655823 (51%)] Loss: 1.7214 (1.513) Data (t): 0.000 Batch (t): 0.416, 629.958/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:05:21 | INFO | Train Epoch: 1 [1894656/3655823 (52%)] Loss: 1.4878 (1.513) Data (t): 0.000 Batch (t): 0.407, 628.809/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:06:02 | INFO | Train Epoch: 1 [1920256/3655823 (53%)] Loss: 1.5888 (1.514) Data (t): 0.000 Batch (t): 0.407, 628.475/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:06:42 | INFO | Train Epoch: 1 [1945856/3655823 (53%)] Loss: 1.5318 (1.514) Data (t): 0.000 Batch (t): 0.407, 628.192/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:07:23 | INFO | Train Epoch: 1 [1971456/3655823 (54%)] Loss: 1.6754 (1.516) Data (t): 0.000 Batch (t): 0.407, 630.166/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:08:05 | INFO | Train Epoch: 1 [1997056/3655823 (55%)] Loss: 1.4976 (1.516) Data (t): 0.000 Batch (t): 0.414, 408.668/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:08:45 | INFO | Train Epoch: 1 [2022656/3655823 (55%)] Loss: 1.7855 (1.520) Data (t): 0.000 Batch (t): 0.407, 630.855/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:09:26 | INFO | Train Epoch: 1 [2048256/3655823 (56%)] Loss: 1.6878 (1.522) Data (t): 0.000 Batch (t): 0.407, 629.100/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:10:07 | INFO | Train Epoch: 1 [2073856/3655823 (57%)] Loss: 1.6600 (1.523) Data (t): 0.000 Batch (t): 0.407, 627.637/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:10:47 | INFO | Train Epoch: 1 [2099456/3655823 (57%)] Loss: 1.6056 (1.524) Data (t): 0.000 Batch (t): 0.408, 628.494/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:11:29 | INFO | Train Epoch: 1 [2125056/3655823 (58%)] Loss: 1.3115 (1.522) Data (t): 0.000 Batch (t): 0.414, 630.900/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:12:10 | INFO | Train Epoch: 1 [2150656/3655823 (59%)] Loss: 1.8129 (1.525) Data (t): 0.000 Batch (t): 0.410, 628.077/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:12:51 | INFO | Train Epoch: 1 [2176256/3655823 (60%)] Loss: 1.6818 (1.527) Data (t): 0.000 Batch (t): 0.408, 628.288/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:13:31 | INFO | Train Epoch: 1 [2201856/3655823 (60%)] Loss: 1.4452 (1.526) Data (t): 0.000 Batch (t): 0.407, 629.634/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:14:12 | INFO | Train Epoch: 1 [2227456/3655823 (61%)] Loss: 1.4680 (1.525) Data (t): 0.000 Batch (t): 0.407, 628.668/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:14:53 | INFO | Train Epoch: 1 [2253056/3655823 (62%)] Loss: 1.5947 (1.526) Data (t): 0.000 Batch (t): 0.414, 628.227/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:15:34 | INFO | Train Epoch: 1 [2278656/3655823 (62%)] Loss: 1.4808 (1.526) Data (t): 0.000 Batch (t): 0.409, 627.235/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:16:15 | INFO | Train Epoch: 1 [2304256/3655823 (63%)] Loss: 1.4525 (1.525) Data (t): 0.000 Batch (t): 0.407, 628.953/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:16:56 | INFO | Train Epoch: 1 [2329856/3655823 (64%)] Loss: 1.4756 (1.524) Data (t): 0.000 Batch (t): 0.407, 627.547/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:17:37 | INFO | Train Epoch: 1 [2355456/3655823 (64%)] Loss: 1.6125 (1.525) Data (t): 0.000 Batch (t): 0.407, 629.699/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:18:17 | INFO | Train Epoch: 1 [2381056/3655823 (65%)] Loss: 1.6759 (1.527) Data (t): 0.000 Batch (t): 0.410, 627.931/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:18:59 | INFO | Train Epoch: 1 [2406656/3655823 (66%)] Loss: 1.6390 (1.528) Data (t): 0.000 Batch (t): 0.414, 627.236/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:19:40 | INFO | Train Epoch: 1 [2432256/3655823 (67%)] Loss: 1.4739 (1.528) Data (t): 0.000 Batch (t): 0.407, 627.855/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:20:20 | INFO | Train Epoch: 1 [2457856/3655823 (67%)] Loss: 1.4514 (1.527) Data (t): 0.000 Batch (t): 0.407, 627.891/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:21:01 | INFO | Train Epoch: 1 [2483456/3655823 (68%)] Loss: 1.5166 (1.527) Data (t): 0.000 Batch (t): 0.407, 626.913/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:21:42 | INFO | Train Epoch: 1 [2509056/3655823 (69%)] Loss: 1.4762 (1.526) Data (t): 0.000 Batch (t): 0.408, 627.541/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:22:23 | INFO | Train Epoch: 1 [2534656/3655823 (69%)] Loss: 1.3142 (1.524) Data (t): 0.000 Batch (t): 0.414, 630.252/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:23:04 | INFO | Train Epoch: 1 [2560256/3655823 (70%)] Loss: 1.6721 (1.525) Data (t): 0.000 Batch (t): 0.409, 630.476/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:23:45 | INFO | Train Epoch: 1 [2585856/3655823 (71%)] Loss: 1.6706 (1.527) Data (t): 0.000 Batch (t): 0.407, 628.681/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:24:26 | INFO | Train Epoch: 1 [2611456/3655823 (71%)] Loss: 1.5446 (1.527) Data (t): 0.000 Batch (t): 0.407, 627.126/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:25:06 | INFO | Train Epoch: 1 [2637056/3655823 (72%)] Loss: 1.7021 (1.529) Data (t): 0.000 Batch (t): 0.407, 628.812/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:25:48 | INFO | Train Epoch: 1 [2662656/3655823 (73%)] Loss: 1.7364 (1.531) Data (t): 0.000 Batch (t): 0.414, 622.986/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:26:29 | INFO | Train Epoch: 1 [2688256/3655823 (74%)] Loss: 1.2895 (1.528) Data (t): 0.000 Batch (t): 0.409, 628.695/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:27:09 | INFO | Train Epoch: 1 [2713856/3655823 (74%)] Loss: 1.6429 (1.530) Data (t): 0.000 Batch (t): 0.407, 630.669/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:27:50 | INFO | Train Epoch: 1 [2739456/3655823 (75%)] Loss: 1.5169 (1.529) Data (t): 0.000 Batch (t): 0.407, 628.794/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:28:31 | INFO | Train Epoch: 1 [2765056/3655823 (76%)] Loss: 1.5358 (1.529) Data (t): 0.000 Batch (t): 0.407, 628.273/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:29:12 | INFO | Train Epoch: 1 [2790656/3655823 (76%)] Loss: 1.5837 (1.530) Data (t): 0.000 Batch (t): 0.414, 627.414/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:29:53 | INFO | Train Epoch: 1 [2816256/3655823 (77%)] Loss: 1.5221 (1.530) Data (t): 0.000 Batch (t): 0.409, 629.769/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:30:34 | INFO | Train Epoch: 1 [2841856/3655823 (78%)] Loss: 1.8568 (1.533) Data (t): 0.000 Batch (t): 0.407, 630.064/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:31:15 | INFO | Train Epoch: 1 [2867456/3655823 (78%)] Loss: 1.5648 (1.533) Data (t): 0.000 Batch (t): 0.407, 631.715/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:31:55 | INFO | Train Epoch: 1 [2893056/3655823 (79%)] Loss: 1.4747 (1.533) Data (t): 0.000 Batch (t): 0.407, 629.037/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:32:37 | INFO | Train Epoch: 1 [2918656/3655823 (80%)] Loss: 1.4372 (1.532) Data (t): 0.000 Batch (t): 0.414, 627.094/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:33:18 | INFO | Train Epoch: 1 [2944256/3655823 (81%)] Loss: 1.6138 (1.532) Data (t): 0.000 Batch (t): 0.410, 627.375/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:33:58 | INFO | Train Epoch: 1 [2969856/3655823 (81%)] Loss: 1.5428 (1.533) Data (t): 0.000 Batch (t): 0.407, 629.974/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:34:39 | INFO | Train Epoch: 1 [2995456/3655823 (82%)] Loss: 1.4792 (1.532) Data (t): 0.000 Batch (t): 0.407, 628.819/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:35:20 | INFO | Train Epoch: 1 [3021056/3655823 (83%)] Loss: 1.6329 (1.533) Data (t): 0.000 Batch (t): 0.407, 630.009/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:36:01 | INFO | Train Epoch: 1 [3046656/3655823 (83%)] Loss: 1.4424 (1.532) Data (t): 0.000 Batch (t): 0.414, 629.763/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:36:42 | INFO | Train Epoch: 1 [3072256/3655823 (84%)] Loss: 1.3678 (1.531) Data (t): 0.000 Batch (t): 0.407, 629.572/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:37:23 | INFO | Train Epoch: 1 [3097856/3655823 (85%)] Loss: 1.7346 (1.532) Data (t): 0.000 Batch (t): 0.410, 631.033/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:38:03 | INFO | Train Epoch: 1 [3123456/3655823 (85%)] Loss: 1.6369 (1.533) Data (t): 0.000 Batch (t): 0.407, 628.910/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:38:44 | INFO | Train Epoch: 1 [3149056/3655823 (86%)] Loss: 1.7221 (1.535) Data (t): 0.000 Batch (t): 0.407, 628.351/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:39:25 | INFO | Train Epoch: 1 [3174656/3655823 (87%)] Loss: 1.5437 (1.535) Data (t): 0.000 Batch (t): 0.412, 630.017/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:40:06 | INFO | Train Epoch: 1 [3200256/3655823 (88%)] Loss: 1.3392 (1.533) Data (t): 0.000 Batch (t): 0.410, 628.299/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:40:47 | INFO | Train Epoch: 1 [3225856/3655823 (88%)] Loss: 1.4120 (1.532) Data (t): 0.000 Batch (t): 0.409, 630.385/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:41:28 | INFO | Train Epoch: 1 [3251456/3655823 (89%)] Loss: 1.4983 (1.532) Data (t): 0.000 Batch (t): 0.407, 629.850/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:42:09 | INFO | Train Epoch: 1 [3277056/3655823 (90%)] Loss: 1.5001 (1.532) Data (t): 0.000 Batch (t): 0.407, 631.704/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:42:49 | INFO | Train Epoch: 1 [3302656/3655823 (90%)] Loss: 1.6518 (1.533) Data (t): 0.000 Batch (t): 0.409, 629.730/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:43:31 | INFO | Train Epoch: 1 [3328256/3655823 (91%)] Loss: 1.5092 (1.533) Data (t): 0.000 Batch (t): 0.412, 630.069/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:44:12 | INFO | Train Epoch: 1 [3353856/3655823 (92%)] Loss: 1.2884 (1.531) Data (t): 0.000 Batch (t): 0.409, 627.948/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:44:52 | INFO | Train Epoch: 1 [3379456/3655823 (92%)] Loss: 1.4368 (1.530) Data (t): 0.000 Batch (t): 0.407, 630.895/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:45:33 | INFO | Train Epoch: 1 [3405056/3655823 (93%)] Loss: 1.5422 (1.530) Data (t): 0.000 Batch (t): 0.407, 628.972/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:46:14 | INFO | Train Epoch: 1 [3430656/3655823 (94%)] Loss: 1.5070 (1.530) Data (t): 0.000 Batch (t): 0.407, 628.455/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:46:55 | INFO | Train Epoch: 1 [3456256/3655823 (95%)] Loss: 1.3101 (1.528) Data (t): 0.000 Batch (t): 0.414, 628.779/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:47:36 | INFO | Train Epoch: 1 [3481856/3655823 (95%)] Loss: 1.3650 (1.527) Data (t): 0.000 Batch (t): 0.409, 628.301/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:48:17 | INFO | Train Epoch: 1 [3507456/3655823 (96%)] Loss: 1.3703 (1.526) Data (t): 0.000 Batch (t): 0.407, 630.465/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:48:57 | INFO | Train Epoch: 1 [3533056/3655823 (97%)] Loss: 1.4965 (1.526) Data (t): 0.000 Batch (t): 0.407, 628.362/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:49:38 | INFO | Train Epoch: 1 [3558656/3655823 (97%)] Loss: 1.5122 (1.526) Data (t): 0.000 Batch (t): 0.407, 629.408/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:50:19 | INFO | Train Epoch: 1 [3584256/3655823 (98%)] Loss: 1.4414 (1.525) Data (t): 0.000 Batch (t): 0.414, 628.142/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:51:00 | INFO | Train Epoch: 1 [3609856/3655823 (99%)] Loss: 1.4188 (1.524) Data (t): 0.000 Batch (t): 0.409, 630.100/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:51:41 | INFO | Train Epoch: 1 [3635456/3655823 (99%)] Loss: 1.5303 (1.524) Data (t): 0.000 Batch (t): 0.407, 627.737/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:52:13 | INFO | Train Epoch: 1 [3655680/3655823 (100%)] Loss: 1.5484 (1.525) Data (t): 0.001 Batch (t): 0.407, 631.627/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:52:22 | INFO | Start epoch 2 -2024-09-02,22:52:24 | INFO | Train Epoch: 2 [ 256/3655823 (0%)] Loss: 1.4037 (1.404) Data (t): 1.370 Batch (t): 1.815, 141.077/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:53:04 | INFO | Train Epoch: 2 [ 25856/3655823 (1%)] Loss: 1.5089 (1.456) Data (t): 0.000 Batch (t): 0.407, 630.111/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:53:46 | INFO | Train Epoch: 2 [ 51456/3655823 (1%)] Loss: 1.5214 (1.478) Data (t): 0.000 Batch (t): 0.415, 628.223/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:54:27 | INFO | Train Epoch: 2 [ 77056/3655823 (2%)] Loss: 1.5636 (1.499) Data (t): 0.000 Batch (t): 0.407, 629.353/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:55:08 | INFO | Train Epoch: 2 [ 102656/3655823 (3%)] Loss: 1.6539 (1.530) Data (t): 0.000 Batch (t): 0.410, 630.132/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:55:48 | INFO | Train Epoch: 2 [ 128256/3655823 (4%)] Loss: 1.5802 (1.539) Data (t): 0.000 Batch (t): 0.407, 631.567/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:56:29 | INFO | Train Epoch: 2 [ 153856/3655823 (4%)] Loss: 1.5986 (1.547) Data (t): 0.000 Batch (t): 0.407, 628.568/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:57:11 | INFO | Train Epoch: 2 [ 179456/3655823 (5%)] Loss: 1.4930 (1.540) Data (t): 0.000 Batch (t): 0.414, 626.270/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:57:51 | INFO | Train Epoch: 2 [ 205056/3655823 (6%)] Loss: 1.4261 (1.528) Data (t): 0.000 Batch (t): 0.407, 630.380/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:58:32 | INFO | Train Epoch: 2 [ 230656/3655823 (6%)] Loss: 1.4926 (1.524) Data (t): 0.000 Batch (t): 0.409, 629.050/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:59:13 | INFO | Train Epoch: 2 [ 256256/3655823 (7%)] Loss: 1.7086 (1.541) Data (t): 0.000 Batch (t): 0.407, 629.610/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,22:59:54 | INFO | Train Epoch: 2 [ 281856/3655823 (8%)] Loss: 1.7055 (1.555) Data (t): 0.000 Batch (t): 0.407, 630.368/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:00:35 | INFO | Train Epoch: 2 [ 307456/3655823 (8%)] Loss: 1.3944 (1.542) Data (t): 0.000 Batch (t): 0.409, 629.897/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:01:16 | INFO | Train Epoch: 2 [ 333056/3655823 (9%)] Loss: 1.4867 (1.538) Data (t): 0.000 Batch (t): 0.412, 630.043/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:01:57 | INFO | Train Epoch: 2 [ 358656/3655823 (10%)] Loss: 1.5009 (1.536) Data (t): 0.000 Batch (t): 0.410, 629.868/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:02:37 | INFO | Train Epoch: 2 [ 384256/3655823 (11%)] Loss: 1.4124 (1.528) Data (t): 0.000 Batch (t): 0.407, 629.839/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:03:18 | INFO | Train Epoch: 2 [ 409856/3655823 (11%)] Loss: 1.6669 (1.536) Data (t): 0.000 Batch (t): 0.407, 629.113/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:03:59 | INFO | Train Epoch: 2 [ 435456/3655823 (12%)] Loss: 1.4738 (1.533) Data (t): 0.000 Batch (t): 0.407, 629.178/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:04:40 | INFO | Train Epoch: 2 [ 461056/3655823 (13%)] Loss: 1.7301 (1.543) Data (t): 0.000 Batch (t): 0.414, 629.342/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:05:21 | INFO | Train Epoch: 2 [ 486656/3655823 (13%)] Loss: 1.5073 (1.541) Data (t): 0.000 Batch (t): 0.410, 629.457/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:06:02 | INFO | Train Epoch: 2 [ 512256/3655823 (14%)] Loss: 1.4765 (1.538) Data (t): 0.000 Batch (t): 0.408, 627.737/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:06:43 | INFO | Train Epoch: 2 [ 537856/3655823 (15%)] Loss: 1.6323 (1.543) Data (t): 0.000 Batch (t): 0.407, 627.787/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:07:23 | INFO | Train Epoch: 2 [ 563456/3655823 (15%)] Loss: 1.4247 (1.537) Data (t): 0.000 Batch (t): 0.407, 629.128/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:08:05 | INFO | Train Epoch: 2 [ 589056/3655823 (16%)] Loss: 1.5308 (1.537) Data (t): 0.000 Batch (t): 0.414, 630.767/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:08:46 | INFO | Train Epoch: 2 [ 614656/3655823 (17%)] Loss: 1.2977 (1.528) Data (t): 0.000 Batch (t): 0.409, 628.541/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:09:26 | INFO | Train Epoch: 2 [ 640256/3655823 (18%)] Loss: 1.7215 (1.535) Data (t): 0.000 Batch (t): 0.407, 627.081/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:10:07 | INFO | Train Epoch: 2 [ 665856/3655823 (18%)] Loss: 1.5919 (1.537) Data (t): 0.000 Batch (t): 0.407, 627.969/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:10:48 | INFO | Train Epoch: 2 [ 691456/3655823 (19%)] Loss: 1.6819 (1.542) Data (t): 0.000 Batch (t): 0.407, 626.797/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:11:29 | INFO | Train Epoch: 2 [ 717056/3655823 (20%)] Loss: 1.5473 (1.543) Data (t): 0.000 Batch (t): 0.414, 626.998/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:12:10 | INFO | Train Epoch: 2 [ 742656/3655823 (20%)] Loss: 1.5662 (1.543) Data (t): 0.000 Batch (t): 0.407, 629.791/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:12:51 | INFO | Train Epoch: 2 [ 768256/3655823 (21%)] Loss: 1.2745 (1.535) Data (t): 0.000 Batch (t): 0.409, 629.149/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:13:32 | INFO | Train Epoch: 2 [ 793856/3655823 (22%)] Loss: 1.5888 (1.536) Data (t): 0.000 Batch (t): 0.407, 627.713/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:14:12 | INFO | Train Epoch: 2 [ 819456/3655823 (22%)] Loss: 1.5075 (1.535) Data (t): 0.000 Batch (t): 0.407, 629.456/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:14:54 | INFO | Train Epoch: 2 [ 845056/3655823 (23%)] Loss: 1.3259 (1.529) Data (t): 0.000 Batch (t): 0.414, 627.704/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:15:34 | INFO | Train Epoch: 2 [ 870656/3655823 (24%)] Loss: 1.4892 (1.528) Data (t): 0.000 