Update README.md

README.md

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-license: mit
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+---
+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-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).
+- 🚀 Our DynMoE-Phi-2-2.7B has totally 5.3B parameters, but **only 3.4B are activated!** (average top-k = 1.68)
+- ⌛ With the DynMoE tuning stage, we can complete training on 8 A100 GPUs **within 2 days.**
+
+## 👍 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}
+}
+```