90e5598930
* fix: lock version in gemma3n docs * feat: add sample configs and docs * chore: move mistraltokenizer into mistral folder * feat: update instructions * feat: add dynamic load voxtral * fix: remove incorrect vision config, add audio * fix: support voxtral processing strategy and address none in data * feat: patch mistraltokenizer subclass upstream and add missing * feat: update cce commit to include voxtral * fix: remove old comment * fix: gemma3 patch not needed anymore * fix: voxtral modeling code * fix: remove incorrect ds path * fix: adjust apply chat template parsing * feat: enable voxtral patch * fix: patch * feat: update example datasets * fix: target layer * feat: update gemma3n docs * feat: update voxtral docs * feat: revert assistant parsing to rely on new upstream changes * chore: skip test till next PR fix * fix: override upstream decode due to missing handling * feat: update readme * fix: update * feat: add magistral small think support * feat: update mistral-common dep * fix: lint * fix: remove optional dep * chore: typing * chore: simply import * feat(doc): update differences for 2507 * fix: coderrabbit comments * feat: update clarify docs on new transformers
91 lines
3.8 KiB
Markdown
91 lines
3.8 KiB
Markdown
# Finetune Magistral Small with Axolotl
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Magistral Small is a 24B parameter opensource model from MistralAI found on HuggingFace at [2506](https://huggingface.co/mistralai/Magistral-Small-2506) and [2507](https://huggingface.co/mistralai/Magistral-Small-2507) (see [Thinking](#thinking)). This guide shows how to fine-tune it with Axolotl with multi-turn conversations and proper masking.
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MistralAI has also released a proprietary medium-sized version called Magistral Medium.
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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). You need to install from main as Magistral is only on nightly or use our latest [Docker images](https://docs.axolotl.ai/docs/docker.html).
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Here is an example of how to install from main for pip:
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```bash
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# Ensure you have Pytorch installed (Pytorch 2.6.0 min)
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git clone https://github.com/axolotl-ai-cloud/axolotl.git
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cd axolotl
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pip3 install packaging==23.2 setuptools==75.8.0 wheel ninja
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pip3 install --no-build-isolation -e '.[flash-attn]'
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```
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2. Run the finetuning example:
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```bash
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axolotl train examples/magistral/magistral-small-qlora.yaml
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```
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This config uses about 24GB VRAM.
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Let us know how it goes. Happy finetuning! 🚀
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### Thinking
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MistralAI has released their [2507](https://huggingface.co/mistralai/Magistral-Small-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.
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Example format:
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```json
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{
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"messages": [
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{"role": "system", "content": [{ "type": "text", "text": "{SYSTEM_PROMPT}"}]},
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{"role": "user", "content": [{ "type": "text", "text": "..."}]},
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{"role": "assistant", "content": [{ "type": "thinking", "thinking": "..."}, { "type": "text", "text": "..." }]},
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],
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}
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```
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Example config: `./magistral-small-think-qlora.yaml`.
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The `thinking` section also supports an optional arg `closed: bool` (`True` default) which controls adding the closing `[/THINK]` tag.
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Limitations:
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- You cannot mix `content: str` with `content: list[dict]` as the `dataset.load_dataset` may complain about different types for `content` key.
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- This mode does not work with custom `train_detail` and `training` at the moment.
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### TIPS
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- 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`.
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- For inference, the official MistralAI team recommends `top_p: 0.95` and `temperature: 0.7` with `max_tokens: 40960`.
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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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## 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, Multi-modal, etc.
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- Add parity to other tokenizer configs like overriding tokens.
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