README.md

--- 
license: mit 
--- 
 
<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> 
<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!</h2> 
<h5 align="center"> 
 
## 📰 News 
 
- **[2024.05.25]** 🔥 Our **checkpoints** are available now! 
- **[2024.05.23]** 🔥 Our [paper](https://arxiv.org/abs/2405.14297) is released! 
 
## 😎 What's Interesting? 
 
**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. 
 
### Top-Any Gating 
 
<video controls src="https://i.imgur.com/bLgNaoH.mp4" title="Top-Any Gating"></video> 
 
### Adaptive Training Process 
 
![](https://cdn.jsdelivr.net/gh/QAQdev/Pics@master/uPic/adaptive.png) 
 
## 💡 Model Details 
 
- 🤔 DynMoE-StableLM is a MoE model with **dynamic top-k gating**, finetuned on [LanguageBind/MoE-LLaVA-StableLM-Stage2](https://huggingface.co/LanguageBind/MoE-LLaVA-StableLM-Stage2). 
- 🚀 Our DynMoE-StableLM-1.6B has totally 3.2B parameters, but **only 1.8B are activated!** (averge top-k = 1.25) 
- ⌛ With the DynMoE tuning stage, we can complete training on 8 A100 GPUs **within 40 hours.** 
 
## 👍 Acknowledgement 
 
We are grateful for the following awesome projects: 
 
- [tutel](https://github.com/microsoft/tutel) 
- [DeepSpeed](https://github.com/microsoft/DeepSpeed) 
- [GMoE](https://github.com/Luodian/Generalizable-Mixture-of-Experts) 
- [EMoE](https://github.com/qiuzh20/EMoE) 
- [MoE-LLaVA](https://github.com/PKU-YuanGroup/MoE-LLaVA) 
- [GLUE-X](https://github.com/YangLinyi/GLUE-X) 
 
## 🔒 License 
 
This project is released under the MIT license as found in the [LICENSE](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/mit.md) file. 
 
## ✏️ Citation 
 
```tex 
@misc{guo2024dynamic, 
 title={Dynamic Mixture of Experts: An Auto-Tuning Approach for Efficient Transformer Models}, 
 author={Yongxin Guo and Zhenglin Cheng and Xiaoying Tang and Tao Lin}, 
 year={2024}, 
 eprint={2405.14297}, 
 archivePrefix={arXiv}, 
 primaryClass={cs.LG} 
} 
```