4f5e8a328a
* feat: add xiaomi's mimo 7b * fix: pin revision * fix: update trinity docs and pin revision * fix: wrong config name * feat: add vram usage * feat: add plano * feat: update plano vram usage * chore: comments
40 lines
1.7 KiB
Markdown
40 lines
1.7 KiB
Markdown
# Finetune Xiaomi's MiMo with Axolotl
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[MiMo](https://huggingface.co/XiaomiMiMo/MiMo-7B-RL) is a family of models trained from scratch for reasoning tasks, incorporating **Multiple-Token Prediction (MTP)** as an additional training objective for enhanced performance and faster inference. Pre-trained on ~25T tokens with a three-stage data mixture strategy and optimized reasoning pattern density.
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This guide shows how to fine-tune it with Axolotl with multi-turn conversations and proper masking.
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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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2. Run the finetuning example:
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```bash
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axolotl train examples/mimo/mimo-7b-qlora.yaml
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```
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This config uses about 17.2 GiB VRAM. 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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Please check the [Optimizations doc](https://docs.axolotl.ai/docs/optimizations.html).
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## Limitations
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**Cut Cross Entropy (CCE)**: Currently not supported. We plan to include CCE support for MiMo in the near future.
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## Related Resources
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- [MiMo Paper](https://arxiv.org/abs/2505.07608)
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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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