# Devstral

Devstral Small is a 24B parameter opensource model from MistralAI found on HuggingFace Devstral-Small-2505 and Devstral-Small-2507. `Devstral-Small-2507` is the latest version of the model and has function calling support.

This guide shows how to fine-tune it with Axolotl with multi-turn conversations with proper masking.

The model was fine-tuned ontop of Mistral-Small-3.1 without the vision layer and has a context of up to 128k tokens.

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.

   Here is an example of how to install from pip:

    # Ensure you have Pytorch installed (Pytorch 2.6.0 min)
    pip3 install packaging==26.0 setuptools==75.8.0 wheel ninja
    pip3 install --no-build-isolation 'axolotl[flash-attn]>=0.12.0'

2. Install Cut Cross Entropy to reduce training VRAM usage

    python scripts/cutcrossentropy_install.py | sh

3. Run the finetuning example:

    axolotl train examples/devstral/devstral-small-qlora.yml

This config uses about 21GB VRAM.

Let us know how it goes. Happy finetuning! 🚀

### TIPS

- 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 dataset format follows the OpenAI Messages format as seen here.
- Learn how to use function calling with Axolotl at docs.

## Optimization Guides

- Multi-GPU Training
- Multi-Node Training
- LoRA Optimizations
- Cut Cross Entropy
- Liger Kernel

## 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 Devstral Blog
- MistralAI Devstral 1.1 Blog
- Axolotl Docs
- Axolotl GitHub
- Axolotl Website
- Axolotl Discord

## Future Work

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