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Finetune Xiaomi's MiMo with Axolotl
MiMo 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.
This guide shows how to fine-tune it with Axolotl with multi-turn conversations and proper masking.
Getting started
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Install Axolotl following the installation guide.
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Run the finetuning example:
axolotl train examples/mimo/mimo-7b-qlora.yaml
This config uses about 17.2 GiB VRAM. Let us know how it goes. Happy finetuning! 🚀
Tips
- You can run a full finetuning by removing the
adapter: qloraandload_in_4bit: truefrom the config. - Read more on how to load your own dataset at docs.
- The dataset format follows the OpenAI Messages format as seen here.
Optimization Guides
Please check the Optimizations doc.
Limitations
Cut Cross Entropy (CCE): Currently not supported. We plan to include CCE support for MiMo in the near future.