---
library_name: transformers
base_model: giux78/zagreus-test-202000
tags:
- generated_from_trainer
model-index:
- name: ale_outputs/sft-prod-l3p32
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

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

axolotl version: `0.9.2`
```yaml
# ============= SFT PRODUCTION (4M ShareGPT) =============
base_model: giux78/zagreus-test-202000
strict: false
output_dir: ./ale_outputs/sft-prod-l3p32
seed: 42

# ---- Dataset ----
datasets:
  - path: /leonardo_work/EUHPC_A04_045/.data
    type: chat_template
    field_messages: conversations
    message_property_mappings:
      role: from
      content: value
    roles:
      user: ["human", "user"]
      assistant: ["gpt", "assistant"]
      system: ["system"]
      tool: ["tool"]
    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/sharegpt_4m_llama32_4096

default_system_message: "Sei un assistente utile."

# ---- Chat template (Llama-3.2 style) ----
chat_template: jinja
chat_template_jinja: |
  {{- bos_token -}}
  {%- set has_system = messages and messages[0]['role'] == 'system' -%}
  {%- if has_system -%}
    {{- '<|start_header_id|>system<|end_header_id|>\n\n' + (messages[0]['content']|default('')) + '<|eot_id|>' -}}
    {%- set loop_messages = messages[1:] -%}
  {%- else -%}
    {%- set loop_messages = messages -%}
  {%- endif -%}
  {%- for m in loop_messages -%}
    {%- set role = m['role']|default('') -%}
    {%- set content = m['content']|default('', true) -%}
    {%- if content is string -%}
      {%- set text = content -%}
    {%- else -%}
      {%- set text = (content | map(attribute='text') | join('')) -%}
    {%- endif -%}
    {%- if text|trim|length > 0 -%}
      {%- if role == 'user' -%}
        {{- '<|start_header_id|>user<|end_header_id|>\n\n' + text + '<|eot_id|>' -}}
      {%- elif role == 'assistant' -%}
        {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' + text + '<|eot_id|>' -}}
      {%- elif role == 'system' -%}
        {{- '<|start_header_id|>system<|end_header_id|>\n\n' + text + '<|eot_id|>' -}}
      {%- endif -%}
    {%- endif -%}
  {%- endfor -%}
  {%- if add_generation_prompt and (loop_messages|length == 0 or (loop_messages|last)['role'] != 'assistant') -%}
    {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
  {%- else -%}
    {{- eos_token -}}
  {%- endif -%}

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

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
evaluation_strategy: steps
eval_steps: 2000                 # ~7-8 eval/epoca
save_strategy: steps
save_steps: 5000                 # ~3 checkpoint/epoca
save_total_limit: 4
# (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:
  bos_token: <|begin_of_text|>
  pad_token: <|pad|>
  eos_token: <|end_of_text|>
  unk_token: <|unk|>

```

</details><br>

# ale_outputs/sft-prod-l3p32

This model is a fine-tuned version of [giux78/zagreus-test-202000](https://huggingface.co/giux78/zagreus-test-202000) on the None 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: 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: 102
- 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