# Voxtral

Voxtral is a 3B/24B parameter opensource model from MistralAI found on HuggingFace. This guide shows how to fine-tune it with Axolotl.

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. Please install the below.

    # audio
    pip3 install librosa==0.11.0
    pip3 install 'mistral_common[audio]==1.8.3'

    # Install CCE https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy
    python scripts/cutcrossentropy_install.py | sh

3. Download sample dataset files

    # for text + audio only
    wget https://huggingface.co/datasets/Nanobit/text-audio-2k-test/resolve/main/En-us-African_elephant.oga

4. Run the finetuning example:

    # text only
    axolotl train examples/voxtral/voxtral-mini-qlora.yml

    # text + audio
    axolotl train examples/voxtral/voxtral-mini-audio-qlora.yml

These configs use about 4.8 GB VRAM.

Let us know how it goes. Happy finetuning! 🚀

### TIPS

- For inference, the official MistralAI team recommends `temperature: 0.2` and `top_p: 0.95` for audio understanding and `temperature: 0.0` for transcription.
- 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.
- The multimodal dataset format follows the OpenAI multi-content 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, etc.
- Add parity to other tokenizer configs like overriding tokens.