axolotl/examples/magistral

Finetune Magistral Small with Axolotl

Magistral Small is a 24B parameter opensource model from MistralAI found on HuggingFace at 2506 and 2507 (see Thinking). This guide shows how to fine-tune it with Axolotl with multi-turn conversations and proper masking.

MistralAI has also released a proprietary medium-sized version called Magistral Medium.

Thanks to the team at MistralAI for giving us early access to prepare for this release.

Getting started

1. Install Axolotl following the installation guide. You need to install from main as Magistral is only on nightly or use our latest Docker images.

   Here is an example of how to install from main for pip:

# Ensure you have Pytorch installed (Pytorch 2.6.0 min)
git clone https://github.com/axolotl-ai-cloud/axolotl.git
cd axolotl

pip3 install packaging==23.2 setuptools==75.8.0 wheel ninja
pip3 install --no-build-isolation -e '.[flash-attn]'

2. Run the finetuning example:

axolotl train examples/magistral/magistral-small-qlora.yaml

This config uses about 24GB VRAM.

Let us know how it goes. Happy finetuning! 🚀

Thinking

MistralAI has released their 2507 model with thinking capabilities. The 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": "..."}]},
        {"role": "assistant", "content": [{ "type": "thinking", "thinking": "..."}, { "type": "text", "text": "..." }]},
    ],
}

Example config: ./magistral-small-think-qlora.yaml.

The thinking section also supports an optional arg closed: bool (True default) which controls adding the closing [/THINK] tag.

Limitations:

• You cannot mix content: str with content: list[dict] as the dataset.load_dataset may complain about different types for content key.
• This mode does not work with custom train_detail and training at the moment.

TIPS

• We recommend adding the same/similar SystemPrompt that the model is tuned for. You can find this within the repo's files titled SYSTEM_PROMPT.txt.
• For inference, the official MistralAI team recommends top_p: 0.95 and temperature: 0.7 with max_tokens: 40960.
• You can run a full finetuning by removing the adapter: qlora and load_in_4bit: true from the config.
• Read more on how to load your own dataset at docs.
• The text dataset format follows the OpenAI Messages format as seen here.

Optimization Guides

• Multi-GPU Training
• Multi-Node Training
• LoRA Optimizations

Limitations

We only support the mistral-common tokenizer for Supervised Fine-tuning at the moment and for type: chat_template only.

In addition, we do not support overriding tokens yet.

Related Resources

• MistralAI Magistral Blog
• Axolotl Docs
• Axolotl Website
• Axolotl GitHub
• Axolotl Discord

Future Work

• Add parity to Preference Tuning, RL, Multi-modal, etc.
• Add parity to other tokenizer configs like overriding tokens.