Batch (t): 0.407, 629.108/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:16:15 | INFO | Train Epoch: 2 [ 896256/3655823 (25%)] Loss: 1.3673 (1.524) Data (t): 0.000 Batch (t): 0.410, 630.858/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:16:56 | INFO | Train Epoch: 2 [ 921856/3655823 (25%)] Loss: 1.4061 (1.521) Data (t): 0.000 Batch (t): 0.407, 627.890/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:17:37 | INFO | Train Epoch: 2 [ 947456/3655823 (26%)] Loss: 1.3169 (1.515) Data (t): 0.000 Batch (t): 0.407, 628.742/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:18:18 | INFO | Train Epoch: 2 [ 973056/3655823 (27%)] Loss: 1.4882 (1.514) Data (t): 0.000 Batch (t): 0.412, 627.743/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:18:59 | INFO | Train Epoch: 2 [ 998656/3655823 (27%)] Loss: 1.3831 (1.511) Data (t): 0.000 Batch (t): 0.409, 629.521/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:19:40 | INFO | Train Epoch: 2 [1024256/3655823 (28%)] Loss: 1.4389 (1.509) Data (t): 0.000 Batch (t): 0.409, 630.280/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:20:21 | INFO | Train Epoch: 2 [1049856/3655823 (29%)] Loss: 1.4462 (1.508) Data (t): 0.000 Batch (t): 0.407, 628.252/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:21:01 | INFO | Train Epoch: 2 [1075456/3655823 (29%)] Loss: 1.8087 (1.515) Data (t): 0.000 Batch (t): 0.407, 627.748/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:21:42 | INFO | Train Epoch: 2 [1101056/3655823 (30%)] Loss: 1.6658 (1.518) Data (t): 0.000 Batch (t): 0.410, 627.804/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:22:23 | INFO | Train Epoch: 2 [1126656/3655823 (31%)] Loss: 1.6089 (1.520) Data (t): 0.000 Batch (t): 0.411, 629.073/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:23:04 | INFO | Train Epoch: 2 [1152256/3655823 (32%)] Loss: 1.4291 (1.518) Data (t): 0.000 Batch (t): 0.409, 629.290/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:23:45 | INFO | Train Epoch: 2 [1177856/3655823 (32%)] Loss: 1.7743 (1.524) Data (t): 0.000 Batch (t): 0.407, 628.358/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:24:26 | INFO | Train Epoch: 2 [1203456/3655823 (33%)] Loss: 1.5225 (1.524) Data (t): 0.000 Batch (t): 0.407, 627.118/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:25:06 | INFO | Train Epoch: 2 [1229056/3655823 (34%)] Loss: 1.6578 (1.527) Data (t): 0.000 Batch (t): 0.407, 631.498/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:25:48 | INFO | Train Epoch: 2 [1254656/3655823 (34%)] Loss: 1.4961 (1.526) Data (t): 0.000 Batch (t): 0.414, 627.631/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:26:29 | INFO | Train Epoch: 2 [1280256/3655823 (35%)] Loss: 1.4220 (1.524) Data (t): 0.000 Batch (t): 0.407, 629.668/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:27:10 | INFO | Train Epoch: 2 [1305856/3655823 (36%)] Loss: 1.7906 (1.529) Data (t): 0.000 Batch (t): 0.410, 628.641/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:27:50 | INFO | Train Epoch: 2 [1331456/3655823 (36%)] Loss: 1.5221 (1.529) Data (t): 0.000 Batch (t): 0.407, 628.897/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:28:31 | INFO | Train Epoch: 2 [1357056/3655823 (37%)] Loss: 1.2659 (1.524) Data (t): 0.000 Batch (t): 0.407, 630.465/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:29:12 | INFO | Train Epoch: 2 [1382656/3655823 (38%)] Loss: 1.7159 (1.527) Data (t): 0.000 Batch (t): 0.413, 628.141/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:29:53 | INFO | Train Epoch: 2 [1408256/3655823 (39%)] Loss: 1.3950 (1.525) Data (t): 0.000 Batch (t): 0.407, 627.700/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:30:34 | INFO | Train Epoch: 2 [1433856/3655823 (39%)] Loss: 1.5019 (1.525) Data (t): 0.000 Batch (t): 0.409, 628.393/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:31:15 | INFO | Train Epoch: 2 [1459456/3655823 (40%)] Loss: 1.4502 (1.523) Data (t): 0.000 Batch (t): 0.407, 630.965/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:31:55 | INFO | Train Epoch: 2 [1485056/3655823 (41%)] Loss: 1.4851 (1.523) Data (t): 0.000 Batch (t): 0.407, 629.367/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:32:37 | INFO | Train Epoch: 2 [1510656/3655823 (41%)] Loss: 1.3077 (1.519) Data (t): 0.000 Batch (t): 0.414, 629.548/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:33:17 | INFO | Train Epoch: 2 [1536256/3655823 (42%)] Loss: 1.2835 (1.515) Data (t): 0.000 Batch (t): 0.407, 628.345/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:33:58 | INFO | Train Epoch: 2 [1561856/3655823 (43%)] Loss: 1.3746 (1.513) Data (t): 0.000 Batch (t): 0.409, 626.895/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:34:39 | INFO | Train Epoch: 2 [1587456/3655823 (43%)] Loss: 1.4182 (1.512) Data (t): 0.000 Batch (t): 0.407, 628.771/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:35:20 | INFO | Train Epoch: 2 [1613056/3655823 (44%)] Loss: 1.6644 (1.514) Data (t): 0.000 Batch (t): 0.407, 627.470/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:36:01 | INFO | Train Epoch: 2 [1638656/3655823 (45%)] Loss: 1.5005 (1.514) Data (t): 0.000 Batch (t): 0.414, 629.166/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:36:42 | INFO | Train Epoch: 2 [1664256/3655823 (46%)] Loss: 1.3908 (1.512) Data (t): 0.000 Batch (t): 0.407, 628.847/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:37:23 | INFO | Train Epoch: 2 [1689856/3655823 (46%)] Loss: 1.3752 (1.510) Data (t): 0.000 Batch (t): 0.409, 628.394/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:38:03 | INFO | Train Epoch: 2 [1715456/3655823 (47%)] Loss: 1.3061 (1.507) Data (t): 0.000 Batch (t): 0.407, 629.893/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:38:44 | INFO | Train Epoch: 2 [1741056/3655823 (48%)] Loss: 1.6684 (1.509) Data (t): 0.000 Batch (t): 0.407, 629.377/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:39:25 | INFO | Train Epoch: 2 [1766656/3655823 (48%)] Loss: 1.5123 (1.509) Data (t): 0.000 Batch (t): 0.412, 627.347/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:40:06 | INFO | Train Epoch: 2 [1792256/3655823 (49%)] Loss: 1.5436 (1.510) Data (t): 0.000 Batch (t): 0.409, 629.354/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:40:47 | INFO | Train Epoch: 2 [1817856/3655823 (50%)] Loss: 1.3315 (1.507) Data (t): 0.000 Batch (t): 0.409, 631.814/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:41:28 | INFO | Train Epoch: 2 [1843456/3655823 (50%)] Loss: 1.4653 (1.507) Data (t): 0.000 Batch (t): 0.407, 630.276/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:42:09 | INFO | Train Epoch: 2 [1869056/3655823 (51%)] Loss: 1.6304 (1.508) Data (t): 0.000 Batch (t): 0.407, 628.864/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:42:50 | INFO | Train Epoch: 2 [1894656/3655823 (52%)] Loss: 1.6257 (1.510) Data (t): 0.000 Batch (t): 0.411, 629.561/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:43:31 | INFO | Train Epoch: 2 [1920256/3655823 (53%)] Loss: 1.4509 (1.509) Data (t): 0.000 Batch (t): 0.409, 629.451/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:44:11 | INFO | Train Epoch: 2 [1945856/3655823 (53%)] Loss: 1.6384 (1.511) Data (t): 0.000 Batch (t): 0.407, 628.176/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:44:52 | INFO | Train Epoch: 2 [1971456/3655823 (54%)] Loss: 1.7136 (1.513) Data (t): 0.000 Batch (t): 0.410, 627.612/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:45:33 | INFO | Train Epoch: 2 [1997056/3655823 (55%)] Loss: 1.4535 (1.513) Data (t): 0.000 Batch (t): 0.407, 630.598/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:46:14 | INFO | Train Epoch: 2 [2022656/3655823 (55%)] Loss: 1.4362 (1.512) Data (t): 0.000 Batch (t): 0.407, 630.524/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:46:55 | INFO | Train Epoch: 2 [2048256/3655823 (56%)] Loss: 1.5898 (1.513) Data (t): 0.000 Batch (t): 0.414, 630.467/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:47:36 | INFO | Train Epoch: 2 [2073856/3655823 (57%)] Loss: 1.5890 (1.514) Data (t): 0.000 Batch (t): 0.407, 629.275/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:48:17 | INFO | Train Epoch: 2 [2099456/3655823 (57%)] Loss: 1.3375 (1.511) Data (t): 0.000 Batch (t): 0.409, 630.843/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:48:57 | INFO | Train Epoch: 2 [2125056/3655823 (58%)] Loss: 1.5884 (1.512) Data (t): 0.000 Batch (t): 0.407, 629.078/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:49:38 | INFO | Train Epoch: 2 [2150656/3655823 (59%)] Loss: 1.4484 (1.512) Data (t): 0.000 Batch (t): 0.407, 628.168/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:50:20 | INFO | Train Epoch: 2 [2176256/3655823 (60%)] Loss: 1.4590 (1.511) Data (t): 0.000 Batch (t): 0.414, 627.246/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:51:00 | INFO | Train Epoch: 2 [2201856/3655823 (60%)] Loss: 1.4026 (1.510) Data (t): 0.000 Batch (t): 0.407, 629.318/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:51:41 | INFO | Train Epoch: 2 [2227456/3655823 (61%)] Loss: 1.6156 (1.511) Data (t): 0.000 Batch (t): 0.410, 627.409/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:52:22 | INFO | Train Epoch: 2 [2253056/3655823 (62%)] Loss: 1.7187 (1.513) Data (t): 0.000 Batch (t): 0.407, 630.345/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:53:03 | INFO | Train Epoch: 2 [2278656/3655823 (62%)] Loss: 1.4741 (1.513) Data (t): 0.000 Batch (t): 0.407, 628.182/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:53:44 | INFO | Train Epoch: 2 [2304256/3655823 (63%)] Loss: 1.4681 (1.512) Data (t): 0.000 Batch (t): 0.415, 627.790/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:54:25 | INFO | Train Epoch: 2 [2329856/3655823 (64%)] Loss: 1.3857 (1.511) Data (t): 0.000 Batch (t): 0.408, 629.276/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:55:06 | INFO | Train Epoch: 2 [2355456/3655823 (64%)] Loss: 1.5978 (1.512) Data (t): 0.000 Batch (t): 0.409, 627.194/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:55:47 | INFO | Train Epoch: 2 [2381056/3655823 (65%)] Loss: 1.6680 (1.514) Data (t): 0.000 Batch (t): 0.407, 628.069/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:56:27 | INFO | Train Epoch: 2 [2406656/3655823 (66%)] Loss: 1.5529 (1.514) Data (t): 0.000 Batch (t): 0.407, 630.765/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:57:09 | INFO | Train Epoch: 2 [2432256/3655823 (67%)] Loss: 1.3681 (1.512) Data (t): 0.000 Batch (t): 0.413, 630.806/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:57:49 | INFO | Train Epoch: 2 [2457856/3655823 (67%)] Loss: 1.5440 (1.513) Data (t): 0.000 Batch (t): 0.407, 628.749/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:58:30 | INFO | Train Epoch: 2 [2483456/3655823 (68%)] Loss: 1.4075 (1.512) Data (t): 0.000 Batch (t): 0.407, 628.206/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:59:11 | INFO | Train Epoch: 2 [2509056/3655823 (69%)] Loss: 1.8254 (1.515) Data (t): 0.000 Batch (t): 0.410, 628.974/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-02,23:59:52 | INFO | Train Epoch: 2 [2534656/3655823 (69%)] Loss: 1.6677 (1.516) Data (t): 0.000 Batch (t): 0.408, 626.462/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-03,00:00:33 | INFO | Train Epoch: 2 [2560256/3655823 (70%)] Loss: 1.4165 (1.515) Data (t): 0.000 Batch (t): 0.412, 629.517/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-03,00:01:14 | INFO | Train Epoch: 2 [2585856/3655823 (71%)] Loss: 1.7347 (1.518) Data (t): 0.000 Batch (t): 0.410, 629.107/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-03,00:01:55 | INFO | Train Epoch: 2 [2611456/3655823 (71%)] Loss: 1.2317 (1.515) Data (t): 0.000 Batch (t): 0.407, 630.817/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-03,00:02:36 | INFO | Train Epoch: 2 [2637056/3655823 (72%)] Loss: 1.5369 (1.515) Data (t): 0.000 Batch (t): 0.410, 625.949/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-03,00:03:16 | INFO | Train Epoch: 2 [2662656/3655823 (73%)] Loss: 1.4907 (1.515) Data (t): 0.000 Batch (t): 0.407, 629.731/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-03,00:03:58 | INFO | Train Epoch: 2 [2688256/3655823 (74%)] Loss: 1.4255 (1.514) Data (t): 0.000 Batch (t): 0.412, 398.724/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-03,00:04:39 | INFO | Train Epoch: 2 [2713856/3655823 (74%)] Loss: 1.4123 (1.513) Data (t): 0.000 Batch (t): 0.410, 631.559/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-03,00:05:19 | INFO | Train Epoch: 2 [2739456/3655823 (75%)] Loss: 1.5758 (1.514) Data (t): 0.000 Batch (t): 0.407, 628.544/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-03,00:06:00 | INFO | Train Epoch: 2 [2765056/3655823 (76%)] Loss: 1.4495 (1.513) Data (t): 0.000 Batch (t): 0.409, 626.030/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-03,00:06:41 | INFO | Train Epoch: 2 [2790656/3655823 (76%)] Loss: 1.5304 (1.513) Data (t): 0.000 Batch (t): 0.407, 627.283/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-03,00:07:22 | INFO | Train Epoch: 2 [2816256/3655823 (77%)] Loss: 1.4701 (1.513) Data (t): 0.000 Batch (t): 0.410, 629.953/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-03,00:08:03 | INFO | Train Epoch: 2 [2841856/3655823 (78%)] Loss: 1.5763 (1.513) Data (t): 0.000 Batch (t): 0.412, 627.543/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-03,00:08:44 | INFO | Train Epoch: 2 [2867456/3655823 (78%)] Loss: 1.7433 (1.515) Data (t): 0.000 Batch (t): 0.407, 627.613/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-03,00:09:25 | INFO | Train Epoch: 2 [2893056/3655823 (79%)] Loss: 1.3988 (1.514) Data (t): 0.000 Batch (t): 0.410, 630.525/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-03,00:10:06 | INFO | Train Epoch: 2 [2918656/3655823 (80%)] Loss: 1.4154 (1.513) Data (t): 0.000 Batch (t): 0.408, 627.753/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-03,00:10:46 | INFO | Train Epoch: 2 [2944256/3655823 (81%)] Loss: 1.3944 (1.512) Data (t): 0.000 Batch (t): 0.408, 627.055/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-03,00:11:28 | INFO | Train Epoch: 2 [2969856/3655823 (81%)] Loss: 1.5659 (1.513) Data (t): 0.000 Batch (t): 0.414, 629.686/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-03,00:12:09 | INFO | Train Epoch: 2 [2995456/3655823 (82%)] Loss: 1.5505 (1.513) Data (t): 0.000 Batch (t): 0.408, 630.069/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-03,00:12:49 | INFO | Train Epoch: 2 [3021056/3655823 (83%)] Loss: 1.5070 (1.513) Data (t): 0.000 Batch (t): 0.407, 628.991/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-03,00:13:30 | INFO | Train Epoch: 2 [3046656/3655823 (83%)] Loss: 1.5398 (1.513) Data (t): 0.000 Batch (t): 0.410, 629.726/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-03,00:14:11 | INFO | Train Epoch: 2 [3072256/3655823 (84%)] Loss: 1.3857 (1.512) Data (t): 0.000 Batch (t): 0.407, 629.560/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-03,00:14:52 | INFO | Train Epoch: 2 [3097856/3655823 (85%)] Loss: 1.5881 (1.513) Data (t): 0.000 Batch (t): 0.414, 631.045/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-03,00:15:33 | INFO | Train Epoch: 2 [3123456/3655823 (85%)] Loss: 1.5037 (1.513) Data (t): 0.000 Batch (t): 0.407, 629.088/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-03,00:16:14 | INFO | Train Epoch: 2 [3149056/3655823 (86%)] Loss: 1.4014 (1.512) Data (t): 0.001 Batch (t): 0.407, 630.044/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-03,00:16:55 | INFO | Train Epoch: 2 [3174656/3655823 (87%)] Loss: 1.2179 (1.510) Data (t): 0.000 Batch (t): 0.410, 629.206/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-03,00:17:36 | INFO | Train Epoch: 2 [3200256/3655823 (88%)] Loss: 1.3645 (1.508) Data (t): 0.000 Batch (t): 0.407, 627.603/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-03,00:18:17 | INFO | Train Epoch: 2 [3225856/3655823 (88%)] Loss: 1.5680 (1.509) Data (t): 0.000 Batch (t): 0.412, 627.229/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-03,00:18:58 | INFO | Train Epoch: 2 [3251456/3655823 (89%)] Loss: 1.3383 (1.508) Data (t): 0.000 Batch (t): 0.410, 626.713/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-03,00:19:39 | INFO | Train Epoch: 2 [3277056/3655823 (90%)] Loss: 1.3487 (1.506) Data (t): 0.000 Batch (t): 0.408, 626.343/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-03,00:20:20 | INFO | Train Epoch: 2 [3302656/3655823 (90%)] Loss: 1.7396 (1.508) Data (t): 0.000 Batch (t): 0.410, 630.255/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-03,00:21:00 | INFO | Train Epoch: 2 [3328256/3655823 (91%)] Loss: 1.5906 (1.509) Data (t): 0.000 Batch (t): 0.408, 626.238/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-03,00:21:41 | INFO | Train Epoch: 2 [3353856/3655823 (92%)] Loss: 1.3907 (1.508) Data (t): 0.000 Batch (t): 0.410, 625.774/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-03,00:22:23 | INFO | Train Epoch: 2 [3379456/3655823 (92%)] Loss: 1.7100 (1.509) Data (t): 0.000 Batch (t): 0.412, 627.232/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-03,00:23:03 | INFO | Train Epoch: 2 [3405056/3655823 (93%)] Loss: 1.5588 (1.510) Data (t): 0.000 Batch (t): 0.408, 629.864/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-03,00:23:44 | INFO | Train Epoch: 2 [3430656/3655823 (94%)] Loss: 1.4719 (1.510) Data (t): 0.000 Batch (t): 0.410, 626.668/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-03,00:24:25 | INFO | Train Epoch: 2 [3456256/3655823 (95%)] Loss: 1.5265 (1.510) Data (t): 0.000 Batch (t): 0.407, 626.014/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-03,00:25:06 | INFO | Train Epoch: 2 [3481856/3655823 (95%)] Loss: 1.4344 (1.509) Data (t): 0.000 Batch (t): 0.410, 628.206/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-03,00:25:47 | INFO | Train Epoch: 2 [3507456/3655823 (96%)] Loss: 1.4098 (1.508) Data (t): 0.000 Batch (t): 0.412, 627.392/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-03,00:26:28 | INFO | Train Epoch: 2 [3533056/3655823 (97%)] Loss: 1.4200 (1.508) Data (t): 0.000 Batch (t): 