Update README.md

README.md

@@ -21,12 +21,12 @@ tags:
 <p align="center">
           🤖 <a href="https://modelscope.cn/organization/inclusionAI">ModelScope</a>
           🤗 <a href="https://huggingface.co/inclusionAI">Hugging Face</a>
-          🖥️ <a href="https://github.com/inclusionAI/Ling">GitHub</a>
+          🖥️ <a href="https://github.com/codefuse-ai/Ling-Coder-Lite">GitHub</a>
 <p>
 
 ## Introduction
 
-Ling-Coder-Lite is a MoE LLM provided and open-sourced by InclusionAI, which has 16.8 billion parameters with 2.75 billion activated parameters. Ling-Coder-Lite performs impressively on coding tasks compared to existing models in the industry. Specifically, Ling-Coder-Lite further pre-training from an intermediate checkpoint of Ling-Lite, incorporating an additional 3 trillion tokens. This extended pre-training significantly boosts the coding abilities of Ling-Lite, while preserving its strong performance in general language tasks. More details are described in the technique report [Ling-Coder-TR](https://huggingface.co/papers/2503.17793).
+Ling-Coder-Lite is a MoE LLM provided and open-sourced by InclusionAI, which has 16.8B parameters with 2.75B activated parameters. This model demonstrates state-of-the-art performance on 12 coding benchmarks, while simultaneously offering competitive latency and throughput compared to code LLMs of similar size. In addition to open-sourcing the model itself, we also release a substantial amount of code-related data, including synthetic QA, SFT and DPO datasets. More details are described in the technique report [Ling-Coder-TR](https://huggingface.co/papers/2503.17793).
 
 ## Model Downloads
 
@@ -40,6 +40,18 @@ You can download the following table to see the various parameters for your use
 | Ling-Coder-lite |       16.8B       |         2.75B         |        16K         |     [🤗 HuggingFace](https://huggingface.co/inclusionAI/Ling-Coder-lite)     |
 </div>
 
+## Dataset Downloads
+
+<div align="center">
+
+|   **Model**    | **Samples** |                                                                     **Download**                                                                     |
+| :------------: | :----------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------: |
+| Ling-Coder-SyntheticQA |        24M         | [🤗 HuggingFace](https://huggingface.co/datasets/inclusionAI/Ling-Coder-SyntheticQA) |
+| Ling-Coder-SFT  |        5M         |      [🤗 HuggingFace](https://huggingface.co/datasets/inclusionAI/Ling-Coder-SFT) |
+| Ling-Coder-DPO  |        250K         | [🤗 HuggingFace](https://huggingface.co/datasets/inclusionAI/Ling-Coder-DPO) |
+
+</div>
+
 ## Evaluation
 
 Detailed evaluation results are reported in our technical report [Ling-Coder-TR](https://huggingface.co/papers/2503.17793).
@@ -89,7 +101,7 @@ print(response)
 ```
 
 ## Deployment
-Please refer to [Github](https://github.com/inclusionAI/Ling/blob/master/README.md)
+Please refer to [Github](https://github.com/codefuse-ai/Ling-Coder-Lite/blob/master/README.md)
 
 ## License
 This code repository is licensed under [the MIT License](https://huggingface.co/inclusionAI/Ling-Coder-lite/blob/main/LICENCE).
@@ -99,7 +111,7 @@ This code repository is licensed under [the MIT License](https://huggingface.co/
 ```
 @misc{codefuse2025samplemattersleveragingmixtureofexperts,
       title={Every Sample Matters: Leveraging Mixture-of-Experts and High-Quality Data for Efficient and Accurate Code LLM}, 
-      author={Codefuse and Ling Team and : and Wenting Cai and Yuchen Cao and Chaoyu Chen and Chen Chen and Siba Chen and Qing Cui and Peng Di and Junpeng Fang and Zi Gong and Ting Guo and Zhengyu He and Yang Huang and Cong Li and Jianguo Li and Zheng Li and Shijie Lian and BingChang Liu and Songshan Luo and Shuo Mao and Min Shen and Jian Wu and Jiaolong Yang and Wenjie Yang and Tong Ye and Hang Yu and Wei Zhang and Zhenduo Zhang and Hailin Zhao and Xunjin Zheng and Jun Zhou},
+      author={Codefuse and Ling Team},
       year={2025},
       eprint={2503.17793},
       archivePrefix={arXiv},