# SIA Training Tool

This tool provides command-line utilities for fine-tuning SIA's language models.

## Supported Models

- DeepSeek R1 models (including distilled versions)
- Mistral models

## Commands

### train_deepseek

Fine-tune DeepSeek models using Unsloth optimization.

```bash
train_deepseek --base-model deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B --output-dir /root/models/DeepSeek-R1-Distill-Qwen-1.5B
```

Options:
- `--config`: Path to training configuration file (default: /root/sia/training/config.yaml)
- `--base-model`: HuggingFace model ID for the base model (required)
- `--output-dir`: Directory to save model (required)
- `--api-key`: HuggingFace API key (optional, will use SIA_HF_API_KEY)

### train_mistral

Fine-tune Mistral models using Mistral's API.

```bash
train_mistral --model mistral-large-latest
```

Options:
- `--config`: Path to training configuration file (default: /root/sia/training/config.yaml)
- `--model`: Base model name (default: mistral-large-latest)
- `--api-key`: Mistral API key (optional, will use SIA_MISTRAL_API_KEY)

## Configuration Format

The training configuration file (YAML) should include:

```yaml
model:
  system_prompt_path: "/root/sia/system_prompt.md"
  action_schema: "/root/sia/action_schema.xsd"
params:
  learning_rate: 1e-5
  epochs: 3
data:
  - "/root/sia/training/data_dir1/"
  - "/root/sia/training/data_dir2/"
```

## Data Format

Training data should be XML files in the following format:

```xml
<iteration system_prompt_hash="..." action_schema_hash="...">
  <context>
    <!-- XML context -->
  </context>
  <response>
    <!-- Model response -->
  </response>
</iteration>
```