zagreus-test-202000-sft-8

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

axolotl version: 0.9.2

# ============= SFT PRODUCTION (4M ShareGPT) =============
base_model: giux78/zagreus-test-202000
strict: false
output_dir: ./ale_outputs/opendata-sft-prod
seed: 42

# ---- Dataset ----
datasets:
  - path: /leonardo_work/EUHPC_A04_045/training/opendata-1000000
    type: chat_template
    field_messages: conversation
    roles_to_train: ["assistant"]   # loss solo sui turni assistant
    train_on_eos: turn              # predici <|eot_id|> a fine risposta assistant

# (opzionale ma consigliato: cache pretokenizzata tra run)
dataset_prepared_path: ./ale_outputs/dataset_cache/opendata-sft

default_system_message: "Sei un assistente utile."

# ---- Chat template (Llama-3.2 style) ----
chat_template: llama3 

# ---- Training ----
sequence_len: 4096
sample_packing: true            # ON per efficienza
eval_sample_packing: true
pad_to_sequence_len: true

optimizer: adamw_torch_fused
lr_scheduler: cosine
learning_rate: 1.5e-5
warmup_ratio: 0.03              # ~3% dei passi totali
weight_decay: 0.01
max_grad_norm: 1.0

# 32 GPU totali -> eff. batch = 1 * 8 * 32 = 256
micro_batch_size: 1
gradient_accumulation_steps: 8

num_epochs: 1.0                 # 1 epoca completa su 4M conv
# (alternativa: usa max_steps se vuoi fermarti prima)

# ---- Precisione & memoria ----
bf16: auto
flash_attention: true
gradient_checkpointing: true

# ---- Log/Eval/Save ----
logging_steps: 20
eval_strategy: steps
eval_steps: 150                 # ~7-8 eval/epoca
save_strategy: steps
save_steps: 300                 # ~3 checkpoint/epoca
save_total_limit: 4
val_set_size: 10000  
# (opzionale) val_set_size: 10000   # se vuoi split automatico dal dataset

# ---- FSDP multi-nodo ----
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

# ---- Token speciali (coerenti col tokenizer del base_model) ----
special_tokens:
  pad_token: <|end_of_text|>
  eos_token: <|end_of_text|>

</details><br>

ale_outputs/opendata-sft-prod

This model is a fine-tuned version of giux78/zagreus-test-202000 on the None dataset. It achieves the following results on the evaluation set:

• Loss: 3.7225

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: 1.5e-05
• 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: cosine
• lr_scheduler_warmup_steps: 14
• num_epochs: 1.0

Training results

Framework versions

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