Finetune Voxtral with Axolotl
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
-
Install Axolotl following the installation guide. You need to install from main as Voxtral 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]'
- 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
- 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: 0.2andtop_p: 0.95for audio understanding andtemperature: 0.0for transcription. - You can run a full finetuning by removing the
adapter: qloraandload_in_4bit: truefrom 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
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
Future Work
- Add parity to Preference Tuning, RL, etc.
- Add parity to other tokenizer configs like overriding tokens.