Update README.md

README.md

@@ -1,58 +1,58 @@
----
-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}
-}
+---
+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 2.9B parameters, but **only 1.8B are activated!** (average 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}
+}
 ```