axolotl/examples/magistral/think

Magistral Small Thinking Fine-tuning

This guide covers fine-tuning 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)

Getting Started

Run the thinking model fine-tuning:

axolotl train 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 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:

{
    "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:

{
    "type": "thinking",
    "thinking": "Internal reasoning here...",
    "closed": true  // Default: true, controls adding the closing [/THINK] tag
}