0.408, 626.110/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-03,00:27:09 | INFO | Train Epoch: 2 [3558656/3655823 (97%)] Loss: 1.5582 (1.508) Data (t): 0.000 Batch (t): 0.408, 626.130/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-03,00:27:50 | INFO | Train Epoch: 2 [3584256/3655823 (98%)] Loss: 1.6104 (1.509) Data (t): 0.000 Batch (t): 0.409, 631.655/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-03,00:28:30 | INFO | Train Epoch: 2 [3609856/3655823 (99%)] Loss: 1.4446 (1.508) Data (t): 0.000 Batch (t): 0.407, 627.898/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-03,00:29:12 | INFO | Train Epoch: 2 [3635456/3655823 (99%)] Loss: 1.3426 (1.507) Data (t): 0.000 Batch (t): 0.414, 627.434/s LR: 0.000000 Logit Scale: 100.000 - V4 -2024-09-03,00:29:44 | INFO | Train Epoch: 2 [3655680/3655823 (100%)] Loss: 1.1659 (1.505) Data (t): 0.001 Batch (t): 0.407, 631.296/s LR: 0.000000 Logit Scale: 100.000 - V4 diff --git a/data/trained_openclip/negative_logs/plotqa_v2/2024_09_02-19_36_58-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/params.txt b/data/trained_openclip/negative_logs/plotqa_v2/2024_09_02-19_36_58-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/params.txt deleted file mode 100644 index c8d2cc7438e4c15b167bc5f9ca0fe93128d9723a..0000000000000000000000000000000000000000 --- a/data/trained_openclip/negative_logs/plotqa_v2/2024_09_02-19_36_58-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/params.txt +++ /dev/null @@ -1,67 +0,0 @@ -batch_size: 64 -beta1: 0.9 -beta2: 0.98 -checkpoint_path: /project/deemreason/junteng/Vision4Math/train_clip/negative_logs/plotqa_v2/2024_09_02-19_36_58-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/checkpoints -copy_codebase: False -csv_caption_key: caption -csv_hard_captions_key: neg_caption -csv_img_key: img_path -csv_separator: , -dataset_resampled: False -dataset_type: csv -ddp_static_graph: False -debug: False -device: cuda:0 -dist_backend: nccl -dist_url: env:// -distributed: True -epochs: 3 -eps: 1e-06 -force_quick_gelu: True -gather_with_grad: False -grad_checkpointing: False -horovod: False -imagenet_v2: None -imagenet_val: None -local_loss: False -local_rank: 0 -lock_image: False -lock_image_freeze_bn_stats: False -lock_image_unlocked_groups: 0 -log_level: 20 -log_local: False -log_path: /project/deemreason/junteng/Vision4Math/train_clip/negative_logs/plotqa_v2/2024_09_02-19_36_58-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/out.log -logs: /project/deemreason/junteng/Vision4Math/train_clip/negative_logs/plotqa_v2 -lr: 1e-06 -model: ViT-L-14-336 -name: 2024_09_02-19_36_58-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp -no_set_device_rank: False -norm_gradient_clip: None -precision: amp -pretrained: /project/deemreason/junteng/Vision4Math/data/openclip-vit-14-336/openclip_model.pt -pretrained_image: False -rank: 0 -report_to: wandb -resume: None -save_frequency: 1 -save_most_recent: False -seed: 0 -skip_scheduler: False -tensorboard: False -tensorboard_path: -torchscript: False -trace: False -train_data: /project/deemreason/junteng/Vision4Math/csv_data/plotqa_train_v2.csv -train_num_samples: None -use_bn_sync: False -val_data: None -val_frequency: 1 -val_num_samples: None -wandb: True -wandb_notes: -wandb_project: open-clip-sum -warmup: 0 -wd: 0.1 -workers: 4 -world_size: 4 -zeroshot_frequency: 2 diff --git a/data/trained_openclip/no_hard_negative_logs/plotqa_v2/2024_09_02-17_00_26-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/checkpoints/epoch_1.pt b/data/trained_openclip/no_hard_negative_logs/plotqa_v2/2024_09_02-17_00_26-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/checkpoints/epoch_1.pt deleted file mode 100644 index 0a794a0941d4ddd27b40b759b92e67d3ab285541..0000000000000000000000000000000000000000 --- a/data/trained_openclip/no_hard_negative_logs/plotqa_v2/2024_09_02-17_00_26-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/checkpoints/epoch_1.pt +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:e520c5f14ebfa4aaa17603db60fb1192698c292dd6746fd865d58571652b4c9b -size 5135895330 diff --git a/data/trained_openclip/no_hard_negative_logs/plotqa_v2/2024_09_02-17_00_26-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/checkpoints/epoch_2.pt b/data/trained_openclip/no_hard_negative_logs/plotqa_v2/2024_09_02-17_00_26-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/checkpoints/epoch_2.pt deleted file mode 100644 index 3c70b4c1dd95b84cafbe83e036b3092fb1f497a3..0000000000000000000000000000000000000000 --- a/data/trained_openclip/no_hard_negative_logs/plotqa_v2/2024_09_02-17_00_26-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/checkpoints/epoch_2.pt +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:43851e0e37bd2295f25bfc7dfe490fed761f49c7ed6f7e3f1cab64b70134739c -size 5135895394 diff --git a/data/trained_openclip/no_hard_negative_logs/plotqa_v2/2024_09_02-17_00_26-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/checkpoints/epoch_3.pt b/data/trained_openclip/no_hard_negative_logs/plotqa_v2/2024_09_02-17_00_26-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/checkpoints/epoch_3.pt deleted file mode 100644 index af420954e5c5815948553da6ce95d242b6e760c2..0000000000000000000000000000000000000000 --- a/data/trained_openclip/no_hard_negative_logs/plotqa_v2/2024_09_02-17_00_26-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/checkpoints/epoch_3.pt +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:2c31bb3d275dcca08bbcc12c275792dc1f711f0bac3b8768f98cd5da40791b84 -size 5135895394 diff --git a/data/trained_openclip/no_hard_negative_logs/plotqa_v2/2024_09_02-17_00_26-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/out.log b/data/trained_openclip/no_hard_negative_logs/plotqa_v2/2024_09_02-17_00_26-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/out.log deleted file mode 100644 index b97eaed1305f85e0abfafdf669fa9ca3e0d9d200..0000000000000000000000000000000000000000 --- a/data/trained_openclip/no_hard_negative_logs/plotqa_v2/2024_09_02-17_00_26-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/out.log +++ /dev/null @@ -1,582 +0,0 @@ -2024-09-02,17:00:30 | INFO | Running in distributed mode with multiple processes. Device: cuda:0.Process (global: 0, local 0), total 4. -2024-09-02,17:00:30 | INFO | Loaded ViT-L-14-336 model config. -2024-09-02,17:00:33 | INFO | Loading pretrained ViT-L-14-336 weights (/project/deemreason/junteng/Vision4Math/data/openclip-vit-14-336/openclip_model.pt). -2024-09-02,17:00:44 | INFO | Model: -2024-09-02,17:00:44 | INFO | CLIP( - (visual): VisionTransformer( - (conv1): Conv2d(3, 1024, kernel_size=(14, 14), stride=(14, 14), bias=False) - (patch_dropout): Identity() - (ln_pre): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) - (transformer): Transformer( - (resblocks): ModuleList( - (0-23): 24 x ResidualAttentionBlock( - (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) - (attn): MultiheadAttention( - (out_proj): NonDynamicallyQuantizableLinear(in_features=1024, out_features=1024, bias=True) - ) - (ls_1): Identity() - (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) - (mlp): Sequential( - (c_fc): Linear(in_features=1024, out_features=4096, bias=True) - (gelu): QuickGELU() - (c_proj): Linear(in_features=4096, out_features=1024, bias=True) - ) - (ls_2): Identity() - ) - ) - ) - (ln_post): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) - ) - (transformer): Transformer( - (resblocks): ModuleList( - (0-11): 12 x ResidualAttentionBlock( - (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) - (attn): MultiheadAttention( - (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) - ) - (ls_1): Identity() - (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) - (mlp): Sequential( - (c_fc): Linear(in_features=768, out_features=3072, bias=True) - (gelu): QuickGELU() - (c_proj): Linear(in_features=3072, out_features=768, bias=True) - ) - (ls_2): Identity() - ) - ) - ) - (token_embedding): Embedding(49408, 768) - (ln_final): LayerNorm((768,), eps=1e-05, elementwise_affine=True) -) -2024-09-02,17:00:44 | INFO | Params: -2024-09-02,17:00:44 | INFO | accum_freq: 1 -2024-09-02,17:00:44 | INFO | aug_cfg: {} -2024-09-02,17:00:44 | INFO | batch_size: 64 -2024-09-02,17:00:44 | INFO | beta1: 0.9 -2024-09-02,17:00:44 | INFO | beta2: 0.98 -2024-09-02,17:00:44 | INFO | checkpoint_path: /project/deemreason/junteng/Vision4Math/train_clip/no_hard_negative_logs/plotqa_v2/2024_09_02-17_00_26-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/checkpoints -2024-09-02,17:00:44 | INFO | coca_caption_loss_weight: 2.0 -2024-09-02,17:00:44 | INFO | coca_contrastive_loss_weight: 1.0 -2024-09-02,17:00:44 | INFO | copy_codebase: False -2024-09-02,17:00:44 | INFO | csv_caption_key: caption -2024-09-02,17:00:44 | INFO | csv_img_key: img_path -2024-09-02,17:00:44 | INFO | csv_separator: , -2024-09-02,17:00:44 | INFO | dataset_resampled: False -2024-09-02,17:00:44 | INFO | dataset_type: csv -2024-09-02,17:00:44 | INFO | ddp_static_graph: False -2024-09-02,17:00:44 | INFO | debug: False -2024-09-02,17:00:44 | INFO | delete_previous_checkpoint: False -2024-09-02,17:00:44 | INFO | device: cuda:0 -2024-09-02,17:00:44 | INFO | dist_backend: nccl -2024-09-02,17:00:44 | INFO | dist_url: env:// -2024-09-02,17:00:44 | INFO | distill: False -2024-09-02,17:00:44 | INFO | distill_model: None -2024-09-02,17:00:44 | INFO | distill_pretrained: None -2024-09-02,17:00:44 | INFO | distributed: True -2024-09-02,17:00:44 | INFO | epochs: 3 -2024-09-02,17:00:44 | INFO | epochs_cooldown: None -2024-09-02,17:00:44 | INFO | eps: 1e-06 -2024-09-02,17:00:44 | INFO | force_custom_text: False -2024-09-02,17:00:44 | INFO | force_image_size: None -2024-09-02,17:00:44 | INFO | force_patch_dropout: None -2024-09-02,17:00:44 | INFO | force_quick_gelu: True -2024-09-02,17:00:44 | INFO | gather_with_grad: False -2024-09-02,17:00:44 | INFO | grad_checkpointing: False -2024-09-02,17:00:44 | INFO | grad_clip_norm: None -2024-09-02,17:00:44 | INFO | horovod: False -2024-09-02,17:00:44 | INFO | image_interpolation: None -2024-09-02,17:00:44 | INFO | image_mean: None -2024-09-02,17:00:44 | INFO | image_resize_mode: None -2024-09-02,17:00:44 | INFO | image_std: None -2024-09-02,17:00:44 | INFO | imagenet_v2: None -2024-09-02,17:00:44 | INFO | imagenet_val: None -2024-09-02,17:00:44 | INFO | local_loss: False -2024-09-02,17:00:44 | INFO | local_rank: 0 -2024-09-02,17:00:44 | INFO | lock_image: False -2024-09-02,17:00:44 | INFO | lock_image_freeze_bn_stats: False -2024-09-02,17:00:44 | INFO | lock_image_unlocked_groups: 0 -2024-09-02,17:00:44 | INFO | lock_text: False -2024-09-02,17:00:44 | INFO | lock_text_freeze_layer_norm: False -2024-09-02,17:00:44 | INFO | lock_text_unlocked_layers: 0 -2024-09-02,17:00:44 | INFO | log_every_n_steps: 100 -2024-09-02,17:00:44 | INFO | log_level: 20 -2024-09-02,17:00:44 | INFO | log_local: False -2024-09-02,17:00:44 | INFO | log_path: /project/deemreason/junteng/Vision4Math/train_clip/no_hard_negative_logs/plotqa_v2/2024_09_02-17_00_26-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/out.log -2024-09-02,17:00:44 | INFO | logs: /project/deemreason/junteng/Vision4Math/train_clip/no_hard_negative_logs/plotqa_v2 -2024-09-02,17:00:44 | INFO | lr: 1e-06 -2024-09-02,17:00:44 | INFO | lr_cooldown_end: 0.0 -2024-09-02,17:00:44 | INFO | lr_cooldown_power: 1.0 -2024-09-02,17:00:44 | INFO | lr_scheduler: cosine -2024-09-02,17:00:44 | INFO | model: ViT-L-14-336 -2024-09-02,17:00:44 | INFO | name: 2024_09_02-17_00_26-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp -2024-09-02,17:00:44 | INFO | no_set_device_rank: False -2024-09-02,17:00:44 | INFO | precision: amp -2024-09-02,17:00:44 | INFO | pretrained: /project/deemreason/junteng/Vision4Math/data/openclip-vit-14-336/openclip_model.pt -2024-09-02,17:00:44 | INFO | pretrained_image: False -2024-09-02,17:00:44 | INFO | rank: 0 -2024-09-02,17:00:44 | INFO | remote_sync: None -2024-09-02,17:00:44 | INFO | remote_sync_frequency: 300 -2024-09-02,17:00:44 | INFO | remote_sync_protocol: s3 -2024-09-02,17:00:44 | INFO | report_to: wandb -2024-09-02,17:00:44 | INFO | resume: None -2024-09-02,17:00:44 | INFO | save_frequency: 1 -2024-09-02,17:00:44 | INFO | save_most_recent: False -2024-09-02,17:00:44 | INFO | seed: 0 -2024-09-02,17:00:44 | INFO | siglip: False -2024-09-02,17:00:44 | INFO | skip_scheduler: False -2024-09-02,17:00:44 | INFO | tensorboard: False -2024-09-02,17:00:44 | INFO | tensorboard_path: -2024-09-02,17:00:44 | INFO | torchcompile: False -2024-09-02,17:00:44 | INFO | torchscript: False -2024-09-02,17:00:44 | INFO | trace: False -2024-09-02,17:00:44 | INFO | train_data: /project/deemreason/junteng/Vision4Math/csv_data/plotqa_train_v2.csv -2024-09-02,17:00:44 | INFO | train_data_upsampling_factors: None -2024-09-02,17:00:44 | INFO | train_num_samples: None -2024-09-02,17:00:44 | INFO | use_bn_sync: False -2024-09-02,17:00:44 | INFO | use_bnb_linear: None -2024-09-02,17:00:44 | INFO | val_data: None -2024-09-02,17:00:44 | INFO | val_frequency: 1 -2024-09-02,17:00:44 | INFO | val_num_samples: None -2024-09-02,17:00:44 | INFO | wandb: True -2024-09-02,17:00:44 | INFO | wandb_notes: -2024-09-02,17:00:44 | INFO | wandb_project_name: open-clip--no-hard-sum -2024-09-02,17:00:44 | INFO | warmup: 0 -2024-09-02,17:00:44 | INFO | wd: 0.1 -2024-09-02,17:00:44 | INFO | workers: 4 -2024-09-02,17:00:44 | INFO | world_size: 4 -2024-09-02,17:00:44 | INFO | zeroshot_frequency: 2 -2024-09-02,17:01:05 | INFO | Start epoch 0 -2024-09-02,17:01:17 | INFO | Train Epoch: 0 [ 256/3655823 (0%)] Data (t): 1.754 Batch (t): 12.248, 20.9012/s, 5.22530/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 5.2595 (5.2595) Loss: 5.2595 (5.2595) -2024-09-02,17:01:56 | INFO | Train Epoch: 0 [ 25856/3655823 (1%)] Data (t): 0.000 Batch (t): 0.380, 677.052/s, 169.263/s/gpu LR: 0.000001 Logit Scale: 99.997 Contrastive_loss: 2.9758 (4.1176) Loss: 2.9758 (4.1176) -2024-09-02,17:02:33 | INFO | Train Epoch: 0 [ 51456/3655823 (1%)] Data (t): 0.000 Batch (t): 0.378, 676.905/s, 169.226/s/gpu LR: 0.000001 Logit Scale: 99.997 Contrastive_loss: 2.4698 (3.5684) Loss: 2.4698 (3.5684) -2024-09-02,17:03:11 | INFO | Train Epoch: 0 [ 77056/3655823 (2%)] Data (t): 0.000 Batch (t): 0.379, 676.556/s, 169.139/s/gpu LR: 0.000001 Logit Scale: 99.998 Contrastive_loss: 2.1856 (3.2227) Loss: 2.1856 (3.2227) -2024-09-02,17:03:49 | INFO | Train Epoch: 0 [ 102656/3655823 (3%)] Data (t): 0.000 Batch (t): 0.379, 676.309/s, 169.077/s/gpu LR: 0.000001 Logit Scale: 99.998 Contrastive_loss: 2.1842 (3.0150) Loss: 2.1842 (3.0150) -2024-09-02,17:04:27 | INFO | Train Epoch: 0 [ 128256/3655823 (4%)] Data (t): 0.000 Batch (t): 0.378, 676.523/s, 169.131/s/gpu LR: 0.000001 Logit Scale: 99.999 Contrastive_loss: 2.0812 (2.8593) Loss: 2.0812 (2.8593) -2024-09-02,17:05:05 | INFO | Train Epoch: 0 [ 153856/3655823 (4%)] Data (t): 0.000 Batch (t): 0.379, 676.623/s, 169.156/s/gpu LR: 0.000001 Logit Scale: 99.999 Contrastive_loss: 2.1902 (2.7638) Loss: 2.1902 (2.7638) -2024-09-02,17:05:43 | INFO | Train Epoch: 0 [ 179456/3655823 (5%)] Data (t): 0.000 Batch (t): 0.379, 675.999/s, 169.000/s/gpu LR: 0.000001 Logit Scale: 99.999 Contrastive_loss: 2.1777 (2.6905) Loss: 2.1777 (2.6905) -2024-09-02,17:06:20 | INFO | Train Epoch: 0 [ 205056/3655823 (6%)] Data (t): 0.000 Batch (t): 0.379, 677.692/s, 169.423/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.8749 (2.5999) Loss: 1.8749 (2.5999) -2024-09-02,17:06:58 | INFO | Train Epoch: 0 [ 230656/3655823 (6%)] Data (t): 0.000 Batch (t): 0.379, 675.463/s, 168.866/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.8951 (2.5294) Loss: 1.8951 (2.5294) -2024-09-02,17:07:36 | INFO | Train Epoch: 0 [ 256256/3655823 (7%)] Data (t): 0.000 Batch (t): 0.379, 676.451/s, 169.113/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.9112 (2.4732) Loss: 1.9112 (2.4732) -2024-09-02,17:08:14 | INFO | Train Epoch: 0 [ 281856/3655823 (8%)] Data (t): 0.000 Batch (t): 0.381, 677.074/s, 169.268/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.7566 (2.4135) Loss: 1.7566 (2.4135) -2024-09-02,17:08:52 | INFO | Train Epoch: 0 [ 307456/3655823 (8%)] Data (t): 0.000 Batch (t): 0.381, 676.286/s, 169.071/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.7460 (2.3621) Loss: 1.7460 (2.3621) -2024-09-02,17:09:30 | INFO | Train Epoch: 0 [ 333056/3655823 (9%)] Data (t): 0.000 Batch (t): 0.379, 676.268/s, 169.067/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.6631 (2.3122) Loss: 1.6631 (2.3122) -2024-09-02,17:10:08 | INFO | Train Epoch: 0 [ 358656/3655823 (10%)] Data (t): 0.000 Batch (t): 0.378, 677.603/s, 169.401/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.5462 (2.2611) Loss: 1.5462 (2.2611) -2024-09-02,17:10:46 | INFO | Train Epoch: 0 [ 384256/3655823 (11%)] Data (t): 0.000 Batch (t): 0.378, 675.742/s, 168.935/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.6793 (2.2248) Loss: 1.6793 (2.2248) -2024-09-02,17:11:24 | INFO | Train Epoch: 0 [ 409856/3655823 (11%)] Data (t): 0.000 Batch (t): 0.379, 677.675/s, 169.419/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.7648 (2.1977) Loss: 1.7648 (2.1977) -2024-09-02,17:12:02 | INFO | Train Epoch: 0 [ 435456/3655823 (12%)] Data (t): 0.000 Batch (t): 0.378, 676.553/s, 169.138/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.6476 (2.1672) Loss: 1.6476 (2.1672) -2024-09-02,17:12:39 | INFO | Train Epoch: 0 [ 461056/3655823 (13%)] Data (t): 0.000 Batch (t): 0.379, 676.094/s, 169.023/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.6472 (2.1398) Loss: 1.6472 (2.1398) -2024-09-02,17:13:17 | INFO | Train Epoch: 0 [ 486656/3655823 (13%)] Data (t): 0.000 Batch (t): 0.378, 676.813/s, 169.203/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.8936 (2.1275) Loss: 1.8936 (2.1275) -2024-09-02,17:13:55 | INFO | Train Epoch: 0 [ 512256/3655823 (14%)] Data (t): 0.000 Batch (t): 0.379, 677.019/s, 169.255/s/gpu LR: 0.000001 Logit Scale: 99.999 Contrastive_loss: 1.6228 (2.1034) Loss: 1.6228 (2.1034) -2024-09-02,17:14:33 | INFO | Train Epoch: 0 [ 537856/3655823 (15%)] Data (t): 0.000 Batch (t): 0.379, 676.537/s, 169.134/s/gpu LR: 0.000001 Logit Scale: 99.999 Contrastive_loss: 1.7543 (2.0876) Loss: 1.7543 (2.0876) -2024-09-02,17:15:11 | INFO | Train Epoch: 0 [ 563456/3655823 (15%)] Data (t): 0.000 Batch (t): 0.379, 675.239/s, 168.810/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.7627 (2.0735) Loss: 1.7627 (2.0735) -2024-09-02,17:15:49 | INFO | Train Epoch: 0 [ 589056/3655823 (16%)] Data (t): 0.000 Batch (t): 0.381, 676.143/s, 169.036/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.6217 (2.0546) Loss: 1.6217 (2.0546) -2024-09-02,17:16:27 | INFO | Train Epoch: 0 [ 614656/3655823 (17%)] Data (t): 0.000 Batch (t): 0.381, 677.080/s, 169.270/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.5036 (2.0326) Loss: 1.5036 (2.0326) -2024-09-02,17:17:05 | INFO | Train Epoch: 0 [ 640256/3655823 (18%)] Data (t): 0.000 Batch (t): 0.379, 676.838/s, 169.209/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.5986 (2.0159) Loss: 1.5986 (2.0159) -2024-09-02,17:17:43 | INFO | Train Epoch: 0 [ 665856/3655823 (18%)] Data (t): 0.000 Batch (t): 0.379, 675.593/s, 168.898/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.8321 (2.0091) Loss: 1.8321 (2.0091) -2024-09-02,17:18:21 | INFO | Train Epoch: 0 [ 691456/3655823 (19%)] Data (t): 0.000 Batch (t): 0.379, 675.721/s, 168.930/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.7241 (1.9989) Loss: 1.7241 (1.9989) -2024-09-02,17:18:59 | INFO | Train Epoch: 0 [ 717056/3655823 (20%)] Data (t): 0.000 Batch (t): 0.379, 675.916/s, 168.979/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.5365 (1.9830) Loss: 1.5365 (1.9830) -2024-09-02,17:19:36 | INFO | Train Epoch: 0 [ 742656/3655823 (20%)] Data (t): 0.000 Batch (t): 0.378, 676.591/s, 169.148/s/gpu LR: 0.000001 Logit Scale: 99.999 Contrastive_loss: 1.4757 (1.9661) Loss: 1.4757 (1.9661) -2024-09-02,17:20:14 | INFO | Train Epoch: 0 [ 768256/3655823 (21%)] Data (t): 0.000 Batch (t): 0.378, 676.128/s, 169.032/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.7370 (1.9587) Loss: 1.7370 (1.9587) -2024-09-02,17:20:52 | INFO | Train Epoch: 0 [ 793856/3655823 (22%)] Data (t): 0.000 Batch (t): 0.378, 675.958/s, 168.989/s/gpu LR: 0.000001 Logit Scale: 99.999 Contrastive_loss: 1.5011 (1.9444) Loss: 1.5011 (1.9444) -2024-09-02,17:21:30 | INFO | Train Epoch: 0 [ 819456/3655823 (22%)] Data (t): 0.000 Batch (t): 0.378, 677.304/s, 169.326/s/gpu LR: 0.000001 Logit Scale: 99.999 Contrastive_loss: 1.7089 (1.9372) Loss: 1.7089 (1.9372) -2024-09-02,17:22:08 | INFO | Train Epoch: 0 [ 845056/3655823 (23%)] Data (t): 0.000 Batch (t): 0.378, 677.401/s, 169.350/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.7800 (1.9326) Loss: 1.7800 (1.9326) -2024-09-02,17:22:46 | INFO | Train Epoch: 0 [ 870656/3655823 (24%)] Data (t): 0.000 Batch (t): 0.378, 676.107/s, 169.027/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.4916 (1.9200) Loss: 1.4916 (1.9200) -2024-09-02,17:23:23 | INFO | Train Epoch: 0 [ 896256/3655823 (25%)] Data (t): 0.000 Batch (t): 0.378, 675.706/s, 168.927/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3761 (1.9049) Loss: 1.3761 (1.9049) -2024-09-02,17:24:02 | INFO | Train Epoch: 0 [ 921856/3655823 (25%)] Data (t): 0.000 Batch (t): 0.386, 677.235/s, 169.309/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3380 (1.8896) Loss: 1.3380 (1.8896) -2024-09-02,17:24:40 | INFO | Train Epoch: 0 [ 947456/3655823 (26%)] Data (t): 0.000 Batch (t): 0.378, 677.327/s, 169.332/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.7603 (1.8862) Loss: 1.7603 (1.8862) -2024-09-02,17:25:18 | INFO | Train Epoch: 0 [ 973056/3655823 (27%)] Data (t): 0.000 Batch (t): 0.378, 676.194/s, 169.049/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3942 (1.8736) Loss: 1.3942 (1.8736) -2024-09-02,17:25:56 | INFO | Train Epoch: 0 [ 998656/3655823 (27%)] Data (t): 0.000 Batch (t): 0.379, 675.893/s, 168.973/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.7801 (1.8712) Loss: 1.7801 (1.8712) -2024-09-02,17:26:34 | INFO | Train Epoch: 0 [1024256/3655823 (28%)] Data (t): 0.000 Batch (t): 0.379, 676.507/s, 169.127/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.5607 (1.8637) Loss: 1.5607 (1.8637) -2024-09-02,17:27:11 | INFO | Train Epoch: 0 [1049856/3655823 (29%)] Data (t): 0.000 Batch (t): 0.379, 675.506/s, 168.877/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.5423 (1.8560) Loss: 1.5423 (1.8560) -2024-09-02,17:27:49 | INFO | Train Epoch: 0 [1075456/3655823 (29%)] Data (t): 0.000 Batch (t): 0.379, 676.067/s, 169.017/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.5785 (1.8496) Loss: 1.5785 (1.8496) -2024-09-02,17:28:27 | INFO | Train Epoch: 0 [1101056/3655823 (30%)] Data (t): 0.000 Batch (t): 0.379, 675.764/s, 168.941/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.5327 (1.8424) Loss: 1.5327 (1.8424) -2024-09-02,17:29:05 | INFO | Train Epoch: 0 [1126656/3655823 (31%)] Data (t): 0.000 Batch (t): 0.379, 675.960/s, 168.990/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.5360 (1.8355) Loss: 1.5360 (1.8355) -2024-09-02,17:29:43 | INFO | Train Epoch: 0 [1152256/3655823 (32%)] Data (t): 0.000 Batch (t): 0.379, 676.676/s, 169.169/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.5511 (1.8294) Loss: 1.5511 (1.8294) -2024-09-02,17:30:21 | INFO | Train Epoch: 0 [1177856/3655823 (32%)] Data (t): 0.000 Batch (t): 0.379, 676.297/s, 169.074/s/gpu LR: 0.000001 Logit Scale: 99.999 Contrastive_loss: 1.5411 (1.8232) Loss: 1.5411 (1.8232) -2024-09-02,17:30:59 | INFO | Train Epoch: 0 [1203456/3655823 (33%)] Data (t): 0.000 Batch (t): 0.379, 676.870/s, 169.217/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.6671 (1.8200) Loss: 1.6671 (1.8200) -2024-09-02,17:31:37 | INFO | Train Epoch: 0 [1229056/3655823 (34%)] Data (t): 0.000 Batch (t): 0.387, 675.279/s, 168.820/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.5602 (1.8147) Loss: 1.5602 (1.8147) -2024-09-02,17:32:15 | INFO | Train Epoch: 0 [1254656/3655823 (34%)] Data (t): 0.000 Batch (t): 0.379, 676.180/s, 169.045/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3065 (1.8045) Loss: 1.3065 (1.8045) -2024-09-02,17:32:53 | INFO | Train Epoch: 0 [1280256/3655823 (35%)] Data (t): 0.000 Batch (t): 0.379, 675.648/s, 168.912/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.6546 (1.8016) Loss: 1.6546 (1.8016) -2024-09-02,17:33:31 | INFO | Train Epoch: 0 [1305856/3655823 (36%)] Data (t): 0.000 Batch (t): 0.379, 676.430/s, 169.108/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.4001 (1.7939) Loss: 1.4001 (1.7939) -2024-09-02,17:34:09 | INFO | Train Epoch: 0 [1331456/3655823 (36%)] Data (t): 0.000 Batch (t): 0.379, 677.057/s, 169.264/s/gpu LR: 0.000001 Logit Scale: 99.999 Contrastive_loss: 1.6090 (1.7904) Loss: 1.6090 (1.7904) -2024-09-02,17:34:47 | INFO | Train Epoch: 0 [1357056/3655823 (37%)] Data (t): 0.000 Batch (t): 0.379, 675.635/s, 168.909/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3077 (1.7814) Loss: 1.3077 (1.7814) -2024-09-02,17:35:25 | INFO | Train Epoch: 0 [1382656/3655823 (38%)] Data (t): 0.000 Batch (t): 0.378, 676.305/s, 169.076/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.5069 (1.7764) Loss: 1.5069 (1.7764) -2024-09-02,17:36:02 | INFO | Train Epoch: 0 [1408256/3655823 (39%)] Data (t): 0.000 Batch (t): 0.379, 676.620/s, 169.155/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.5540 (1.7725) Loss: 1.5540 (1.7725) -2024-09-02,17:36:40 | INFO | Train Epoch: 0 [1433856/3655823 (39%)] Data (t): 0.000 Batch (t): 0.378, 676.559/s, 169.140/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.6383 (1.7701) Loss: 1.6383 (1.7701) -2024-09-02,17:37:18 | INFO | Train Epoch: 0 [1459456/3655823 (40%)] Data (t): 0.000 Batch (t): 0.378, 676.126/s, 169.031/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.5248 (1.7659) Loss: 1.5248 (1.7659) -2024-09-02,17:37:56 | INFO | Train Epoch: 0 [1485056/3655823 (41%)] Data (t): 0.000 Batch (t): 0.378, 675.003/s, 168.751/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.5814 (1.7628) Loss: 1.5814 (1.7628) -2024-09-02,17:38:34 | INFO | Train Epoch: 0 [1510656/3655823 (41%)] Data (t): 0.000 Batch (t): 0.378, 677.089/s, 169.272/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.4005 (1.7567) Loss: 1.4005 (1.7567) -2024-09-02,17:39:12 | INFO | Train Epoch: 0 [1536256/3655823 (42%)] Data (t): 0.000 Batch (t): 0.381, 677.374/s, 169.343/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.1673 (1.7471) Loss: 1.1673 (1.7471) -2024-09-02,17:39:50 | INFO | Train Epoch: 0 [1561856/3655823 (43%)] Data (t): 0.000 Batch (t): 0.385, 675.953/s, 168.988/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3990 (1.7414) Loss: 1.3990 (1.7414) -2024-09-02,17:40:28 | INFO | Train Epoch: 0 [1587456/3655823 (43%)] Data (t): 0.000 Batch (t): 0.379, 675.146/s, 168.787/s/gpu LR: 0.000001 Logit Scale: 99.999 Contrastive_loss: 1.4165 (1.7363) Loss: 1.4165 (1.7363) -2024-09-02,17:41:06 | INFO | Train Epoch: 0 [1613056/3655823 (44%)] Data (t): 0.000 Batch (t): 0.378, 677.154/s, 169.288/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3328 (1.7300) Loss: 1.3328 (1.7300) -2024-09-02,17:41:44 | INFO | Train Epoch: 0 [1638656/3655823 (45%)] Data (t): 0.000 Batch (t): 0.379, 675.483/s, 168.871/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.5460 (1.7271) Loss: 1.5460 (1.7271) -2024-09-02,17:42:22 | INFO | Train Epoch: 0 [1664256/3655823 (46%)] Data (t): 0.000 Batch (t): 0.378, 677.595/s, 169.399/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3846 (1.7220) Loss: 1.3846 (1.7220) -2024-09-02,17:43:00 | INFO | Train Epoch: 0 [1689856/3655823 (46%)] Data (t): 0.000 Batch (t): 0.378, 677.483/s, 169.371/s/gpu LR: 0.000001 Logit Scale: 99.999 Contrastive_loss: 1.7580 (1.7225) Loss: 1.7580 (1.7225) -2024-09-02,17:43:37 | INFO | Train Epoch: 0 [1715456/3655823 (47%)] Data (t): 0.000 Batch (t): 0.378, 677.225/s, 169.306/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3731 (1.7174) Loss: 1.3731 (1.7174) -2024-09-02,17:44:15 | INFO | Train Epoch: 0 [1741056/3655823 (48%)] Data (t): 0.000 Batch (t): 0.378, 676.380/s, 169.095/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.2451 (1.7105) Loss: 1.2451 (1.7105) -2024-09-02,17:44:53 | INFO | Train Epoch: 0 [1766656/3655823 (48%)] Data (t): 0.000 Batch (t): 0.378, 676.181/s, 169.045/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.5197 (1.7078) Loss: 1.5197 (1.7078) -2024-09-02,17:45:31 | INFO | Train Epoch: 0 [1792256/3655823 (49%)] Data (t): 0.000 Batch (t): 0.379, 676.012/s, 169.003/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.2161 (1.7009) Loss: 1.2161 (1.7009) -2024-09-02,17:46:09 | INFO | Train Epoch: 0 [1817856/3655823 (50%)] Data (t): 0.000 Batch (t): 0.379, 677.011/s, 169.253/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3967 (1.6966) Loss: 1.3967 (1.6966) -2024-09-02,17:46:47 | INFO | Train Epoch: 0 [1843456/3655823 (50%)] Data (t): 0.000 Batch (t): 0.380, 675.726/s, 168.931/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.6459 (1.6959) Loss: 1.6459 (1.6959) -2024-09-02,17:47:25 | INFO | Train Epoch: 0 [1869056/3655823 (51%)] Data (t): 0.000 Batch (t): 0.383, 676.035/s, 169.009/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3637 (1.6915) Loss: 1.3637 (1.6915) -2024-09-02,17:48:03 | INFO | Train Epoch: 0 [1894656/3655823 (52%)] Data (t): 0.000 Batch (t): 0.378, 677.644/s, 169.411/s/gpu LR: 0.000001 Logit Scale: 99.999 Contrastive_loss: 1.3728 (1.6872) Loss: 1.3728 (1.6872) -2024-09-02,17:48:41 | INFO | Train Epoch: 0 [1920256/3655823 (53%)] Data (t): 0.000 Batch (t): 0.378, 678.242/s, 169.560/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.2112 (1.6809) Loss: 1.2112 (1.6809) -2024-09-02,17:49:19 | INFO | Train Epoch: 0 [1945856/3655823 (53%)] Data (t): 0.000 Batch (t): 0.378, 677.097/s, 169.274/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.4954 (1.6785) Loss: 1.4954 (1.6785) -2024-09-02,17:49:56 | INFO | Train Epoch: 0 [1971456/3655823 (54%)] Data (t): 0.000 Batch (t): 0.378, 675.711/s, 168.928/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.5867 (1.6774) Loss: 1.5867 (1.6774) -2024-09-02,17:50:34 | INFO | Train Epoch: 0 [1997056/3655823 (55%)] Data (t): 0.000 Batch (t): 0.378, 675.975/s, 168.994/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3394 (1.6731) Loss: 1.3394 (1.6731) -2024-09-02,17:51:12 | INFO | Train Epoch: 0 [2022656/3655823 (55%)] Data (t): 0.000 Batch (t): 0.378, 677.547/s, 169.387/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.2307 (1.6675) Loss: 1.2307 (1.6675) -2024-09-02,17:51:50 | INFO | Train Epoch: 0 [2048256/3655823 (56%)] Data (t): 0.000 Batch (t): 0.378, 677.933/s, 169.483/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.5318 (1.6659) Loss: 1.5318 (1.6659) -2024-09-02,17:52:28 | INFO | Train Epoch: 0 [2073856/3655823 (57%)] Data (t): 0.000 Batch (t): 0.378, 675.782/s, 168.946/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.2522 (1.6608) Loss: 1.2522 (1.6608) -2024-09-02,17:53:06 | INFO | Train Epoch: 0 [2099456/3655823 (57%)] Data (t): 0.000 Batch (t): 0.378, 676.487/s, 169.122/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3010 (1.6565) Loss: 1.3010 (1.6565) -2024-09-02,17:53:43 | INFO | Train Epoch: 0 [2125056/3655823 (58%)] Data (t): 0.000 Batch (t): 0.378, 677.672/s, 169.418/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3918 (1.6533) Loss: 1.3918 (1.6533) -2024-09-02,17:54:22 | INFO | Train Epoch: 0 [2150656/3655823 (59%)] Data (t): 0.000 Batch (t): 0.381, 676.016/s, 169.004/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3925 (1.6503) Loss: 1.3925 (1.6503) -2024-09-02,17:55:00 | INFO | Train Epoch: 0 [2176256/3655823 (60%)] Data (t): 0.000 Batch (t): 0.384, 676.428/s, 169.107/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.4327 (1.6477) Loss: 1.4327 (1.6477) -2024-09-02,17:55:38 | INFO | Train Epoch: 0 [2201856/3655823 (60%)] Data (t): 0.000 Batch (t): 0.378, 676.436/s, 169.109/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.6019 (1.6472) Loss: 1.6019 (1.6472) -2024-09-02,17:56:16 | INFO | Train Epoch: 0 [2227456/3655823 (61%)] Data (t): 0.000 Batch (t): 0.378, 677.053/s, 169.263/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.4854 (1.6454) Loss: 1.4854 (1.6454) -2024-09-02,17:56:54 | INFO | Train Epoch: 0 [2253056/3655823 (62%)] Data (t): 0.000 Batch (t): 0.378, 676.850/s, 169.212/s/gpu LR: 0.000001 Logit Scale: 99.999 Contrastive_loss: 1.3348 (1.6419) Loss: 1.3348 (1.6419) -2024-09-02,17:57:31 | INFO | Train Epoch: 0 [2278656/3655823 (62%)] Data (t): 0.000 Batch (t): 0.379, 677.696/s, 169.424/s/gpu LR: 0.000001 Logit Scale: 99.999 Contrastive_loss: 1.3502 (1.6386) Loss: 1.3502 (1.6386) -2024-09-02,17:58:09 | INFO | Train Epoch: 0 [2304256/3655823 (63%)] Data (t): 0.000 Batch (t): 0.379, 675.946/s, 168.986/s/gpu LR: 0.000001 Logit Scale: 99.999 Contrastive_loss: 1.1628 (1.6334) Loss: 1.1628 (1.6334) -2024-09-02,17:58:47 | INFO | Train Epoch: 0 [2329856/3655823 (64%)] Data (t): 0.000 Batch (t): 0.379, 676.944/s, 169.236/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3943 (1.6308) Loss: 1.3943 (1.6308) -2024-09-02,17:59:25 | INFO | Train Epoch: 0 [2355456/3655823 (64%)] Data (t): 0.000 Batch (t): 0.378, 676.151/s, 169.038/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.5322 (1.6298) Loss: 1.5322 (1.6298) -2024-09-02,18:00:03 | INFO | Train Epoch: 0 [2381056/3655823 (65%)] Data (t): 0.000 Batch (t): 0.379, 676.359/s, 169.090/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3759 (1.6271) Loss: 1.3759 (1.6271) -2024-09-02,18:00:41 | INFO | Train Epoch: 0 [2406656/3655823 (66%)] Data (t): 0.000 Batch (t): 0.378, 676.859/s, 169.215/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.4201 (1.6249) Loss: 1.4201 (1.6249) -2024-09-02,18:01:18 | INFO | Train Epoch: 0 [2432256/3655823 (67%)] Data (t): 0.000 Batch (t): 0.379, 676.175/s, 169.044/s/gpu LR: 0.000001 Logit Scale: 99.999 Contrastive_loss: 1.5969 (1.6246) Loss: 1.5969 (1.6246) -2024-09-02,18:01:57 | INFO | Train Epoch: 0 [2457856/3655823 (67%)] Data (t): 0.000 Batch (t): 0.380, 677.101/s, 169.275/s/gpu LR: 0.000001 Logit Scale: 99.999 Contrastive_loss: 1.3824 (1.6221) Loss: 1.3824 (1.6221) -2024-09-02,18:02:35 | INFO | Train Epoch: 0 [2483456/3655823 (68%)] Data (t): 0.000 Batch (t): 0.383, 676.666/s, 169.167/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.7469 (1.6234) Loss: 1.7469 (1.6234) -2024-09-02,18:03:13 | INFO | Train Epoch: 0 [2509056/3655823 (69%)] Data (t): 0.000 Batch (t): 0.378, 676.162/s, 169.041/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3684 (1.6208) Loss: 1.3684 (1.6208) -2024-09-02,18:03:50 | INFO | Train Epoch: 0 [2534656/3655823 (69%)] Data (t): 0.000 Batch (t): 0.378, 676.999/s, 169.250/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.6684 (1.6213) Loss: 1.6684 (1.6213) -2024-09-02,18:04:28 | INFO | Train Epoch: 0 [2560256/3655823 (70%)] Data (t): 0.000 Batch (t): 0.378, 677.823/s, 169.456/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.4214 (1.6193) Loss: 1.4214 (1.6193) -2024-09-02,18:05:06 | INFO | Train Epoch: 0 [2585856/3655823 (71%)] Data (t): 0.000 Batch (t): 0.378, 677.033/s, 169.258/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3613 (1.6168) Loss: 1.3613 (1.6168) -2024-09-02,18:05:44 | INFO | Train Epoch: 0 [2611456/3655823 (71%)] Data (t): 0.000 Batch (t): 0.378, 676.606/s, 169.151/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.4383 (1.6150) Loss: 1.4383 (1.6150) -2024-09-02,18:06:22 | INFO | Train Epoch: 0 [2637056/3655823 (72%)] Data (t): 0.000 Batch (t): 0.378, 675.724/s, 168.931/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.4383 (1.6133) Loss: 1.4383 (1.6133) -2024-09-02,18:07:00 | INFO | Train Epoch: 0 [2662656/3655823 (73%)] Data (t): 0.000 Batch (t): 0.378, 676.319/s, 169.080/s/gpu LR: 0.000001 Logit Scale: 99.999 Contrastive_loss: 1.5502 (1.6127) Loss: 1.5502 (1.6127) -2024-09-02,18:07:37 | INFO | Train Epoch: 0 [2688256/3655823 (74%)] Data (t): 0.000 Batch (t): 0.378, 676.899/s, 169.225/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.4213 (1.6109) Loss: 1.4213 (1.6109) -2024-09-02,18:08:15 | INFO | Train Epoch: 0 [2713856/3655823 (74%)] Data (t): 0.000 Batch (t): 0.378, 677.534/s, 169.383/s/gpu LR: 0.000001 Logit Scale: 99.999 Contrastive_loss: 1.2989 (1.6080) Loss: 1.2989 (1.6080) -2024-09-02,18:08:53 | INFO | Train Epoch: 0 [2739456/3655823 (75%)] Data (t): 0.000 Batch (t): 0.379, 677.148/s, 169.287/s/gpu LR: 0.000001 Logit Scale: 99.999 Contrastive_loss: 1.5259 (1.6072) Loss: 1.5259 (1.6072) -2024-09-02,18:09:31 | INFO | Train Epoch: 0 [2765056/3655823 (76%)] Data (t): 0.000 Batch (t): 0.381, 677.541/s, 169.385/s/gpu LR: 0.000001 Logit Scale: 99.999 Contrastive_loss: 1.5917 (1.6071) Loss: 1.5917 (1.6071) -2024-09-02,18:10:10 | INFO | Train Epoch: 0 [2790656/3655823 (76%)] Data (t): 0.000 Batch (t): 0.384, 677.485/s, 169.371/s/gpu LR: 0.000001 Logit Scale: 99.999 Contrastive_loss: 1.5645 (1.6067) Loss: 1.5645 (1.6067) -2024-09-02,18:10:47 | INFO | Train Epoch: 0 [2816256/3655823 (77%)] Data (t): 0.000 Batch (t): 0.378, 677.393/s, 169.348/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.7347 (1.6079) Loss: 1.7347 (1.6079) -2024-09-02,18:11:25 | INFO | Train Epoch: 0 [2841856/3655823 (78%)] Data (t): 0.000 Batch (t): 0.378, 676.566/s, 169.142/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.6727 (1.6084) Loss: 1.6727 (1.6084) -2024-09-02,18:12:03 | INFO | Train Epoch: 0 [2867456/3655823 (78%)] Data (t): 0.000 Batch (t): 0.378, 676.489/s, 169.122/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3092 (1.6058) Loss: 1.3092 (1.6058) -2024-09-02,18:12:41 | INFO | Train Epoch: 0 [2893056/3655823 (79%)] Data (t): 0.000 Batch (t): 0.378, 677.232/s, 169.308/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.4066 (1.6040) Loss: 1.4066 (1.6040) -2024-09-02,18:13:19 | INFO | Train Epoch: 0 [2918656/3655823 (80%)] Data (t): 0.000 Batch (t): 0.378, 676.959/s, 169.240/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.5859 (1.6039) Loss: 1.5859 (1.6039) -2024-09-02,18:13:57 | INFO | Train Epoch: 0 [2944256/3655823 (81%)] Data (t): 0.000 Batch (t): 0.378, 676.232/s, 169.058/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.4356 (1.6024) Loss: 1.4356 (1.6024) -2024-09-02,18:14:34 | INFO | Train Epoch: 0 [2969856/3655823 (81%)] Data (t): 0.000 Batch (t): 0.378, 677.882/s, 169.471/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3906 (1.6006) Loss: 1.3906 (1.6006) -2024-09-02,18:15:12 | INFO | Train Epoch: 0 [2995456/3655823 (82%)] Data (t): 0.000 Batch (t): 0.378, 677.295/s, 169.324/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.5649 (1.6003) Loss: 1.5649 (1.6003) -2024-09-02,18:15:50 | INFO | Train Epoch: 0 [3021056/3655823 (83%)] Data (t): 0.000 Batch (t): 0.379, 674.552/s, 168.638/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.5406 (1.5998) Loss: 1.5406 (1.5998) -2024-09-02,18:16:28 | INFO | Train Epoch: 0 [3046656/3655823 (83%)] Data (t): 0.000 Batch (t): 0.379, 675.588/s, 168.897/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3607 (1.5978) Loss: 1.3607 (1.5978) -2024-09-02,18:17:06 | INFO | Train Epoch: 0 [3072256/3655823 (84%)] Data (t): 0.000 Batch (t): 0.379, 675.415/s, 168.854/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.4144 (1.5963) Loss: 1.4144 (1.5963) -2024-09-02,18:17:45 | INFO | Train Epoch: 0 [3097856/3655823 (85%)] Data (t): 0.000 Batch (t): 0.387, 676.759/s, 169.190/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3620 (1.5944) Loss: 1.3620 (1.5944) -2024-09-02,18:18:22 | INFO | Train Epoch: 0 [3123456/3655823 (85%)] Data (t): 0.000 Batch (t): 0.379, 677.187/s, 169.297/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.1696 (1.5909) Loss: 1.1696 (1.5909) -2024-09-02,18:19:00 | INFO | Train Epoch: 0 [3149056/3655823 (86%)] Data (t): 0.000 Batch (t): 0.379, 676.087/s, 169.022/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.2539 (1.5882) Loss: 1.2539 (1.5882) -2024-09-02,18:19:38 | INFO | Train Epoch: 0 [3174656/3655823 (87%)] Data (t): 0.000 Batch (t): 0.379, 676.190/s, 169.048/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.5336 (1.5878) Loss: 1.5336 (1.5878) -2024-09-02,18:20:16 | INFO | Train Epoch: 0 [3200256/3655823 (88%)] Data (t): 0.000 Batch (t): 0.379, 675.661/s, 168.915/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.5062 (1.5871) Loss: 1.5062 (1.5871) -2024-09-02,18:20:54 | INFO | Train Epoch: 0 [3225856/3655823 (88%)] Data (t): 0.000 Batch (t): 0.378, 677.067/s, 169.267/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3511 (1.5853) Loss: 1.3511 (1.5853) -2024-09-02,18:21:32 | INFO | Train Epoch: 0 [3251456/3655823 (89%)] Data (t): 0.000 Batch (t): 0.378, 675.913/s, 168.978/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3807 (1.5837) Loss: 1.3807 (1.5837) -2024-09-02,18:22:09 | INFO | Train Epoch: 0 [3277056/3655823 (90%)] Data (t): 0.000 Batch (t): 0.378, 676.783/s, 169.196/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.4780 (1.5829) Loss: 1.4780 (1.5829) -2024-09-02,18:22:47 | INFO | Train Epoch: 0 [3302656/3655823 (90%)] Data (t): 0.000 Batch (t): 0.378, 676.323/s, 169.081/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.6936 (1.5837) Loss: 1.6936 (1.5837) -2024-09-02,18:23:25 | INFO | Train Epoch: 0 [3328256/3655823 (91%)] Data (t): 0.000 Batch (t): 0.378, 676.284/s, 169.071/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3626 (1.5820) Loss: 1.3626 (1.5820) -2024-09-02,18:24:03 | INFO | Train Epoch: 0 [3353856/3655823 (92%)] Data (t): 0.000 Batch (t): 0.379, 675.473/s, 168.868/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.2329 (1.5794) Loss: 1.2329 (1.5794) -2024-09-02,18:24:41 | INFO | Train Epoch: 0 [3379456/3655823 (92%)] Data (t): 0.000 Batch (t): 0.379, 676.611/s, 169.153/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.2383 (1.5768) Loss: 1.2383 (1.5768) -2024-09-02,18:25:19 | INFO | Train Epoch: 0 [3405056/3655823 (93%)] Data (t): 0.000 Batch (t): 0.380, 676.167/s, 169.042/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3239 (1.5749) Loss: 1.3239 (1.5749) -2024-09-02,18:25:57 | INFO | Train Epoch: 0 [3430656/3655823 (94%)] Data (t): 0.000 Batch (t): 0.383, 674.988/s, 168.747/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3073 (1.5729) Loss: 1.3073 (1.5729) -2024-09-02,18:26:35 | INFO | Train Epoch: 0 [3456256/3655823 (95%)] Data (t): 0.000 Batch (t): 0.379, 676.414/s, 169.103/s/gpu LR: 0.000001 Logit Scale: 99.999 Contrastive_loss: 1.4496 (1.5720) Loss: 1.4496 (1.5720) -2024-09-02,18:27:13 | INFO | Train Epoch: 0 [3481856/3655823 (95%)] Data (t): 0.000 Batch (t): 0.379, 677.595/s, 169.399/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.2305 (1.5695) Loss: 1.2305 (1.5695) -2024-09-02,18:27:51 | INFO | Train Epoch: 0 [3507456/3655823 (96%)] Data (t): 0.000 Batch (t): 0.379, 676.493/s, 169.123/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.6572 (1.5702) Loss: 1.6572 (1.5702) -2024-09-02,18:28:29 | INFO | Train Epoch: 0 [3533056/3655823 (97%)] Data (t): 0.000 Batch (t): 0.379, 676.216/s, 169.054/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3553 (1.5686) Loss: 1.3553 (1.5686) -2024-09-02,18:29:07 | INFO | Train Epoch: 0 [3558656/3655823 (97%)] Data (t): 0.000 Batch (t): 0.379, 676.980/s, 169.245/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3982 (1.5674) Loss: 1.3982 (1.5674) -2024-09-02,18:29:44 | INFO | Train Epoch: 0 [3584256/3655823 (98%)] Data (t): 0.000 Batch (t): 0.379, 675.737/s, 168.934/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.6576 (1.5681) Loss: 1.6576 (1.5681) -2024-09-02,18:30:22 | INFO | Train Epoch: 0 [3609856/3655823 (99%)] Data (t): 0.000 Batch (t): 0.379, 675.732/s, 168.933/s/gpu LR: 0.000001 Logit Scale: 99.999 Contrastive_loss: 1.4003 (1.5669) Loss: 1.4003 (1.5669) -2024-09-02,18:31:00 | INFO | Train Epoch: 0 [3635456/3655823 (99%)] Data (t): 0.000 Batch (t): 0.378, 676.460/s, 169.115/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.2079 (1.5644) Loss: 1.2079 (1.5644) -2024-09-02,18:31:30 | INFO | Train Epoch: 0 [3655680/3655823 (100%)] Data (t): 0.001 Batch (t): 0.378, 680.387/s, 170.097/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3869 (1.5631) Loss: 1.3869 (1.5631) -2024-09-02,18:31:38 | INFO | Start epoch 1 -2024-09-02,18:31:40 | INFO | Train Epoch: 1 [ 256/3655823 (0%)] Data (t): 1.318 Batch (t): 1.708, 149.884/s, 37.4710/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3206 (1.3206) Loss: 1.3206 (1.3206) -2024-09-02,18:32:18 | INFO | Train Epoch: 1 [ 25856/3655823 (1%)] Data (t): 0.000 Batch (t): 0.381, 676.180/s, 169.045/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3646 (1.3426) Loss: 1.3646 (1.3426) -2024-09-02,18:32:56 | INFO | Train Epoch: 1 [ 51456/3655823 (1%)] Data (t): 0.000 Batch (t): 0.378, 676.441/s, 169.110/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3668 (1.3507) Loss: 1.3668 (1.3507) -2024-09-02,18:33:34 | INFO | Train Epoch: 1 [ 77056/3655823 (2%)] Data (t): 0.000 Batch (t): 0.383, 676.097/s, 169.024/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.5541 (1.4015) Loss: 1.5541 (1.4015) -2024-09-02,18:34:12 | INFO | Train Epoch: 1 [ 102656/3655823 (3%)] Data (t): 0.000 Batch (t): 0.378, 677.952/s, 169.488/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.1364 (1.3485) Loss: 1.1364 (1.3485) -2024-09-02,18:34:50 | INFO | Train Epoch: 1 [ 128256/3655823 (4%)] Data (t): 0.000 Batch (t): 0.379, 676.564/s, 169.141/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.4352 (1.3630) Loss: 1.4352 (1.3630) -2024-09-02,18:35:28 | INFO | Train Epoch: 1 [ 153856/3655823 (4%)] Data (t): 0.000 Batch (t): 0.378, 677.698/s, 169.424/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.1965 (1.3392) Loss: 1.1965 (1.3392) -2024-09-02,18:36:05 | INFO | Train Epoch: 1 [ 179456/3655823 (5%)] Data (t): 0.000 Batch (t): 0.379, 676.961/s, 169.240/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.2519 (1.3283) Loss: 1.2519 (1.3283) -2024-09-02,18:36:43 | INFO | Train Epoch: 1 [ 205056/3655823 (6%)] Data (t): 0.000 Batch (t): 0.378, 676.971/s, 169.243/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3401 (1.3296) Loss: 1.3401 (1.3296) -2024-09-02,18:37:21 | INFO | Train Epoch: 1 [ 230656/3655823 (6%)] Data (t): 0.000 Batch (t): 0.378, 677.208/s, 169.302/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.5534 (1.3520) Loss: 1.5534 (1.3520) -2024-09-02,18:37:59 | INFO | Train Epoch: 1 [ 256256/3655823 (7%)] Data (t): 0.000 Batch (t): 0.378, 677.115/s, 169.279/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3837 (1.3548) Loss: 1.3837 (1.3548) -2024-09-02,18:38:37 | INFO | Train Epoch: 1 [ 281856/3655823 (8%)] Data (t): 0.000 Batch (t): 0.378, 676.050/s, 169.012/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.1810 (1.3404) Loss: 1.1810 (1.3404) -2024-09-02,18:39:15 | INFO | Train Epoch: 1 [ 307456/3655823 (8%)] Data (t): 0.000 Batch (t): 0.378, 676.067/s, 169.017/s/gpu LR: 0.000001 Logit Scale: 99.999 Contrastive_loss: 1.5743 (1.3584) Loss: 1.5743 (1.3584) -2024-09-02,18:39:53 | INFO | Train Epoch: 1 [ 333056/3655823 (9%)] Data (t): 0.000 Batch (t): 0.381, 673.184/s, 168.296/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.5649 (1.3731) Loss: 1.5649 (1.3731) -2024-09-02,18:40:31 | INFO | Train Epoch: 1 [ 358656/3655823 (10%)] Data (t): 0.000 Batch (t): 0.378, 677.106/s, 169.276/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3331 (1.3704) Loss: 1.3331 (1.3704) -2024-09-02,18:41:09 | INFO | Train Epoch: 1 [ 384256/3655823 (11%)] Data (t): 0.000 Batch (t): 0.382, 676.942/s, 169.236/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.5151 (1.3795) Loss: 1.5151 (1.3795) -2024-09-02,18:41:47 | INFO | Train Epoch: 1 [ 409856/3655823 (11%)] Data (t): 0.000 Batch (t): 0.379, 676.163/s, 169.041/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3618 (1.3784) Loss: 1.3618 (1.3784) -2024-09-02,18:42:25 | INFO | Train Epoch: 1 [ 435456/3655823 (12%)] Data (t): 0.000 Batch (t): 0.378, 678.376/s, 169.594/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3043 (1.3743) Loss: 1.3043 (1.3743) -2024-09-02,18:43:02 | INFO | Train Epoch: 1 [ 461056/3655823 (13%)] Data (t): 0.000 Batch (t): 0.379, 676.245/s, 169.061/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.2998 (1.3704) Loss: 1.2998 (1.3704) -2024-09-02,18:43:40 | INFO | Train Epoch: 1 [ 486656/3655823 (13%)] Data (t): 0.000 Batch (t): 0.379, 675.095/s, 168.774/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3554 (1.3697) Loss: 1.3554 (1.3697) -2024-09-02,18:44:18 | INFO | Train Epoch: 1 [ 512256/3655823 (14%)] Data (t): 0.000 Batch (t): 0.378, 677.371/s, 169.343/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.1097 (1.3573) Loss: 1.1097 (1.3573) -2024-09-02,18:44:56 | INFO | Train Epoch: 1 [ 537856/3655823 (15%)] Data (t): 0.000 Batch (t): 0.379, 676.012/s, 169.003/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.4220 (1.3602) Loss: 1.4220 (1.3602) -2024-09-02,18:45:34 | INFO | Train Epoch: 1 [ 563456/3655823 (15%)] Data (t): 0.000 Batch (t): 0.379, 676.611/s, 169.153/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.2330 (1.3547) Loss: 1.2330 (1.3547) -2024-09-02,18:46:12 | INFO | Train Epoch: 1 [ 589056/3655823 (16%)] Data (t): 0.000 Batch (t): 0.379, 675.899/s, 168.975/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.4963 (1.3606) Loss: 1.4963 (1.3606) -2024-09-02,18:46:50 | INFO | Train Epoch: 1 [ 614656/3655823 (17%)] Data (t): 0.000 Batch (t): 0.379, 676.690/s, 169.173/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.4153 (1.3628) Loss: 1.4153 (1.3628) -2024-09-02,18:47:28 | INFO | Train Epoch: 1 [ 640256/3655823 (18%)] Data (t): 0.000 Batch (t): 0.381, 676.393/s, 169.098/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.2886 (1.3599) Loss: 1.2886 (1.3599) -2024-09-02,18:48:05 | INFO | Train Epoch: 1 [ 665856/3655823 (18%)] Data (t): 0.000 Batch (t): 0.379, 676.566/s, 169.142/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3555 (1.3598) Loss: 1.3555 (1.3598) -2024-09-02,18:48:44 | INFO | Train Epoch: 1 [ 691456/3655823 (19%)] Data (t): 0.000 Batch (t): 0.385, 676.789/s, 169.197/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3131 (1.3581) Loss: 1.3131 (1.3581) -2024-09-02,18:49:22 | INFO | Train Epoch: 1 [ 717056/3655823 (20%)] Data (t): 0.000 Batch (t): 0.379, 676.749/s, 169.187/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.4689 (1.3619) Loss: 1.4689 (1.3619) -2024-09-02,18:50:00 | INFO | Train Epoch: 1 [ 742656/3655823 (20%)] Data (t): 0.000 Batch (t): 0.379, 676.051/s, 169.013/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3362 (1.3611) Loss: 1.3362 (1.3611) -2024-09-02,18:50:38 | INFO | Train Epoch: 1 [ 768256/3655823 (21%)] Data (t): 0.000 Batch (t): 0.379, 675.688/s, 168.922/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.0804 (1.3520) Loss: 1.0804 (1.3520) -2024-09-02,18:51:15 | INFO | Train Epoch: 1 [ 793856/3655823 (22%)] Data (t): 0.000 Batch (t): 0.379, 675.277/s, 168.819/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.2939 (1.3502) Loss: 1.2939 (1.3502) -2024-09-02,18:51:53 | INFO | Train Epoch: 1 [ 819456/3655823 (22%)] Data (t): 0.000 Batch (t): 0.379, 676.982/s, 169.245/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.4794 (1.3541) Loss: 1.4794 (1.3541) -2024-09-02,18:52:31 | INFO | Train Epoch: 1 [ 845056/3655823 (23%)] Data (t): 0.000 Batch (t): 0.379, 676.448/s, 169.112/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.2374 (1.3507) Loss: 1.2374 (1.3507) -2024-09-02,18:53:09 | INFO | Train Epoch: 1 [ 870656/3655823 (24%)] Data (t): 0.000 Batch (t): 0.378, 676.251/s, 169.063/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3711 (1.3513) Loss: 1.3711 (1.3513) -2024-09-02,18:53:47 | INFO | Train Epoch: 1 [ 896256/3655823 (25%)] Data (t): 0.000 Batch (t): 0.378, 677.706/s, 169.427/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.0320 (1.3424) Loss: 1.0320 (1.3424) -2024-09-02,18:54:25 | INFO | Train Epoch: 1 [ 921856/3655823 (25%)] Data (t): 0.000 Batch (t): 0.379, 676.659/s, 169.165/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.2438 (1.3397) Loss: 1.2438 (1.3397) -2024-09-02,18:55:03 | INFO | Train Epoch: 1 [ 947456/3655823 (26%)] Data (t): 0.000 Batch (t): 0.379, 677.129/s, 169.282/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.0724 (1.3327) Loss: 1.0724 (1.3327) -2024-09-02,18:55:41 | INFO | Train Epoch: 1 [ 973056/3655823 (27%)] Data (t): 0.000 Batch (t): 0.381, 675.555/s, 168.889/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3111 (1.3321) Loss: 1.3111 (1.3321) -2024-09-02,18:56:19 | INFO | Train Epoch: 1 [ 998656/3655823 (27%)] Data (t): 0.000 Batch (t): 0.383, 676.767/s, 169.192/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3787 (1.3333) Loss: 1.3787 (1.3333) -2024-09-02,18:56:57 | INFO | Train Epoch: 1 [1024256/3655823 (28%)] Data (t): 0.000 Batch (t): 0.379, 675.381/s, 168.845/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.2580 (1.3315) Loss: 1.2580 (1.3315) -2024-09-02,18:57:35 | INFO | Train Epoch: 1 [1049856/3655823 (29%)] Data (t): 0.000 Batch (t): 0.379, 677.230/s, 169.308/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.1818 (1.3279) Loss: 1.1818 (1.3279) -2024-09-02,18:58:12 | INFO | Train Epoch: 1 [1075456/3655823 (29%)] Data (t): 0.000 Batch (t): 0.379, 674.269/s, 168.567/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.1513 (1.3238) Loss: 1.1513 (1.3238) -2024-09-02,18:58:50 | INFO | Train Epoch: 1 [1101056/3655823 (30%)] Data (t): 0.000 Batch (t): 0.379, 676.215/s, 169.054/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3069 (1.3234) Loss: 1.3069 (1.3234) -2024-09-02,18:59:28 | INFO | Train Epoch: 1 [1126656/3655823 (31%)] Data (t): 0.000 Batch (t): 0.379, 676.861/s, 169.215/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.1968 (1.3206) Loss: 1.1968 (1.3206) -2024-09-02,19:00:06 | INFO | Train Epoch: 1 [1152256/3655823 (32%)] Data (t): 0.000 Batch (t): 0.378, 676.704/s, 169.176/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3242 (1.3207) Loss: 1.3242 (1.3207) -2024-09-02,19:00:44 | INFO | Train Epoch: 1 [1177856/3655823 (32%)] Data (t): 0.000 Batch (t): 0.379, 676.378/s, 169.094/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.4719 (1.3239) Loss: 1.4719 (1.3239) -2024-09-02,19:01:22 | INFO | Train Epoch: 1 [1203456/3655823 (33%)] Data (t): 0.000 Batch (t): 0.379, 665.932/s, 166.483/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.4825 (1.3272) Loss: 1.4825 (1.3272) -2024-09-02,19:02:00 | INFO | Train Epoch: 1 [1229056/3655823 (34%)] Data (t): 0.000 Batch (t): 0.378, 675.871/s, 168.968/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3335 (1.3273) Loss: 1.3335 (1.3273) -2024-09-02,19:02:37 | INFO | Train Epoch: 1 [1254656/3655823 (34%)] Data (t): 0.000 Batch (t): 0.379, 676.136/s, 169.034/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3935 (1.3287) Loss: 1.3935 (1.3287) -2024-09-02,19:03:16 | INFO | Train Epoch: 1 [1280256/3655823 (35%)] Data (t): 0.000 Batch (t): 0.380, 676.243/s, 169.061/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.1273 (1.3247) Loss: 1.1273 (1.3247) -2024-09-02,19:03:54 | INFO | Train Epoch: 1 [1305856/3655823 (36%)] Data (t): 0.000 Batch (t): 0.383, 676.823/s, 169.206/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3286 (1.3248) Loss: 1.3286 (1.3248) -2024-09-02,19:04:32 | INFO | Train Epoch: 1 [1331456/3655823 (36%)] Data (t): 0.000 Batch (t): 0.378, 675.300/s, 168.825/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.2069 (1.3226) Loss: 1.2069 (1.3226) -2024-09-02,19:05:09 | INFO | Train Epoch: 1 [1357056/3655823 (37%)] Data (t): 0.000 Batch (t): 0.378, 677.890/s, 169.472/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.5278 (1.3264) Loss: 1.5278 (1.3264) -2024-09-02,19:05:47 | INFO | Train Epoch: 1 [1382656/3655823 (38%)] Data (t): 0.000 Batch (t): 0.378, 677.210/s, 169.302/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3578 (1.3269) Loss: 1.3578 (1.3269) -2024-09-02,19:06:25 | INFO | Train Epoch: 1 [1408256/3655823 (39%)] Data (t): 0.000 Batch (t): 0.378, 677.765/s, 169.441/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.5584 (1.3311) Loss: 1.5584 (1.3311) -2024-09-02,19:07:03 | INFO | Train Epoch: 1 [1433856/3655823 (39%)] Data (t): 0.000 Batch (t): 0.378, 676.684/s, 169.171/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.0684 (1.3265) Loss: 1.0684 (1.3265) -2024-09-02,19:07:41 | INFO | Train Epoch: 1 [1459456/3655823 (40%)] Data (t): 0.000 Batch (t): 0.378, 676.138/s, 169.034/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.4312 (1.3283) Loss: 1.4312 (1.3283) -2024-09-02,19:08:18 | INFO | Train Epoch: 1 [1485056/3655823 (41%)] Data (t): 0.000 Batch (t): 0.378, 678.006/s, 169.502/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3832 (1.3292) Loss: 1.3832 (1.3292) -2024-09-02,19:08:56 | INFO | Train Epoch: 1 [1510656/3655823 (41%)] Data (t): 0.000 Batch (t): 0.378, 678.038/s, 169.510/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3224 (1.3291) Loss: 1.3224 (1.3291) -2024-09-02,19:09:34 | INFO | Train Epoch: 1 [1536256/3655823 (42%)] Data (t): 0.000 Batch (t): 0.378, 676.621/s, 169.155/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.1920 (1.3268) Loss: 1.1920 (1.3268) -2024-09-02,19:10:12 | INFO | Train Epoch: 1 [1561856/3655823 (43%)] Data (t): 0.000 Batch (t): 0.378, 677.554/s, 169.388/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.1530 (1.3240) Loss: 1.1530 (1.3240) -2024-09-02,19:10:50 | INFO | Train Epoch: 1 [1587456/3655823 (43%)] Data (t): 0.000 Batch (t): 0.380, 678.509/s, 169.627/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.2508 (1.3229) Loss: 1.2508 (1.3229) -2024-09-02,19:11:28 | INFO | Train Epoch: 1 [1613056/3655823 (44%)] Data (t): 0.000 Batch (t): 0.378, 678.060/s, 169.515/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.2690 (1.3220) Loss: 1.2690 (1.3220) -2024-09-02,19:12:06 | INFO | Train Epoch: 1 [1638656/3655823 (45%)] Data (t): 0.000 Batch (t): 0.384, 677.897/s, 169.474/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3444 (1.3224) Loss: 1.3444 (1.3224) -2024-09-02,19:12:44 | INFO | Train Epoch: 1 [1664256/3655823 (46%)] Data (t): 0.000 Batch (t): 0.378, 678.119/s, 169.530/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3062 (1.3221) Loss: 1.3062 (1.3221) -2024-09-02,19:13:21 | INFO | Train Epoch: 1 [1689856/3655823 (46%)] Data (t): 0.000 Batch (t): 0.378, 678.805/s, 169.701/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.2509 (1.3211) Loss: 1.2509 (1.3211) -2024-09-02,19:13:59 | INFO | Train Epoch: 1 [1715456/3655823 (47%)] Data (t): 0.000 Batch (t): 0.378, 677.433/s, 169.358/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.0719 (1.3174) Loss: 1.0719 (1.3174) -2024-09-02,19:14:37 | INFO | Train Epoch: 1 [1741056/3655823 (48%)] Data (t): 0.000 Batch (t): 0.378, 676.230/s, 169.058/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3447 (1.3178) Loss: 1.3447 (1.3178) -2024-09-02,19:15:15 | INFO | Train Epoch: 1 [1766656/3655823 (48%)] Data (t): 0.000 Batch (t): 0.378, 677.450/s, 169.362/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3510 (1.3183) Loss: 1.3510 (1.3183) -2024-09-02,19:15:53 | INFO | Train Epoch: 1 [1792256/3655823 (49%)] Data (t): 0.000 Batch (t): 0.378, 676.687/s, 169.172/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.4492 (1.3201) Loss: 1.4492 (1.3201) -2024-09-02,19:16:31 | INFO | Train Epoch: 1 [1817856/3655823 (50%)] Data (t): 0.000 Batch (t): 0.378, 677.659/s, 169.415/s/gpu LR: 0.000001 Logit Scale: 100.000 Contrastive_loss: 1.3111 (1.3200) Loss: 1.3111 (1.3200) -2024-09-02,19:17:08 | INFO | Train Epoch: 1 [1843456/3655823 (50%)] Data (t): 0.000 Batch (t): 0.378, 677.358/s, 169.340/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3456 (1.3203) Loss: 1.3456 (1.3203) -2024-09-02,19:17:46 | INFO | Train Epoch: 1 [1869056/3655823 (51%)] Data (t): 0.000 Batch (t): 0.378, 677.352/s, 169.338/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.5031 (1.3228) Loss: 1.5031 (1.3228) -2024-09-02,19:18:24 | INFO | Train Epoch: 1 [1894656/3655823 (52%)] Data (t): 0.000 Batch (t): 0.380, 677.869/s, 169.467/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3079 (1.3226) Loss: 1.3079 (1.3226) -2024-09-02,19:19:02 | INFO | Train Epoch: 1 [1920256/3655823 (53%)] Data (t): 0.000 Batch (t): 0.378, 678.186/s, 169.547/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4192 (1.3239) Loss: 1.4192 (1.3239) -2024-09-02,19:19:40 | INFO | Train Epoch: 1 [1945856/3655823 (53%)] Data (t): 0.000 Batch (t): 0.384, 676.751/s, 169.188/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3721 (1.3245) Loss: 1.3721 (1.3245) -2024-09-02,19:20:18 | INFO | Train Epoch: 1 [1971456/3655823 (54%)] Data (t): 0.000 Batch (t): 0.378, 676.469/s, 169.117/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4978 (1.3267) Loss: 1.4978 (1.3267) -2024-09-02,19:20:56 | INFO | Train Epoch: 1 [1997056/3655823 (55%)] Data (t): 0.000 Batch (t): 0.378, 678.038/s, 169.509/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3179 (1.3266) Loss: 1.3179 (1.3266) -2024-09-02,19:21:34 | INFO | Train Epoch: 1 [2022656/3655823 (55%)] Data (t): 0.000 Batch (t): 0.378, 678.658/s, 169.664/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.6068 (1.3301) Loss: 1.6068 (1.3301) -2024-09-02,19:22:12 | INFO | Train Epoch: 1 [2048256/3655823 (56%)] Data (t): 0.000 Batch (t): 0.378, 678.065/s, 169.516/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.5001 (1.3322) Loss: 1.5001 (1.3322) -2024-09-02,19:22:49 | INFO | Train Epoch: 1 [2073856/3655823 (57%)] Data (t): 0.000 Batch (t): 0.378, 677.912/s, 169.478/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4725 (1.3339) Loss: 1.4725 (1.3339) -2024-09-02,19:23:27 | INFO | Train Epoch: 1 [2099456/3655823 (57%)] Data (t): 0.000 Batch (t): 0.378, 673.685/s, 168.421/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4260 (1.3350) Loss: 1.4260 (1.3350) -2024-09-02,19:24:05 | INFO | Train Epoch: 1 [2125056/3655823 (58%)] Data (t): 0.000 Batch (t): 0.378, 678.333/s, 169.583/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.1183 (1.3325) Loss: 1.1183 (1.3325) -2024-09-02,19:24:43 | INFO | Train Epoch: 1 [2150656/3655823 (59%)] Data (t): 0.000 Batch (t): 0.378, 677.766/s, 169.441/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.6450 (1.3361) Loss: 1.6450 (1.3361) -2024-09-02,19:25:21 | INFO | Train Epoch: 1 [2176256/3655823 (60%)] Data (t): 0.000 Batch (t): 0.378, 676.160/s, 169.040/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4786 (1.3378) Loss: 1.4786 (1.3378) -2024-09-02,19:25:59 | INFO | Train Epoch: 1 [2201856/3655823 (60%)] Data (t): 0.000 Batch (t): 0.380, 675.471/s, 168.868/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2688 (1.3370) Loss: 1.2688 (1.3370) -2024-09-02,19:26:36 | INFO | Train Epoch: 1 [2227456/3655823 (61%)] Data (t): 0.000 Batch (t): 0.378, 677.298/s, 169.324/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2953 (1.3365) Loss: 1.2953 (1.3365) -2024-09-02,19:27:15 | INFO | Train Epoch: 1 [2253056/3655823 (62%)] Data (t): 0.000 Batch (t): 0.384, 676.164/s, 169.041/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4181 (1.3374) Loss: 1.4181 (1.3374) -2024-09-02,19:27:53 | INFO | Train Epoch: 1 [2278656/3655823 (62%)] Data (t): 0.000 Batch (t): 0.378, 676.951/s, 169.238/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2973 (1.3370) Loss: 1.2973 (1.3370) -2024-09-02,19:28:30 | INFO | Train Epoch: 1 [2304256/3655823 (63%)] Data (t): 0.000 Batch (t): 0.378, 676.441/s, 169.110/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2935 (1.3365) Loss: 1.2935 (1.3365) -2024-09-02,19:29:08 | INFO | Train Epoch: 1 [2329856/3655823 (64%)] Data (t): 0.000 Batch (t): 0.378, 677.252/s, 169.313/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2943 (1.3361) Loss: 1.2943 (1.3361) -2024-09-02,19:29:46 | INFO | Train Epoch: 1 [2355456/3655823 (64%)] Data (t): 0.000 Batch (t): 0.378, 675.950/s, 168.987/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4487 (1.3373) Loss: 1.4487 (1.3373) -2024-09-02,19:30:24 | INFO | Train Epoch: 1 [2381056/3655823 (65%)] Data (t): 0.000 Batch (t): 0.378, 677.526/s, 169.382/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4694 (1.3387) Loss: 1.4694 (1.3387) -2024-09-02,19:31:02 | INFO | Train Epoch: 1 [2406656/3655823 (66%)] Data (t): 0.000 Batch (t): 0.378, 677.123/s, 169.281/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4633 (1.3400) Loss: 1.4633 (1.3400) -2024-09-02,19:31:40 | INFO | Train Epoch: 1 [2432256/3655823 (67%)] Data (t): 0.000 Batch (t): 0.378, 677.191/s, 169.298/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3039 (1.3396) Loss: 1.3039 (1.3396) -2024-09-02,19:32:17 | INFO | Train Epoch: 1 [2457856/3655823 (67%)] Data (t): 0.000 Batch (t): 0.378, 677.727/s, 169.432/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2910 (1.3391) Loss: 1.2910 (1.3391) -2024-09-02,19:32:55 | INFO | Train Epoch: 1 [2483456/3655823 (68%)] Data (t): 0.000 Batch (t): 0.378, 677.183/s, 169.296/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3252 (1.3390) Loss: 1.3252 (1.3390) -2024-09-02,19:33:33 | INFO | Train Epoch: 1 [2509056/3655823 (69%)] Data (t): 0.000 Batch (t): 0.379, 675.692/s, 168.923/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2814 (1.3384) Loss: 1.2814 (1.3384) -2024-09-02,19:34:11 | INFO | Train Epoch: 1 [2534656/3655823 (69%)] Data (t): 0.000 Batch (t): 0.381, 671.090/s, 167.773/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.1405 (1.3364) Loss: 1.1405 (1.3364) -2024-09-02,19:34:50 | INFO | Train Epoch: 1 [2560256/3655823 (70%)] Data (t): 0.000 Batch (t): 0.385, 676.786/s, 169.196/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4687 (1.3377) Loss: 1.4687 (1.3377) -2024-09-02,19:35:27 | INFO | Train Epoch: 1 [2585856/3655823 (71%)] Data (t): 0.000 Batch (t): 0.378, 677.773/s, 169.443/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4841 (1.3391) Loss: 1.4841 (1.3391) -2024-09-02,19:36:05 | INFO | Train Epoch: 1 [2611456/3655823 (71%)] Data (t): 0.000 Batch (t): 0.378, 677.054/s, 169.264/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3552 (1.3393) Loss: 1.3552 (1.3393) -2024-09-02,19:36:43 | INFO | Train Epoch: 1 [2637056/3655823 (72%)] Data (t): 0.000 Batch (t): 0.378, 673.968/s, 168.492/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4770 (1.3406) Loss: 1.4770 (1.3406) -2024-09-02,19:37:21 | INFO | Train Epoch: 1 [2662656/3655823 (73%)] Data (t): 0.000 Batch (t): 0.378, 676.861/s, 169.215/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.5643 (1.3428) Loss: 1.5643 (1.3428) -2024-09-02,19:37:59 | INFO | Train Epoch: 1 [2688256/3655823 (74%)] Data (t): 0.000 Batch (t): 0.378, 676.341/s, 169.085/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.0868 (1.3403) Loss: 1.0868 (1.3403) -2024-09-02,19:38:37 | INFO | Train Epoch: 1 [2713856/3655823 (74%)] Data (t): 0.000 Batch (t): 0.378, 677.789/s, 169.447/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4392 (1.3413) Loss: 1.4392 (1.3413) -2024-09-02,19:39:14 | INFO | Train Epoch: 1 [2739456/3655823 (75%)] Data (t): 0.000 Batch (t): 0.378, 677.783/s, 169.446/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3094 (1.3410) Loss: 1.3094 (1.3410) -2024-09-02,19:39:52 | INFO | Train Epoch: 1 [2765056/3655823 (76%)] Data (t): 0.000 Batch (t): 0.378, 694.539/s, 173.635/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3531 (1.3411) Loss: 1.3531 (1.3411) -2024-09-02,19:40:30 | INFO | Train Epoch: 1 [2790656/3655823 (76%)] Data (t): 0.000 Batch (t): 0.378, 677.922/s, 169.480/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4230 (1.3418) Loss: 1.4230 (1.3418) -2024-09-02,19:41:08 | INFO | Train Epoch: 1 [2816256/3655823 (77%)] Data (t): 0.000 Batch (t): 0.378, 676.674/s, 169.168/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3340 (1.3418) Loss: 1.3340 (1.3418) -2024-09-02,19:41:46 | INFO | Train Epoch: 1 [2841856/3655823 (78%)] Data (t): 0.000 Batch (t): 0.380, 675.274/s, 168.819/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.6616 (1.3446) Loss: 1.6616 (1.3446) -2024-09-02,19:42:24 | INFO | Train Epoch: 1 [2867456/3655823 (78%)] Data (t): 0.000 Batch (t): 0.380, 677.948/s, 169.487/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3492 (1.3447) Loss: 1.3492 (1.3447) -2024-09-02,19:43:02 | INFO | Train Epoch: 1 [2893056/3655823 (79%)] Data (t): 0.000 Batch (t): 0.382, 677.514/s, 169.379/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2959 (1.3442) Loss: 1.2959 (1.3442) -2024-09-02,19:43:40 | INFO | Train Epoch: 1 [2918656/3655823 (80%)] Data (t): 0.000 Batch (t): 0.378, 676.429/s, 169.107/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2472 (1.3434) Loss: 1.2472 (1.3434) -2024-09-02,19:44:18 | INFO | Train Epoch: 1 [2944256/3655823 (81%)] Data (t): 0.000 Batch (t): 0.378, 677.526/s, 169.382/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4289 (1.3441) Loss: 1.4289 (1.3441) -2024-09-02,19:44:55 | INFO | Train Epoch: 1 [2969856/3655823 (81%)] Data (t): 0.000 Batch (t): 0.378, 676.835/s, 169.209/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3872 (1.3445) Loss: 1.3872 (1.3445) -2024-09-02,19:45:33 | INFO | Train Epoch: 1 [2995456/3655823 (82%)] Data (t): 0.000 Batch (t): 0.378, 676.346/s, 169.086/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2906 (1.3440) Loss: 1.2906 (1.3440) -2024-09-02,19:46:11 | INFO | Train Epoch: 1 [3021056/3655823 (83%)] Data (t): 0.000 Batch (t): 0.378, 677.331/s, 169.333/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4437 (1.3449) Loss: 1.4437 (1.3449) -2024-09-02,19:46:49 | INFO | Train Epoch: 1 [3046656/3655823 (83%)] Data (t): 0.000 Batch (t): 0.378, 676.768/s, 169.192/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2601 (1.3442) Loss: 1.2601 (1.3442) -2024-09-02,19:47:27 | INFO | Train Epoch: 1 [3072256/3655823 (84%)] Data (t): 0.000 Batch (t): 0.378, 677.406/s, 169.351/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.1880 (1.3429) Loss: 1.1880 (1.3429) -2024-09-02,19:48:05 | INFO | Train Epoch: 1 [3097856/3655823 (85%)] Data (t): 0.000 Batch (t): 0.378, 677.267/s, 169.317/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.5226 (1.3443) Loss: 1.5226 (1.3443) -2024-09-02,19:48:42 | INFO | Train Epoch: 1 [3123456/3655823 (85%)] Data (t): 0.000 Batch (t): 0.378, 677.367/s, 169.342/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4617 (1.3453) Loss: 1.4617 (1.3453) -2024-09-02,19:49:20 | INFO | Train Epoch: 1 [3149056/3655823 (86%)] Data (t): 0.000 Batch (t): 0.380, 677.908/s, 169.477/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4784 (1.3464) Loss: 1.4784 (1.3464) -2024-09-02,19:49:58 | INFO | Train Epoch: 1 [3174656/3655823 (87%)] Data (t): 0.000 Batch (t): 0.378, 677.313/s, 169.328/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3648 (1.3465) Loss: 1.3648 (1.3465) -2024-09-02,19:50:36 | INFO | Train Epoch: 1 [3200256/3655823 (88%)] Data (t): 0.000 Batch (t): 0.384, 675.626/s, 168.907/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.1654 (1.3451) Loss: 1.1654 (1.3451) -2024-09-02,19:51:14 | INFO | Train Epoch: 1 [3225856/3655823 (88%)] Data (t): 0.000 Batch (t): 0.378, 677.134/s, 169.284/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2349 (1.3442) Loss: 1.2349 (1.3442) -2024-09-02,19:51:52 | INFO | Train Epoch: 1 [3251456/3655823 (89%)] Data (t): 0.000 Batch (t): 0.378, 676.566/s, 169.142/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3140 (1.3440) Loss: 1.3140 (1.3440) -2024-09-02,19:52:30 | INFO | Train Epoch: 1 [3277056/3655823 (90%)] Data (t): 0.000 Batch (t): 0.378, 677.606/s, 169.402/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2842 (1.3435) Loss: 1.2842 (1.3435) -2024-09-02,19:53:08 | INFO | Train Epoch: 1 [3302656/3655823 (90%)] Data (t): 0.000 Batch (t): 0.378, 678.056/s, 169.514/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4530 (1.3444) Loss: 1.4530 (1.3444) -2024-09-02,19:53:45 | INFO | Train Epoch: 1 [3328256/3655823 (91%)] Data (t): 0.000 Batch (t): 0.378, 678.353/s, 169.588/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3112 (1.3441) Loss: 1.3112 (1.3441) -2024-09-02,19:54:23 | INFO | Train Epoch: 1 [3353856/3655823 (92%)] Data (t): 0.000 Batch (t): 0.378, 676.432/s, 169.108/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.1092 (1.3423) Loss: 1.1092 (1.3423) -2024-09-02,19:55:01 | INFO | Train Epoch: 1 [3379456/3655823 (92%)] Data (t): 