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axolotl/examples/magistral/think/README.md
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Co-authored-by: salman <salman.mohammadi@outlook.com>

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Co-authored-by: Wing Lian <wing@axolotl.ai>
Co-authored-by: salman <salman.mohammadi@outlook.com>
2025-10-22 15:23:20 -07:00

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# Magistral Small Thinking Fine-tuning
This guide covers fine-tuning [Magistral Small 2507](https://huggingface.co/mistralai/Magistral-Small-2507) with thinking capabilities using Axolotl. The thinking model enables explicit Chain-of-Thought reasoning with separate thinking and response sections.
## Prerequisites
Before starting, ensure you have:
- Installed Axolotl (see [main README](../README.md))
## Getting Started
Run the thinking model fine-tuning:
```bash
axolotl train examples/magistral/think/magistral-small-think-qlora.yaml
```
This config uses about 19.1 GiB VRAM.
### Tips
- Dataset uses multi-content format with `type: thinking` support. See [Dataset Format](#dataset-format) below.
- You cannot mix `content: str` and `content: list[dict]`, otherwise, dataset loading will fail. Keep it consistent.
## Dataset Format
The thinking model requires the multi-content dataset format with support for an extra `role: thinking` within system and assistant messages.
Example format:
```json
{
"messages": [
{
"role": "system",
"content": [
{ "type": "text", "text": "{SYSTEM_PROMPT}"}
]
},
{
"role": "user",
"content": [
{ "type": "text", "text": "Solve this step by step: What is 15% of 240?"}
]
},
{
"role": "assistant",
"content": [
{
"type": "thinking",
"thinking": "I need to calculate 15% of 240. First, I'll convert 15% to decimal: 0.15. Then multiply: 0.15 × 240 = 36."
},
{
"type": "text",
"text": "To find 15% of 240, I'll multiply 240 by 0.15:\n\n240 × 0.15 = 36\n\nTherefore, 15% of 240 is 36."
}
]
}
]
}
```
### Advanced Options
The `thinking` section supports an optional `closed` parameter:
```json
{
"type": "thinking",
"thinking": "Internal reasoning here...",
"closed": true // Default: true, controls adding the closing [/THINK] tag
}
```