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README.md
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---
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library_name: transformers
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tags:
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- generated_from_trainer
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datasets:
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- 2025-01_conversations_truncated.jsonl
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model-index:
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- name: outputs/
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
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<details><summary>See axolotl config</summary>
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axolotl version: `0.6.0`
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```yaml
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base_model: ./meta-llama_Llama-3.2-3B
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# optionally might have model_type or tokenizer_type
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model_type: LlamaForCausalLM
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tokenizer_type: AutoTokenizer
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# Automatically upload checkpoint and final model to HF
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# hub_model_id: username/custom_model_name
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load_in_8bit: false
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load_in_4bit: false
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strict: false
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datasets:
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- path: 2025-01_conversations_truncated.jsonl
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type: chat_template
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chat_template: llama3
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field_messages: conversations
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message_field_role: from
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message_field_content: value
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roles:
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user:
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- human
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assistant:
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- gpt
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system:
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- system
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dataset_prepared_path:
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val_set_size: 0.05
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output_dir: ./outputs/
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dataset_prepared_path: last_run_prepared
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sequence_len: 4096
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eval_sample_packing: false
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sample_packing: true
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pad_to_sequence_len: true
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wandb_project: JVCGPT Light 3b base
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wandb_entity:
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wandb_watch:
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wandb_name:
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wandb_log_model:
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gradient_accumulation_steps: 4
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micro_batch_size: 2
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num_epochs: 4
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optimizer: paged_adamw_8bit
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lr_scheduler: cosine
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learning_rate: 0.000007
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train_on_inputs: true
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group_by_length: false
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bf16: auto
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fp16:
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tf32: false
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gradient_checkpointing: unsloth
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early_stopping_patience:
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resume_from_checkpoint:
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local_rank:
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logging_steps: 1
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xformers_attention:
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flash_attention: true
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s2_attention:
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warmup_steps: 100
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eval_table_size:
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saves_per_epoch: 2
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debug:
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deepspeed:
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weight_decay: 0.0
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fsdp:
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fsdp_config:
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special_tokens:
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pad_token: <|end_of_text|>
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save_safetensors: true
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save_total_limit: 10
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```
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</details><br>
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# outputs/
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This model was trained from scratch on the 2025-01_conversations_truncated.jsonl dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.1520
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 7e-06
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- train_batch_size: 2
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- eval_batch_size: 2
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- seed: 42
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- distributed_type: multi-GPU
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- num_devices: 4
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 32
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- total_eval_batch_size: 8
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- optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: cosine
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- lr_scheduler_warmup_steps: 100
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- num_epochs: 4
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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:------:|:----:|:---------------:|
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| 0.6055 | 1.0006 | 789 | 1.1893 |
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| 0.5619 | 2.0006 | 1578 | 1.1576 |
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| 0.4873 | 3.0006 | 2367 | 1.1522 |
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| 1.2133 | 3.9917 | 3148 | 1.1520 |
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### Framework versions
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- Transformers 4.47.1
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- Pytorch 2.5.1+cu124
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- Datasets 3.2.0
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- Tokenizers 0.21.0
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model-00001-of-00002.safetensors
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size 4965799096
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model-00002-of-00002.safetensors
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