0.000 Batch (t): 0.378, 678.571/s, 169.643/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2593 (1.3417) Loss: 1.2593 (1.3417) -2024-09-02,19:55:39 | INFO | Train Epoch: 1 [3405056/3655823 (93%)] Data (t): 0.000 Batch (t): 0.378, 676.829/s, 169.207/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3618 (1.3419) Loss: 1.3618 (1.3419) -2024-09-02,19:56:17 | INFO | Train Epoch: 1 [3430656/3655823 (94%)] Data (t): 0.000 Batch (t): 0.378, 677.256/s, 169.314/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3486 (1.3419) Loss: 1.3486 (1.3419) -2024-09-02,19:56:54 | INFO | Train Epoch: 1 [3456256/3655823 (95%)] Data (t): 0.000 Batch (t): 0.378, 677.226/s, 169.307/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.1109 (1.3402) Loss: 1.1109 (1.3402) -2024-09-02,19:57:32 | INFO | Train Epoch: 1 [3481856/3655823 (95%)] Data (t): 0.000 Batch (t): 0.380, 677.971/s, 169.493/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.1857 (1.3391) Loss: 1.1857 (1.3391) -2024-09-02,19:58:11 | INFO | Train Epoch: 1 [3507456/3655823 (96%)] Data (t): 0.000 Batch (t): 0.384, 678.350/s, 169.588/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.1857 (1.3380) Loss: 1.1857 (1.3380) -2024-09-02,19:58:49 | INFO | Train Epoch: 1 [3533056/3655823 (97%)] Data (t): 0.000 Batch (t): 0.378, 676.603/s, 169.151/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3314 (1.3379) Loss: 1.3314 (1.3379) -2024-09-02,19:59:26 | INFO | Train Epoch: 1 [3558656/3655823 (97%)] Data (t): 0.000 Batch (t): 0.378, 677.256/s, 169.314/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3126 (1.3377) Loss: 1.3126 (1.3377) -2024-09-02,20:00:04 | INFO | Train Epoch: 1 [3584256/3655823 (98%)] Data (t): 0.000 Batch (t): 0.378, 677.511/s, 169.378/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2343 (1.3370) Loss: 1.2343 (1.3370) -2024-09-02,20:00:42 | INFO | Train Epoch: 1 [3609856/3655823 (99%)] Data (t): 0.000 Batch (t): 0.378, 679.617/s, 169.904/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2410 (1.3363) Loss: 1.2410 (1.3363) -2024-09-02,20:01:20 | INFO | Train Epoch: 1 [3635456/3655823 (99%)] Data (t): 0.000 Batch (t): 0.378, 677.979/s, 169.495/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3356 (1.3363) Loss: 1.3356 (1.3363) -2024-09-02,20:01:50 | INFO | Train Epoch: 1 [3655680/3655823 (100%)] Data (t): 0.001 Batch (t): 0.378, 682.050/s, 170.513/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3771 (1.3366) Loss: 1.3771 (1.3366) -2024-09-02,20:02:00 | INFO | Start epoch 2 -2024-09-02,20:02:02 | INFO | Train Epoch: 2 [ 256/3655823 (0%)] Data (t): 1.319 Batch (t): 1.710, 149.718/s, 37.4296/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2449 (1.2449) Loss: 1.2449 (1.2449) -2024-09-02,20:02:40 | INFO | Train Epoch: 2 [ 25856/3655823 (1%)] Data (t): 0.000 Batch (t): 0.378, 676.861/s, 169.215/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3189 (1.2819) Loss: 1.3189 (1.2819) -2024-09-02,20:03:17 | INFO | Train Epoch: 2 [ 51456/3655823 (1%)] Data (t): 0.000 Batch (t): 0.378, 676.681/s, 169.170/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3308 (1.2982) Loss: 1.3308 (1.2982) -2024-09-02,20:03:55 | INFO | Train Epoch: 2 [ 77056/3655823 (2%)] Data (t): 0.000 Batch (t): 0.378, 677.320/s, 169.330/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3594 (1.3135) Loss: 1.3594 (1.3135) -2024-09-02,20:04:33 | INFO | Train Epoch: 2 [ 102656/3655823 (3%)] Data (t): 0.000 Batch (t): 0.380, 677.514/s, 169.379/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4559 (1.3420) Loss: 1.4559 (1.3420) -2024-09-02,20:05:11 | INFO | Train Epoch: 2 [ 128256/3655823 (4%)] Data (t): 0.000 Batch (t): 0.378, 676.716/s, 169.179/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4018 (1.3519) Loss: 1.4018 (1.3519) -2024-09-02,20:05:50 | INFO | Train Epoch: 2 [ 153856/3655823 (4%)] Data (t): 0.000 Batch (t): 0.385, 677.741/s, 169.435/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4088 (1.3601) Loss: 1.4088 (1.3601) -2024-09-02,20:06:27 | INFO | Train Epoch: 2 [ 179456/3655823 (5%)] Data (t): 0.000 Batch (t): 0.378, 677.493/s, 169.373/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2922 (1.3516) Loss: 1.2922 (1.3516) -2024-09-02,20:07:05 | INFO | Train Epoch: 2 [ 205056/3655823 (6%)] Data (t): 0.000 Batch (t): 0.378, 678.098/s, 169.525/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2421 (1.3394) Loss: 1.2421 (1.3394) -2024-09-02,20:07:43 | INFO | Train Epoch: 2 [ 230656/3655823 (6%)] Data (t): 0.000 Batch (t): 0.378, 678.278/s, 169.569/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3272 (1.3382) Loss: 1.3272 (1.3382) -2024-09-02,20:08:21 | INFO | Train Epoch: 2 [ 256256/3655823 (7%)] Data (t): 0.000 Batch (t): 0.378, 677.266/s, 169.317/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.5243 (1.3551) Loss: 1.5243 (1.3551) -2024-09-02,20:08:58 | INFO | Train Epoch: 2 [ 281856/3655823 (8%)] Data (t): 0.000 Batch (t): 0.378, 677.565/s, 169.391/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4867 (1.3661) Loss: 1.4867 (1.3661) -2024-09-02,20:09:36 | INFO | Train Epoch: 2 [ 307456/3655823 (8%)] Data (t): 0.000 Batch (t): 0.378, 678.309/s, 169.577/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2077 (1.3539) Loss: 1.2077 (1.3539) -2024-09-02,20:10:14 | INFO | Train Epoch: 2 [ 333056/3655823 (9%)] Data (t): 0.000 Batch (t): 0.378, 677.762/s, 169.441/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3039 (1.3503) Loss: 1.3039 (1.3503) -2024-09-02,20:10:52 | INFO | Train Epoch: 2 [ 358656/3655823 (10%)] Data (t): 0.000 Batch (t): 0.378, 678.020/s, 169.505/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3097 (1.3476) Loss: 1.3097 (1.3476) -2024-09-02,20:11:30 | INFO | Train Epoch: 2 [ 384256/3655823 (11%)] Data (t): 0.000 Batch (t): 0.378, 677.468/s, 169.367/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2229 (1.3398) Loss: 1.2229 (1.3398) -2024-09-02,20:12:08 | INFO | Train Epoch: 2 [ 409856/3655823 (11%)] Data (t): 0.000 Batch (t): 0.380, 678.148/s, 169.537/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4866 (1.3485) Loss: 1.4866 (1.3485) -2024-09-02,20:12:45 | INFO | Train Epoch: 2 [ 435456/3655823 (12%)] Data (t): 0.000 Batch (t): 0.378, 677.281/s, 169.320/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2608 (1.3436) Loss: 1.2608 (1.3436) -2024-09-02,20:13:23 | INFO | Train Epoch: 2 [ 461056/3655823 (13%)] Data (t): 0.000 Batch (t): 0.380, 677.689/s, 169.422/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.5716 (1.3556) Loss: 1.5716 (1.3556) -2024-09-02,20:14:02 | INFO | Train Epoch: 2 [ 486656/3655823 (13%)] Data (t): 0.000 Batch (t): 0.382, 677.783/s, 169.446/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3118 (1.3534) Loss: 1.3118 (1.3534) -2024-09-02,20:14:39 | INFO | Train Epoch: 2 [ 512256/3655823 (14%)] Data (t): 0.000 Batch (t): 0.378, 677.796/s, 169.449/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3275 (1.3522) Loss: 1.3275 (1.3522) -2024-09-02,20:15:17 | INFO | Train Epoch: 2 [ 537856/3655823 (15%)] Data (t): 0.000 Batch (t): 0.378, 678.541/s, 169.635/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4273 (1.3556) Loss: 1.4273 (1.3556) -2024-09-02,20:15:55 | INFO | Train Epoch: 2 [ 563456/3655823 (15%)] Data (t): 0.000 Batch (t): 0.378, 677.061/s, 169.265/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2714 (1.3519) Loss: 1.2714 (1.3519) -2024-09-02,20:16:33 | INFO | Train Epoch: 2 [ 589056/3655823 (16%)] Data (t): 0.000 Batch (t): 0.378, 678.619/s, 169.655/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3372 (1.3513) Loss: 1.3372 (1.3513) -2024-09-02,20:17:10 | INFO | Train Epoch: 2 [ 614656/3655823 (17%)] Data (t): 0.000 Batch (t): 0.378, 678.221/s, 169.555/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.0998 (1.3412) Loss: 1.0998 (1.3412) -2024-09-02,20:17:48 | INFO | Train Epoch: 2 [ 640256/3655823 (18%)] Data (t): 0.000 Batch (t): 0.378, 676.796/s, 169.199/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.5302 (1.3485) Loss: 1.5302 (1.3485) -2024-09-02,20:18:26 | INFO | Train Epoch: 2 [ 665856/3655823 (18%)] Data (t): 0.000 Batch (t): 0.378, 677.731/s, 169.433/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4029 (1.3505) Loss: 1.4029 (1.3505) -2024-09-02,20:19:04 | INFO | Train Epoch: 2 [ 691456/3655823 (19%)] Data (t): 0.000 Batch (t): 0.378, 677.991/s, 169.498/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.5002 (1.3559) Loss: 1.5002 (1.3559) -2024-09-02,20:19:42 | INFO | Train Epoch: 2 [ 717056/3655823 (20%)] Data (t): 0.000 Batch (t): 0.380, 678.621/s, 169.655/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3825 (1.3568) Loss: 1.3825 (1.3568) -2024-09-02,20:20:20 | INFO | Train Epoch: 2 [ 742656/3655823 (20%)] Data (t): 0.000 Batch (t): 0.378, 677.083/s, 169.271/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4232 (1.3590) Loss: 1.4232 (1.3590) -2024-09-02,20:20:57 | INFO | Train Epoch: 2 [ 768256/3655823 (21%)] Data (t): 0.000 Batch (t): 0.380, 435.518/s, 108.880/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.0991 (1.3506) Loss: 1.0991 (1.3506) -2024-09-02,20:21:36 | INFO | Train Epoch: 2 [ 793856/3655823 (22%)] Data (t): 0.000 Batch (t): 0.382, 676.688/s, 169.172/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4253 (1.3530) Loss: 1.4253 (1.3530) -2024-09-02,20:22:13 | INFO | Train Epoch: 2 [ 819456/3655823 (22%)] Data (t): 0.000 Batch (t): 0.378, 678.179/s, 169.545/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3273 (1.3522) Loss: 1.3273 (1.3522) -2024-09-02,20:22:51 | INFO | Train Epoch: 2 [ 845056/3655823 (23%)] Data (t): 0.000 Batch (t): 0.378, 677.533/s, 169.383/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.1739 (1.3469) Loss: 1.1739 (1.3469) -2024-09-02,20:23:29 | INFO | Train Epoch: 2 [ 870656/3655823 (24%)] Data (t): 0.000 Batch (t): 0.378, 677.902/s, 169.476/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2975 (1.3455) Loss: 1.2975 (1.3455) -2024-09-02,20:24:07 | INFO | Train Epoch: 2 [ 896256/3655823 (25%)] Data (t): 0.000 Batch (t): 0.378, 676.265/s, 169.066/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.1678 (1.3406) Loss: 1.1678 (1.3406) -2024-09-02,20:24:45 | INFO | Train Epoch: 2 [ 921856/3655823 (25%)] Data (t): 0.000 Batch (t): 0.378, 677.108/s, 169.277/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2021 (1.3368) Loss: 1.2021 (1.3368) -2024-09-02,20:25:22 | INFO | Train Epoch: 2 [ 947456/3655823 (26%)] Data (t): 0.000 Batch (t): 0.378, 678.126/s, 169.531/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.1532 (1.3320) Loss: 1.1532 (1.3320) -2024-09-02,20:26:00 | INFO | Train Epoch: 2 [ 973056/3655823 (27%)] Data (t): 0.000 Batch (t): 0.378, 677.229/s, 169.307/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3183 (1.3317) Loss: 1.3183 (1.3317) -2024-09-02,20:26:38 | INFO | Train Epoch: 2 [ 998656/3655823 (27%)] Data (t): 0.000 Batch (t): 0.378, 677.786/s, 169.447/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2127 (1.3287) Loss: 1.2127 (1.3287) -2024-09-02,20:27:16 | INFO | Train Epoch: 2 [1024256/3655823 (28%)] Data (t): 0.000 Batch (t): 0.378, 677.390/s, 169.347/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2672 (1.3272) Loss: 1.2672 (1.3272) -2024-09-02,20:27:54 | INFO | Train Epoch: 2 [1049856/3655823 (29%)] Data (t): 0.000 Batch (t): 0.380, 677.524/s, 169.381/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2595 (1.3256) Loss: 1.2595 (1.3256) -2024-09-02,20:28:31 | INFO | Train Epoch: 2 [1075456/3655823 (29%)] Data (t): 0.000 Batch (t): 0.378, 677.786/s, 169.446/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.6476 (1.3331) Loss: 1.6476 (1.3331) -2024-09-02,20:29:10 | INFO | Train Epoch: 2 [1101056/3655823 (30%)] Data (t): 0.000 Batch (t): 0.384, 677.664/s, 169.416/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4891 (1.3366) Loss: 1.4891 (1.3366) -2024-09-02,20:29:48 | INFO | Train Epoch: 2 [1126656/3655823 (31%)] Data (t): 0.000 Batch (t): 0.378, 677.655/s, 169.414/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4163 (1.3384) Loss: 1.4163 (1.3384) -2024-09-02,20:30:25 | INFO | Train Epoch: 2 [1152256/3655823 (32%)] Data (t): 0.000 Batch (t): 0.378, 678.294/s, 169.573/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2264 (1.3359) Loss: 1.2264 (1.3359) -2024-09-02,20:31:03 | INFO | Train Epoch: 2 [1177856/3655823 (32%)] Data (t): 0.000 Batch (t): 0.378, 675.881/s, 168.970/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.5726 (1.3410) Loss: 1.5726 (1.3410) -2024-09-02,20:31:41 | INFO | Train Epoch: 2 [1203456/3655823 (33%)] Data (t): 0.000 Batch (t): 0.378, 677.321/s, 169.330/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2750 (1.3396) Loss: 1.2750 (1.3396) -2024-09-02,20:32:19 | INFO | Train Epoch: 2 [1229056/3655823 (34%)] Data (t): 0.000 Batch (t): 0.378, 678.268/s, 169.567/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4289 (1.3414) Loss: 1.4289 (1.3414) -2024-09-02,20:32:56 | INFO | Train Epoch: 2 [1254656/3655823 (34%)] Data (t): 0.000 Batch (t): 0.378, 677.645/s, 169.411/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3045 (1.3407) Loss: 1.3045 (1.3407) -2024-09-02,20:33:34 | INFO | Train Epoch: 2 [1280256/3655823 (35%)] Data (t): 0.000 Batch (t): 0.378, 675.694/s, 168.923/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2525 (1.3390) Loss: 1.2525 (1.3390) -2024-09-02,20:34:12 | INFO | Train Epoch: 2 [1305856/3655823 (36%)] Data (t): 0.000 Batch (t): 0.378, 677.159/s, 169.290/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.6430 (1.3448) Loss: 1.6430 (1.3448) -2024-09-02,20:34:50 | INFO | Train Epoch: 2 [1331456/3655823 (36%)] Data (t): 0.000 Batch (t): 0.378, 678.489/s, 169.622/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3494 (1.3449) Loss: 1.3494 (1.3449) -2024-09-02,20:35:28 | INFO | Train Epoch: 2 [1357056/3655823 (37%)] Data (t): 0.000 Batch (t): 0.380, 677.029/s, 169.257/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.0992 (1.3403) Loss: 1.0992 (1.3403) -2024-09-02,20:36:06 | INFO | Train Epoch: 2 [1382656/3655823 (38%)] Data (t): 0.000 Batch (t): 0.378, 677.663/s, 169.416/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.5391 (1.3440) Loss: 1.5391 (1.3440) -2024-09-02,20:36:44 | INFO | Train Epoch: 2 [1408256/3655823 (39%)] Data (t): 0.000 Batch (t): 0.384, 677.486/s, 169.372/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2099 (1.3416) Loss: 1.2099 (1.3416) -2024-09-02,20:37:22 | INFO | Train Epoch: 2 [1433856/3655823 (39%)] Data (t): 0.000 Batch (t): 0.378, 678.183/s, 169.546/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3242 (1.3413) Loss: 1.3242 (1.3413) -2024-09-02,20:37:59 | INFO | Train Epoch: 2 [1459456/3655823 (40%)] Data (t): 0.000 Batch (t): 0.378, 677.552/s, 169.388/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2776 (1.3402) Loss: 1.2776 (1.3402) -2024-09-02,20:38:37 | INFO | Train Epoch: 2 [1485056/3655823 (41%)] Data (t): 0.000 Batch (t): 0.378, 678.564/s, 169.641/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2871 (1.3393) Loss: 1.2871 (1.3393) -2024-09-02,20:39:15 | INFO | Train Epoch: 2 [1510656/3655823 (41%)] Data (t): 0.000 Batch (t): 0.378, 677.467/s, 169.367/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.1060 (1.3354) Loss: 1.1060 (1.3354) -2024-09-02,20:39:53 | INFO | Train Epoch: 2 [1536256/3655823 (42%)] Data (t): 0.000 Batch (t): 0.378, 677.348/s, 169.337/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.0803 (1.3312) Loss: 1.0803 (1.3312) -2024-09-02,20:40:31 | INFO | Train Epoch: 2 [1561856/3655823 (43%)] Data (t): 0.000 Batch (t): 0.378, 678.910/s, 169.728/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.1991 (1.3291) Loss: 1.1991 (1.3291) -2024-09-02,20:41:08 | INFO | Train Epoch: 2 [1587456/3655823 (43%)] Data (t): 0.000 Batch (t): 0.378, 677.717/s, 169.429/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2045 (1.3271) Loss: 1.2045 (1.3271) -2024-09-02,20:41:46 | INFO | Train Epoch: 2 [1613056/3655823 (44%)] Data (t): 0.000 Batch (t): 0.378, 676.006/s, 169.002/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4872 (1.3296) Loss: 1.4872 (1.3296) -2024-09-02,20:42:24 | INFO | Train Epoch: 2 [1638656/3655823 (45%)] Data (t): 0.000 Batch (t): 0.378, 678.952/s, 169.738/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3148 (1.3294) Loss: 1.3148 (1.3294) -2024-09-02,20:43:02 | INFO | Train Epoch: 2 [1664256/3655823 (46%)] Data (t): 0.000 Batch (t): 0.380, 677.703/s, 169.426/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2000 (1.3274) Loss: 1.2000 (1.3274) -2024-09-02,20:43:40 | INFO | Train Epoch: 2 [1689856/3655823 (46%)] Data (t): 0.000 Batch (t): 0.378, 677.683/s, 169.421/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.1651 (1.3250) Loss: 1.1651 (1.3250) -2024-09-02,20:44:18 | INFO | Train Epoch: 2 [1715456/3655823 (47%)] Data (t): 0.000 Batch (t): 0.384, 677.744/s, 169.436/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.1259 (1.3220) Loss: 1.1259 (1.3220) -2024-09-02,20:44:56 | INFO | Train Epoch: 2 [1741056/3655823 (48%)] Data (t): 0.000 Batch (t): 0.378, 679.044/s, 169.761/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4703 (1.3242) Loss: 1.4703 (1.3242) -2024-09-02,20:45:34 | INFO | Train Epoch: 2 [1766656/3655823 (48%)] Data (t): 0.000 Batch (t): 0.378, 677.998/s, 169.500/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3210 (1.3241) Loss: 1.3210 (1.3241) -2024-09-02,20:46:12 | INFO | Train Epoch: 2 [1792256/3655823 (49%)] Data (t): 0.000 Batch (t): 0.378, 677.743/s, 169.436/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3419 (1.3244) Loss: 1.3419 (1.3244) -2024-09-02,20:46:49 | INFO | Train Epoch: 2 [1817856/3655823 (50%)] Data (t): 0.000 Batch (t): 0.378, 677.919/s, 169.480/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.1527 (1.3220) Loss: 1.1527 (1.3220) -2024-09-02,20:47:27 | INFO | Train Epoch: 2 [1843456/3655823 (50%)] Data (t): 0.000 Batch (t): 0.378, 676.520/s, 169.130/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3024 (1.3217) Loss: 1.3024 (1.3217) -2024-09-02,20:48:05 | INFO | Train Epoch: 2 [1869056/3655823 (51%)] Data (t): 0.000 Batch (t): 0.378, 677.188/s, 