74 lines
3.1 KiB
Markdown
74 lines
3.1 KiB
Markdown
# Finetune Devstral with Axolotl
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Devstral Small is a 24B parameter opensource model from MistralAI found on HuggingFace [Devstral-Small-2505](https://huggingface.co/mistralai/Devstral-Small-2505) and [Devstral-Small-2507](https://huggingface.co/mistralai/Devstral-Small-2507). `Devstral-Small-2507` is the latest version of the model and has [function calling](https://mistralai.github.io/mistral-common/usage/tools/) support.
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This guide shows how to fine-tune it with Axolotl with multi-turn conversations with proper masking.
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The model was fine-tuned ontop of [Mistral-Small-3.1](https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Base-2503) without the vision layer and has a context of up to 128k tokens.
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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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pip3 install packaging==26.0 setuptools==75.8.0 wheel ninja
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pip3 install --no-build-isolation 'axolotl[flash-attn]>=0.12.0'
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```
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2. Install [Cut Cross Entropy](https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy) to reduce training VRAM usage
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```bash
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python scripts/cutcrossentropy_install.py | sh
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```
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3. Run the finetuning example:
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```bash
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axolotl train examples/devstral/devstral-small-qlora.yml
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```
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This config uses about 21GB VRAM.
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Let us know how it goes. Happy finetuning! 🚀
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### TIPS
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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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- Learn how to use function calling with Axolotl at [docs](https://docs.axolotl.ai/docs/dataset-formats/conversation.html#using-tool-use).
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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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- [Cut Cross Entropy](https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy)
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- [Liger Kernel](https://docs.axolotl.ai/docs/custom_integrations.html#liger-kernels)
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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 Devstral Blog](https://mistral.ai/news/devstral)
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- [MistralAI Devstral 1.1 Blog](https://mistral.ai/news/devstral-2507)
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- [Axolotl Docs](https://docs.axolotl.ai)
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- [Axolotl GitHub](https://github.com/axolotl-ai-cloud/axolotl)
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- [Axolotl Website](https://axolotl.ai)
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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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