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* feat: move to uv first * fix: update doc to uv first * fix: merge dev/tests into uv pyproject * fix: update docker docs to match current config * fix: migrate examples to readme * fix: add llmcompressor to conflict * feat: rec uv sync with lockfile for dev/ci * fix: update docker docs to clarify how to use uv images * chore: docs * fix: use system python, no venv * fix: set backend cpu * fix: only set for installing pytorch step * fix: remove unsloth kernel and installs * fix: remove U in tests * fix: set backend in deps too * chore: test * chore: comments * fix: attempt to lock torch * fix: workaround torch cuda and not upgraded * fix: forgot to push * fix: missed source * fix: nightly upstream loralinear config * fix: nightly phi3 long rope not work * fix: forgot commit * fix: test phi3 template change * fix: no more requirements * fix: carry over changes from new requirements to pyproject * chore: remove lockfile per discussion * fix: set match-runtime * fix: remove unneeded hf hub buildtime * fix: duplicate cache delete on nightly * fix: torchvision being overridden * fix: migrate to uv images * fix: leftover from merge * fix: simplify base readme * fix: update assertion message to be clearer * chore: docs * fix: change fallback for cicd script * fix: match against main exactly * fix: peft 0.19.1 change * fix: e2e test * fix: ci * fix: e2e test
83 lines
2.9 KiB
Markdown
83 lines
2.9 KiB
Markdown
# Finetune Voxtral with Axolotl
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Voxtral is a [3B](https://huggingface.co/mistralai/Voxtral-Mini-3B-2507)/[24B](https://huggingface.co/mistralai/Voxtral-Small-24B-2507) parameter opensource model from MistralAI found on HuggingFace. This guide shows how to fine-tune it with Axolotl.
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Thanks to the team at MistralAI for giving us early access to prepare for this release.
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## Getting started
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1. Install Axolotl following the [installation guide](https://docs.axolotl.ai/docs/installation.html).
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Here is an example of how to install from pip:
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```bash
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# Ensure you have Pytorch installed (Pytorch 2.6.0 min)
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uv pip install --no-build-isolation 'axolotl[flash-attn]>=0.12.0'
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```
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2. Please install the below.
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```bash
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# audio
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uv pip install librosa==0.11.0
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uv pip install 'mistral_common[audio]==1.8.3'
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# Install CCE https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy
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python scripts/cutcrossentropy_install.py | sh
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```
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3. Download sample dataset files
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```bash
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# for text + audio only
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wget https://huggingface.co/datasets/Nanobit/text-audio-2k-test/resolve/main/En-us-African_elephant.oga
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```
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4. Run the finetuning example:
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```bash
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# text only
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axolotl train examples/voxtral/voxtral-mini-qlora.yml
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# text + audio
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axolotl train examples/voxtral/voxtral-mini-audio-qlora.yml
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```
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These configs use about 4.8 GB VRAM.
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Let us know how it goes. Happy finetuning! 🚀
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### TIPS
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- For inference, the official MistralAI team recommends `temperature: 0.2` and `top_p: 0.95` for audio understanding and `temperature: 0.0` for transcription.
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- You can run a full finetuning by removing the `adapter: qlora` and `load_in_4bit: true` from the config.
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- Read more on how to load your own dataset at [docs](https://docs.axolotl.ai/docs/dataset_loading.html).
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- The text dataset format follows the OpenAI Messages format as seen [here](https://docs.axolotl.ai/docs/dataset-formats/conversation.html#chat_template).
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- The multimodal dataset format follows the OpenAI multi-content Messages format as seen [here](https://docs.axolotl.ai/docs/multimodal.html#dataset-format).
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## Optimization Guides
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- [Multi-GPU Training](https://docs.axolotl.ai/docs/multi-gpu.html)
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- [Multi-Node Training](https://docs.axolotl.ai/docs/multi-node.html)
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- [LoRA Optimizations](https://docs.axolotl.ai/docs/lora_optims.html)
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## Limitations
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We only support the `mistral-common` tokenizer for Supervised Fine-tuning at the moment and for `type: chat_template` only.
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In addition, we do not support overriding tokens yet.
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## Related Resources
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- [MistralAI Magistral Blog](https://mistral.ai/news/magistral/)
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- [Axolotl Docs](https://docs.axolotl.ai)
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- [Axolotl Website](https://axolotl.ai)
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- [Axolotl GitHub](https://github.com/axolotl-ai-cloud/axolotl)
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- [Axolotl Discord](https://discord.gg/7m9sfhzaf3)
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## Future Work
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- Add parity to Preference Tuning, RL, etc.
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- Add parity to other tokenizer configs like overriding tokens.
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