See axolotl config

axolotl version: 0.12.1

base_model: openai/gpt-oss-20b
use_kernels: true
model_quantization_config: Mxfp4Config
model_quantization_config_kwargs:
  dequantize: true

plugins:
  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin

experimental_skip_move_to_device: true  # prevent OOM by not putting model to GPU before sharding

datasets:
  - path: /workspace/swe-tests/scripts/1_low_stakes_control/sft/aquarat_sft_gt_train_stylized.jsonl
    type: chat_template
    ds_type: json
    field_thinking: thinking
    template_thinking_key: thinking

    # step 2
    field_messages: messages
    message_property_mappings:
      role: role
      content: content

    # step 3
    roles_to_train: ["assistant"]
    train_on_eos: "turn"
 
test_datasets:
  - path: /workspace/swe-tests/scripts/1_low_stakes_control/sft/aquarat_sft_gt_val_stylized.jsonl
    type: chat_template
    ds_type: json
    field_thinking: thinking
    template_thinking_key: thinking

    # step 2
    field_messages: messages
    message_property_mappings:
      role: role
      content: content
    
    split: train

output_dir: ./outputs/out/gpt-oss-20b-aquarat-ground-truth

sequence_len: 4096
#sample_packing: true

adapter: lora
lora_r: 32
lora_alpha: 32
lora_dropout: 0.0  # dropout not supported when using LoRA over expert parameters
lora_target_linear: true

# TODO: not supported for now, see peft#2710
#lora_target_parameters:  # target the experts in the last two layers
#  - "22._checkpoint_wrapped_module.mlp.experts.gate_up_proj"
#  - "22._checkpoint_wrapped_module.mlp.experts.down_proj"
#  - "23._checkpoint_wrapped_module.mlp.experts.gate_up_proj"
#  - "23._checkpoint_wrapped_module.mlp.experts.down_proj"

wandb_project: low-stakes-control-sft
wandb_entity: mats-low-stakes
wandb_name: gpt-oss-20b-aquarat-ground-truth
wandb_log_model: checkpoint
hub_model_id: EmilRyd/gpt-oss-20b-aquarat-ground-truth

gradient_accumulation_steps: 8
micro_batch_size: 4
num_epochs: 3

optimizer: adamw_torch_8bit
lr_scheduler: constant_with_warmup
learning_rate: 1e-4

bf16: true
tf32: true

flash_attention: true
attn_implementation: kernels-community/vllm-flash-attn3

gradient_checkpointing: true
activation_offloading: true

logging_steps: 1
save_steps: 2
save_only_model: true
warmup_ratio: 0.1
eval_steps: 5

special_tokens:
eot_tokens:
  - "<|end|>"

gpt-oss-20b-aquarat-ground-truth

This model is a fine-tuned version of openai/gpt-oss-20b on an unknown dataset.

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0001
• train_batch_size: 4
• eval_batch_size: 4
• seed: 42
• distributed_type: multi-GPU
• num_devices: 8
• gradient_accumulation_steps: 8
• total_train_batch_size: 256
• total_eval_batch_size: 32
• optimizer: Use adamw_torch_8bit with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: constant_with_warmup
• lr_scheduler_warmup_steps: 9
• training_steps: 93

Framework versions

• PEFT 0.17.0
• Transformers 4.55.0
• Pytorch 2.6.0+cu126
• Datasets 4.0.0
• Tokenizers 0.21.4