Update README.md

README.md

@@ -25,9 +25,9 @@ tags:
   <a href="https://discord.gg/P2yYH95N" target="_blank" style="margin: 2px;">
     <img alt="Discord" src="https://img.shields.io/badge/Discord-Small%20Doges-7289da?logo=discord&logoColor=white&color=7289da" style="display: inline-block; vertical-align: middle;"/>
   </a>
-  <a href="https://arxiv.org/abs/2412.11834" target="_blank" style="margin: 2px;">
+  <!-- <a href="https://arxiv.org/abs/2412.11834" target="_blank" style="margin: 2px;">
     <img alt="arXiv" src="https://img.shields.io/static/v1?label=arXiv&message=2412.11834&color=B31B1B&logo=arXiv" style="display: inline-block; vertical-align: middle;"/>
-  </a>
+  </a> -->
   <a href="https://github.com/SmallDoges/small-doge" target="_blank" style="margin: 2px;">
     <img alt="GitHub" src="https://img.shields.io/badge/GitHub-SmallDoge-181717?logo=github" style="display: inline-block; vertical-align: middle;"/>
   </a>
@@ -36,7 +36,7 @@ tags:
   </a>
 </div>
 
-Doge uses Dynamic Mask Attention as sequence transformation and can use Multi-Layer Perceptron or Cross Domain Mixture of Experts as state transformation. Dynamic Mask Attention allows the Transformer to use self-attention during training and state space during inference, and Cross Domain Mixture of Experts can directly inherit the weights of Multi-Layer Perceptron for further training. This model is trained by [SmallDoge](https://huggingface.co/SmallDoge) community, for detailed algorithm and model architecture, please refer to [Wonderful Matrices](https://arxiv.org/abs/2412.11834), all training details and code are publicly available on the [small-doge](https://github.com/SmallDoges/small-doge) repository.
+Doge uses Dynamic Mask Attention as sequence transformation and can use Multi-Layer Perceptron or Cross Domain Mixture of Experts as state transformation. Dynamic Mask Attention allows the Transformer to use self-attention during training and state space during inference, and Cross Domain Mixture of Experts can directly inherit the weights of Multi-Layer Perceptron for further training. This model is trained by [SmallDoge](https://huggingface.co/SmallDoge) community, for detailed algorithm and model architecture, paper coming soon, all training details and code are available in the [small-doge](https://github.com/SmallDoges/small-doge) repository.
 
 
 ## Uses
@@ -83,13 +83,13 @@ outputs = model.generate(
 
 We build the Doge-Instruct-SFT by SFT on [SmolTalk](https://huggingface.co/datasets/HuggingFaceTB/smoltalk).
 
-> TODO: The larger model is under training and will be uploaded soon.
-
 **SFT**:
 | Model | Training Data | Epochs | Content Length | LR | Batch Size | Precision |
 |---|---|---|---|---|---|---|
-| [Doge-20M-Instruct-SFT](https://huggingface.co/SmallDoge/Doge-20M-Instruct-SFT) | [HuggingFaceTB/smoltalk](https://huggingface.co/datasets/HuggingFaceTB/smoltalk) | 2 | 2048 | 8e-4 | 0.25M | bfloat16 |
-| [Doge-60M-Instruct-SFT](https://huggingface.co/SmallDoge/Doge-60M-Instruct-SFT) | [HuggingFaceTB/smoltalk](https://huggingface.co/datasets/HuggingFaceTB/smoltalk) | 2 | 2048 | 6e-4 | 0.25M | bfloat16 |
+| [Doge-20M-Instruct-SFT](https://huggingface.co/SmallDoge/Doge-20M-Instruct-SFT) | [smoltalk](https://huggingface.co/datasets/HuggingFaceTB/smoltalk) | 2 | 2048 | 8e-4 | 0.25M | bfloat16 |
+| [Doge-60M-Instruct-SFT](https://huggingface.co/SmallDoge/Doge-60M-Instruct-SFT) | [smoltalk](https://huggingface.co/datasets/HuggingFaceTB/smoltalk) | 2 | 2048 | 6e-4 | 0.25M | bfloat16 |
+| [Doge-160M-Instruct-SFT](https://huggingface.co/SmallDoge/Doge-160M-Instruct-SFT) | [smoltalk](https://huggingface.co/datasets/HuggingFaceTB/smoltalk) | 2 | 2048 | 4e-4 | 0.25M | bfloat16 |
+| [Doge-320M-Instruct-SFT](https://huggingface.co/SmallDoge/Doge-320M-Instruct-SFT) | [smoltalk](https://huggingface.co/datasets/HuggingFaceTB/smoltalk) | 2 | 2048 | 2e-4 | 0.25M | bfloat16 |
 
 
 **Procedure**:
@@ -107,13 +107,11 @@ We build the Doge-Instruct-SFT by SFT on [SmolTalk](https://huggingface.co/datas
 ## Citation
 
 ```bibtex
-@misc{shi2024wonderfulmatrices,
-      title={Wonderful Matrices: Combining for a More Efficient and Effective Foundation Model Architecture}, 
-      author={Jingze Shi and Bingheng Wu},
-      year={2024},
-      eprint={2412.11834},
-      archivePrefix={arXiv},
-      primaryClass={cs.LG},
-      url={https://arxiv.org/abs/2412.11834}, 
+@misc{smalldoges,
+  title={SmallDoges: A Family of Dynamic UltraFast Small Language Models}, 
+  author={Jingze, Shi and Yifan, Wu and Bingheng, Wu and Yuyu, Luo},
+  year={2025},
+  month={March},
+  url={https://github.com/SmallDoges/small-doge}
 }
 ```