169.297/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4475 (1.3234) Loss: 1.4475 (1.3234) -2024-09-02,20:48:43 | INFO | Train Epoch: 2 [1894656/3655823 (52%)] Data (t): 0.000 Batch (t): 0.378, 677.400/s, 169.350/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4376 (1.3250) Loss: 1.4376 (1.3250) -2024-09-02,20:49:20 | INFO | Train Epoch: 2 [1920256/3655823 (53%)] Data (t): 0.000 Batch (t): 0.378, 676.134/s, 169.033/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2484 (1.3240) Loss: 1.2484 (1.3240) -2024-09-02,20:49:58 | INFO | Train Epoch: 2 [1945856/3655823 (53%)] Data (t): 0.000 Batch (t): 0.378, 678.072/s, 169.518/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4302 (1.3253) Loss: 1.4302 (1.3253) -2024-09-02,20:50:36 | INFO | Train Epoch: 2 [1971456/3655823 (54%)] Data (t): 0.000 Batch (t): 0.378, 678.164/s, 169.541/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.5555 (1.3283) Loss: 1.5555 (1.3283) -2024-09-02,20:51:14 | INFO | Train Epoch: 2 [1997056/3655823 (55%)] Data (t): 0.000 Batch (t): 0.380, 678.117/s, 169.529/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2496 (1.3273) Loss: 1.2496 (1.3273) -2024-09-02,20:51:52 | INFO | Train Epoch: 2 [2022656/3655823 (55%)] Data (t): 0.000 Batch (t): 0.382, 436.934/s, 109.234/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2623 (1.3265) Loss: 1.2623 (1.3265) -2024-09-02,20:52:30 | INFO | Train Epoch: 2 [2048256/3655823 (56%)] Data (t): 0.000 Batch (t): 0.380, 677.176/s, 169.294/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4024 (1.3274) Loss: 1.4024 (1.3274) -2024-09-02,20:53:08 | INFO | Train Epoch: 2 [2073856/3655823 (57%)] Data (t): 0.000 Batch (t): 0.378, 678.544/s, 169.636/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4059 (1.3284) Loss: 1.4059 (1.3284) -2024-09-02,20:53:46 | INFO | Train Epoch: 2 [2099456/3655823 (57%)] Data (t): 0.000 Batch (t): 0.378, 678.434/s, 169.608/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.1841 (1.3266) Loss: 1.1841 (1.3266) -2024-09-02,20:54:24 | INFO | Train Epoch: 2 [2125056/3655823 (58%)] Data (t): 0.000 Batch (t): 0.378, 675.548/s, 168.887/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4134 (1.3277) Loss: 1.4134 (1.3277) -2024-09-02,20:55:01 | INFO | Train Epoch: 2 [2150656/3655823 (59%)] Data (t): 0.000 Batch (t): 0.378, 676.085/s, 169.021/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2536 (1.3268) Loss: 1.2536 (1.3268) -2024-09-02,20:55:39 | INFO | Train Epoch: 2 [2176256/3655823 (60%)] Data (t): 0.000 Batch (t): 0.378, 677.768/s, 169.442/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2858 (1.3263) Loss: 1.2858 (1.3263) -2024-09-02,20:56:17 | INFO | Train Epoch: 2 [2201856/3655823 (60%)] Data (t): 0.000 Batch (t): 0.378, 678.261/s, 169.565/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2195 (1.3251) Loss: 1.2195 (1.3251) -2024-09-02,20:56:55 | INFO | Train Epoch: 2 [2227456/3655823 (61%)] Data (t): 0.000 Batch (t): 0.378, 676.778/s, 169.195/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4445 (1.3265) Loss: 1.4445 (1.3265) -2024-09-02,20:57:33 | INFO | Train Epoch: 2 [2253056/3655823 (62%)] Data (t): 0.000 Batch (t): 0.378, 676.155/s, 169.039/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.5238 (1.3287) Loss: 1.5238 (1.3287) -2024-09-02,20:58:10 | INFO | Train Epoch: 2 [2278656/3655823 (62%)] Data (t): 0.000 Batch (t): 0.378, 676.956/s, 169.239/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2976 (1.3283) Loss: 1.2976 (1.3283) -2024-09-02,20:58:48 | INFO | Train Epoch: 2 [2304256/3655823 (63%)] Data (t): 0.000 Batch (t): 0.380, 677.609/s, 169.402/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2780 (1.3278) Loss: 1.2780 (1.3278) -2024-09-02,20:59:26 | INFO | Train Epoch: 2 [2329856/3655823 (64%)] Data (t): 0.000 Batch (t): 0.379, 677.970/s, 169.493/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2000 (1.3264) Loss: 1.2000 (1.3264) -2024-09-02,21:00:04 | INFO | Train Epoch: 2 [2355456/3655823 (64%)] Data (t): 0.000 Batch (t): 0.382, 679.044/s, 169.761/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4016 (1.3272) Loss: 1.4016 (1.3272) -2024-09-02,21:00:42 | INFO | Train Epoch: 2 [2381056/3655823 (65%)] Data (t): 0.000 Batch (t): 0.377, 678.672/s, 169.668/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4712 (1.3287) Loss: 1.4712 (1.3287) -2024-09-02,21:01:20 | INFO | Train Epoch: 2 [2406656/3655823 (66%)] Data (t): 0.000 Batch (t): 0.378, 678.801/s, 169.700/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3562 (1.3290) Loss: 1.3562 (1.3290) -2024-09-02,21:01:58 | INFO | Train Epoch: 2 [2432256/3655823 (67%)] Data (t): 0.000 Batch (t): 0.377, 679.188/s, 169.797/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2049 (1.3277) Loss: 1.2049 (1.3277) -2024-09-02,21:02:35 | INFO | Train Epoch: 2 [2457856/3655823 (67%)] Data (t): 0.000 Batch (t): 0.377, 677.978/s, 169.494/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3495 (1.3279) Loss: 1.3495 (1.3279) -2024-09-02,21:03:13 | INFO | Train Epoch: 2 [2483456/3655823 (68%)] Data (t): 0.000 Batch (t): 0.378, 678.681/s, 169.670/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.1933 (1.3266) Loss: 1.1933 (1.3266) -2024-09-02,21:03:51 | INFO | Train Epoch: 2 [2509056/3655823 (69%)] Data (t): 0.000 Batch (t): 0.378, 678.829/s, 169.707/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.6302 (1.3296) Loss: 1.6302 (1.3296) -2024-09-02,21:04:29 | INFO | Train Epoch: 2 [2534656/3655823 (69%)] Data (t): 0.000 Batch (t): 0.378, 677.655/s, 169.414/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4722 (1.3311) Loss: 1.4722 (1.3311) -2024-09-02,21:05:06 | INFO | Train Epoch: 2 [2560256/3655823 (70%)] Data (t): 0.000 Batch (t): 0.377, 678.714/s, 169.678/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2526 (1.3303) Loss: 1.2526 (1.3303) -2024-09-02,21:05:44 | INFO | Train Epoch: 2 [2585856/3655823 (71%)] Data (t): 0.000 Batch (t): 0.378, 677.076/s, 169.269/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.5646 (1.3326) Loss: 1.5646 (1.3326) -2024-09-02,21:06:22 | INFO | Train Epoch: 2 [2611456/3655823 (71%)] Data (t): 0.000 Batch (t): 0.380, 678.035/s, 169.509/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.0440 (1.3298) Loss: 1.0440 (1.3298) -2024-09-02,21:07:00 | INFO | Train Epoch: 2 [2637056/3655823 (72%)] Data (t): 0.000 Batch (t): 0.378, 678.280/s, 169.570/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3705 (1.3302) Loss: 1.3705 (1.3302) -2024-09-02,21:07:38 | INFO | Train Epoch: 2 [2662656/3655823 (73%)] Data (t): 0.000 Batch (t): 0.384, 678.158/s, 169.540/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3095 (1.3300) Loss: 1.3095 (1.3300) -2024-09-02,21:08:16 | INFO | Train Epoch: 2 [2688256/3655823 (74%)] Data (t): 0.000 Batch (t): 0.378, 677.534/s, 169.384/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2514 (1.3292) Loss: 1.2514 (1.3292) -2024-09-02,21:08:54 | INFO | Train Epoch: 2 [2713856/3655823 (74%)] Data (t): 0.000 Batch (t): 0.378, 676.951/s, 169.238/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2341 (1.3283) Loss: 1.2341 (1.3283) -2024-09-02,21:09:32 | INFO | Train Epoch: 2 [2739456/3655823 (75%)] Data (t): 0.000 Batch (t): 0.378, 676.280/s, 169.070/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3958 (1.3290) Loss: 1.3958 (1.3290) -2024-09-02,21:10:09 | INFO | Train Epoch: 2 [2765056/3655823 (76%)] Data (t): 0.000 Batch (t): 0.378, 676.302/s, 169.076/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2967 (1.3287) Loss: 1.2967 (1.3287) -2024-09-02,21:10:47 | INFO | Train Epoch: 2 [2790656/3655823 (76%)] Data (t): 0.000 Batch (t): 0.378, 677.827/s, 169.457/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3618 (1.3290) Loss: 1.3618 (1.3290) -2024-09-02,21:11:25 | INFO | Train Epoch: 2 [2816256/3655823 (77%)] Data (t): 0.000 Batch (t): 0.378, 676.040/s, 169.010/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2839 (1.3286) Loss: 1.2839 (1.3286) -2024-09-02,21:12:03 | INFO | Train Epoch: 2 [2841856/3655823 (78%)] Data (t): 0.000 Batch (t): 0.378, 677.107/s, 169.277/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3783 (1.3290) Loss: 1.3783 (1.3290) -2024-09-02,21:12:41 | INFO | Train Epoch: 2 [2867456/3655823 (78%)] Data (t): 0.000 Batch (t): 0.378, 678.247/s, 169.562/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.5307 (1.3308) Loss: 1.5307 (1.3308) -2024-09-02,21:13:18 | INFO | Train Epoch: 2 [2893056/3655823 (79%)] Data (t): 0.000 Batch (t): 0.378, 678.268/s, 169.567/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2354 (1.3300) Loss: 1.2354 (1.3300) -2024-09-02,21:13:56 | INFO | Train Epoch: 2 [2918656/3655823 (80%)] Data (t): 0.000 Batch (t): 0.378, 676.731/s, 169.183/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2266 (1.3291) Loss: 1.2266 (1.3291) -2024-09-02,21:14:34 | INFO | Train Epoch: 2 [2944256/3655823 (81%)] Data (t): 0.000 Batch (t): 0.380, 678.249/s, 169.562/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2062 (1.3280) Loss: 1.2062 (1.3280) -2024-09-02,21:15:13 | INFO | Train Epoch: 2 [2969856/3655823 (81%)] Data (t): 0.000 Batch (t): 0.384, 677.535/s, 169.384/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3600 (1.3283) Loss: 1.3600 (1.3283) -2024-09-02,21:15:50 | INFO | Train Epoch: 2 [2995456/3655823 (82%)] Data (t): 0.000 Batch (t): 0.378, 677.787/s, 169.447/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3766 (1.3287) Loss: 1.3766 (1.3287) -2024-09-02,21:16:28 | INFO | Train Epoch: 2 [3021056/3655823 (83%)] Data (t): 0.000 Batch (t): 0.378, 678.284/s, 169.571/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3064 (1.3285) Loss: 1.3064 (1.3285) -2024-09-02,21:17:06 | INFO | Train Epoch: 2 [3046656/3655823 (83%)] Data (t): 0.000 Batch (t): 0.378, 676.858/s, 169.215/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3456 (1.3286) Loss: 1.3456 (1.3286) -2024-09-02,21:17:44 | INFO | Train Epoch: 2 [3072256/3655823 (84%)] Data (t): 0.000 Batch (t): 0.378, 677.567/s, 169.392/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2079 (1.3276) Loss: 1.2079 (1.3276) -2024-09-02,21:18:22 | INFO | Train Epoch: 2 [3097856/3655823 (85%)] Data (t): 0.000 Batch (t): 0.378, 677.997/s, 169.499/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4054 (1.3283) Loss: 1.4054 (1.3283) -2024-09-02,21:19:00 | INFO | Train Epoch: 2 [3123456/3655823 (85%)] Data (t): 0.010 Batch (t): 0.389, 678.416/s, 169.604/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3007 (1.3281) Loss: 1.3007 (1.3281) -2024-09-02,21:19:38 | INFO | Train Epoch: 2 [3149056/3655823 (86%)] Data (t): 0.000 Batch (t): 0.378, 676.870/s, 169.218/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2144 (1.3271) Loss: 1.2144 (1.3271) -2024-09-02,21:20:16 | INFO | Train Epoch: 2 [3174656/3655823 (87%)] Data (t): 0.000 Batch (t): 0.378, 678.335/s, 169.584/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.0321 (1.3248) Loss: 1.0321 (1.3248) -2024-09-02,21:20:54 | INFO | Train Epoch: 2 [3200256/3655823 (88%)] Data (t): 0.000 Batch (t): 0.378, 677.500/s, 169.375/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.1940 (1.3237) Loss: 1.1940 (1.3237) -2024-09-02,21:21:32 | INFO | Train Epoch: 2 [3225856/3655823 (88%)] Data (t): 0.000 Batch (t): 0.378, 666.167/s, 166.542/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3978 (1.3243) Loss: 1.3978 (1.3243) -2024-09-02,21:22:10 | INFO | Train Epoch: 2 [3251456/3655823 (89%)] Data (t): 0.000 Batch (t): 0.380, 676.621/s, 169.155/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.1450 (1.3229) Loss: 1.1450 (1.3229) -2024-09-02,21:22:48 | INFO | Train Epoch: 2 [3277056/3655823 (90%)] Data (t): 0.000 Batch (t): 0.384, 676.788/s, 169.197/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.1781 (1.3218) Loss: 1.1781 (1.3218) -2024-09-02,21:23:26 | INFO | Train Epoch: 2 [3302656/3655823 (90%)] Data (t): 0.000 Batch (t): 0.378, 678.198/s, 169.550/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.5485 (1.3235) Loss: 1.5485 (1.3235) -2024-09-02,21:24:04 | INFO | Train Epoch: 2 [3328256/3655823 (91%)] Data (t): 0.000 Batch (t): 0.378, 677.541/s, 169.385/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3852 (1.3240) Loss: 1.3852 (1.3240) -2024-09-02,21:24:42 | INFO | Train Epoch: 2 [3353856/3655823 (92%)] Data (t): 0.000 Batch (t): 0.378, 677.359/s, 169.340/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2017 (1.3231) Loss: 1.2017 (1.3231) -2024-09-02,21:25:19 | INFO | Train Epoch: 2 [3379456/3655823 (92%)] Data (t): 0.000 Batch (t): 0.378, 677.775/s, 169.444/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.5104 (1.3245) Loss: 1.5104 (1.3245) -2024-09-02,21:25:57 | INFO | Train Epoch: 2 [3405056/3655823 (93%)] Data (t): 0.000 Batch (t): 0.378, 676.563/s, 169.141/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4114 (1.3251) Loss: 1.4114 (1.3251) -2024-09-02,21:26:35 | INFO | Train Epoch: 2 [3430656/3655823 (94%)] Data (t): 0.000 Batch (t): 0.378, 678.026/s, 169.506/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2695 (1.3247) Loss: 1.2695 (1.3247) -2024-09-02,21:27:13 | INFO | Train Epoch: 2 [3456256/3655823 (95%)] Data (t): 0.000 Batch (t): 0.378, 677.805/s, 169.451/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3511 (1.3249) Loss: 1.3511 (1.3249) -2024-09-02,21:27:54 | INFO | Train Epoch: 2 [3481856/3655823 (95%)] Data (t): 0.031 Batch (t): 0.414, 676.920/s, 169.230/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2732 (1.3245) Loss: 1.2732 (1.3245) -2024-09-02,21:28:32 | INFO | Train Epoch: 2 [3507456/3655823 (96%)] Data (t): 0.000 Batch (t): 0.378, 676.889/s, 169.222/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2559 (1.3241) Loss: 1.2559 (1.3241) -2024-09-02,21:29:10 | INFO | Train Epoch: 2 [3533056/3655823 (97%)] Data (t): 0.000 Batch (t): 0.378, 677.979/s, 169.495/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2239 (1.3233) Loss: 1.2239 (1.3233) -2024-09-02,21:29:48 | INFO | Train Epoch: 2 [3558656/3655823 (97%)] Data (t): 0.000 Batch (t): 0.380, 676.143/s, 169.036/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.3523 (1.3235) Loss: 1.3523 (1.3235) -2024-09-02,21:30:26 | INFO | Train Epoch: 2 [3584256/3655823 (98%)] Data (t): 0.000 Batch (t): 0.384, 677.621/s, 169.405/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.4448 (1.3244) Loss: 1.4448 (1.3244) -2024-09-02,21:31:04 | INFO | Train Epoch: 2 [3609856/3655823 (99%)] Data (t): 0.000 Batch (t): 0.378, 677.564/s, 169.391/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.2651 (1.3240) Loss: 1.2651 (1.3240) -2024-09-02,21:31:42 | INFO | Train Epoch: 2 [3635456/3655823 (99%)] Data (t): 0.000 Batch (t): 0.378, 677.751/s, 169.438/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 1.1543 (1.3228) Loss: 1.1543 (1.3228) -2024-09-02,21:32:12 | INFO | Train Epoch: 2 [3655680/3655823 (100%)] Data (t): 0.001 Batch (t): 0.378, 681.823/s, 170.456/s/gpu LR: 0.000000 Logit Scale: 100.000 Contrastive_loss: 0.98438 (1.3204) Loss: 0.98438 (1.3204) diff --git a/data/trained_openclip/no_hard_negative_logs/plotqa_v2/2024_09_02-17_00_26-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/params.txt b/data/trained_openclip/no_hard_negative_logs/plotqa_v2/2024_09_02-17_00_26-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/params.txt deleted file mode 100644 index 76f93176a2087e52d95699cab7c46ae29c2fe52a..0000000000000000000000000000000000000000 --- a/data/trained_openclip/no_hard_negative_logs/plotqa_v2/2024_09_02-17_00_26-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/params.txt +++ /dev/null @@ -1,96 +0,0 @@ -accum_freq: 1 -aug_cfg: {} -batch_size: 64 -beta1: 0.9 -beta2: 0.98 -checkpoint_path: /project/deemreason/junteng/Vision4Math/train_clip/no_hard_negative_logs/plotqa_v2/2024_09_02-17_00_26-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/checkpoints -coca_caption_loss_weight: 2.0 -coca_contrastive_loss_weight: 1.0 -copy_codebase: False -csv_caption_key: caption -csv_img_key: img_path -csv_separator: , -dataset_resampled: False -dataset_type: csv -ddp_static_graph: False -debug: False -delete_previous_checkpoint: False -device: cuda:0 -dist_backend: nccl -dist_url: env:// -distill: False -distill_model: None -distill_pretrained: None -distributed: True -epochs: 3 -epochs_cooldown: None -eps: 1e-06 -force_custom_text: False -force_image_size: None -force_patch_dropout: None -force_quick_gelu: True -gather_with_grad: False -grad_checkpointing: False -grad_clip_norm: None -horovod: False -image_interpolation: None -image_mean: None -image_resize_mode: None -image_std: None -imagenet_v2: None -imagenet_val: None -local_loss: False -local_rank: 0 -lock_image: False -lock_image_freeze_bn_stats: False -lock_image_unlocked_groups: 0 -lock_text: False -lock_text_freeze_layer_norm: False -lock_text_unlocked_layers: 0 -log_every_n_steps: 100 -log_level: 20 -log_local: False -log_path: /project/deemreason/junteng/Vision4Math/train_clip/no_hard_negative_logs/plotqa_v2/2024_09_02-17_00_26-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp/out.log -logs: /project/deemreason/junteng/Vision4Math/train_clip/no_hard_negative_logs/plotqa_v2 -lr: 1e-06 -lr_cooldown_end: 0.0 -lr_cooldown_power: 1.0 -lr_scheduler: cosine -model: ViT-L-14-336 -name: 2024_09_02-17_00_26-model_ViT-L-14-336-lr_1e-06-b_64-j_4-p_amp -no_set_device_rank: False -precision: amp -pretrained: /project/deemreason/junteng/Vision4Math/data/openclip-vit-14-336/openclip_model.pt -pretrained_image: False -rank: 0 -remote_sync: None -remote_sync_frequency: 300 -remote_sync_protocol: s3 -report_to: wandb -resume: None -save_frequency: 1 -save_most_recent: False -seed: 0 -siglip: False -skip_scheduler: False -tensorboard: False -tensorboard_path: -torchcompile: False -torchscript: False -trace: False -train_data: /project/deemreason/junteng/Vision4Math/csv_data/plotqa_train_v2.csv -train_data_upsampling_factors: None -train_num_samples: None -use_bn_sync: False -use_bnb_linear: None -val_data: None -val_frequency: 1 -val_num_samples: None -wandb: True -wandb_notes: -wandb_project_name: open-clip--no-hard-sum -warmup: 0 -wd: 0.1 -workers: 4 -world_size: 4 -zeroshot_frequency: 2