Add link to paper and GitHub repository

README.md

@@ -1,16 +1,17 @@
 ---
-license: mit
 datasets:
 - inclusionAI/Ling-Coder-SyntheticQA
 language:
 - en
 - zh
-pipeline_tag: text-generation
 library_name: transformers
+license: mit
+pipeline_tag: text-generation
 tags:
 - code
 - moe
 ---
+
 # Ling-Coder-lite-base
 
 <p align="center">
@@ -25,7 +26,7 @@ tags:
 
 ## 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.
+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.  This model is described in the paper [Every Sample Matters: Leveraging Mixture-of-Experts and High-Quality Data for Efficient and Accurate Code LLM](https://huggingface.co/papers/2503.17793).
 
 ## Model Downloads
 
@@ -105,4 +106,4 @@ This code repository is licensed under [the MIT License](https://huggingface.co/
       primaryClass={cs.LG},
       url={https://arxiv.org/abs/2503.17793}, 
 }
-```
+```