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
70 lines
2.7 KiB
Markdown
70 lines
2.7 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). 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 upto 128k tokens.
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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 Devstral 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+)
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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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# Install the latest mistral-common from source
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pip3 uninstall mistral-common
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pip3 install git+https://github.com/mistralai/mistral-common.git@039465d
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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/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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## 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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- [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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