zagreus-test-202000-sft-13

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<details><summary>See axolotl config</summary>

axolotl version: 0.9.2

base_model: ./ale_outputs/opendata-sft-debug-reg/checkpoint-1500/  # <— checkpoint finale precedente
strict: false
output_dir: ./ale_outputs/opendata-sft-lastmile
seed: 42


chat_template: llama3
datasets:
  - path: /leonardo_work/EUHPC_A04_045/training/opendata-1000000
    type: chat_template
    field_messages: conversation
    roles_to_train: ["assistant"]
    train_on_eos: turn

dataset_prepared_path: ./ale_outputs/dataset_cache/opendata-sft

# ---- Training (last-mile fine-tuning) ----
max_steps: 800                    # 500–800 step per consolidare
lr_scheduler: constant_with_warmup
learning_rate: 9.0e-6             # LR “vivo” per qualche centinaio di step
warmup_ratio: 0.0
weight_decay: 0.005
max_grad_norm: 1.0

micro_batch_size: 1
gradient_accumulation_steps: 8
bf16: auto
flash_attention: true
gradient_checkpointing: true

eval_strategy: steps
eval_steps: 100
save_strategy: steps
save_steps: 200
save_total_limit: 4
val_set_size: 10000

# ---- Token ----
special_tokens:
  pad_token: <|end_of_text|>
  eos_token: <|eot_id|>          # importantissimo per train_on_eos: turn

# ---- fsdp ---- (se ti serve ancora)
fsdp_config:
  fsdp_sharding_strategy: FULL_SHARD
  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
  fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer
  fsdp_backward_prefetch_policy: BACKWARD_PRE
  fsdp_state_dict_type: FULL_STATE_DICT

</details><br>

ale_outputs/opendata-sft-lastmile

This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:

• Loss: 2.2857

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: 9e-06
• train_batch_size: 1
• eval_batch_size: 1
• seed: 42
• distributed_type: multi-GPU
• num_devices: 32
• gradient_accumulation_steps: 8
• total_train_batch_size: 256
• total_eval_batch_size: 32
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: constant_with_warmup
• training_steps: 800

Training results

Framework versions

• Transformers 4.56.2
• Pytorch 2.5.1+cu121
• Datasets 3.5.1
• Tokenizers 0.22.1