8c6a6ea6eb
* fix: do not add training and training_detail block by default * fixed: magistral docs * fix: address pad adding new fields and use built-in from_openai * feat: try enable multiprocessing * fix: check for keys before deleting attn_mask * feat: add mistral pad test * feat: add tool calling test * feat: add devstral tokenizer tests * fix: comma format * chore: remove unused support_preprocessing as tokenizer is pickable now * chore: update magistral doc * feat: add devstral readme and example * chore: refactor error handling
66 lines
2.5 KiB
Markdown
66 lines
2.5 KiB
Markdown
# Finetune Magistral Small with Axolotl
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Magistral Small is a 24B parameter opensource model from MistralAI found on [HuggingFace](https://huggingface.co/mistralai/Magistral-Small-2506). This guide shows how to fine-tune it with Axolotl with multi-turn conversations with 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 recommended)
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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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### TIPS
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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 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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