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--- |
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license: apache-2.0 |
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pipeline_tag: text-generation |
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library_name: transformers |
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--- |
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<h2 align="center"> <a href="https://arxiv.org/abs/2405.14297">Dynamic Mixture of Experts: An Auto-Tuning Approach for Efficient Transformer Models</a></h2> |
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<h5 align="center"> If our project helps you, please give us a star ⭐ on <a href="https://github.com/LINs-lab/DynMoE">GitHub</a> and cite our paper!</h5> |
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<h5 align="center"> |
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## 📰 News |
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- **[2025.01.23]**: 🎉 Our paper is accepted to ICLR 2025! |
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- **[2024.05.25]** 🔥 Our **checkpoints** are available now! |
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- **[2024.05.23]** 🔥 Our [paper](https://arxiv.org/abs/2405.14297) is released! |
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## 😎 What's Interesting? |
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**Dynamic Mixture of Experts (DynMoE)** incorporates (1) a novel gating method that enables each token to automatically determine the number of experts to activate. (2) An adaptive process automatically adjusts the number of experts during training. |
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### Top-Any Gating |
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<video controls src="https://i.imgur.com/bLgNaoH.mp4" title="Top-Any Gating"></video> |
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### Adaptive Training Process |
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## 💡 Model Details |
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- 🤔 DynMoE-Phi-2 is a MoE model with **dynamic top-k gating**, finetuned on [LanguageBind/MoE-LLaVA-Phi2-Stage2](https://huggingface.co/LanguageBind/MoE-LLaVA-Phi2-Stage2). |
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- 🚀 Our DynMoE-Phi-2-2.7B has totally 5.3B parameters, but **only 3.4B are activated!** (average top-k = 1.68) |
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- ⌛ With the DynMoE tuning stage, we can complete training on 8 A100 GPUs **within 2 days.** |
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## 👍 Acknowledgement |
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We are grateful for the following awesome projects: |
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- [tutel](https://github.com/microsoft/tutel) |
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- [DeepSpeed](https://github.com/microsoft/DeepSpeed) |
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- [GMoE](https://github.com/Luodian/Generalizable-Mixture-of-Experts) |
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- [EMoE](https://github.com/qiuzh20/EMoE) |
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- [MoE-LLaVA](https://github.com/PKU-YuanGroup/MoE-LLaVA) |
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- [GLUE-X](https://github.com/YangLinyi/GLUE-X) |
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## 🔒 License |
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This project is released under the Apache-2.0 license as found in the [LICENSE](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/mit.md) file. |
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## ✏️ Citation |
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```tex |
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@misc{guo2024dynamic, |
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title={Dynamic Mixture of Experts: An Auto-Tuning Approach for Efficient Transformer Models}, |
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author={Yongxin Guo and Zhenglin Cheng and Xiaoying Tang and Tao Lin}, |
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year={2024}, |
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eprint={2405.14297}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.LG} |
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} |
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``` |
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## Star History |
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[](https://star-history.com/#LINs-lab/DynMoE&Date) |
