Add link to paper and mention Github repository (#1)

- Add link to paper and mention Github repository (e56d963ff921359d26349b7c00c96a9b03c6c914)


Co-authored-by: Niels Rogge <[email protected]>

README.md

@@ -1,5 +1,6 @@
 ---
-license: mit
+base_model:
+- inclusionAI/Ling-Coder-lite-base
 datasets:
 - inclusionAI/Ling-Coder-SFT
 - inclusionAI/Ling-Coder-SyntheticQA
@@ -7,14 +8,14 @@ datasets:
 language:
 - en
 - zh
-base_model:
-- inclusionAI/Ling-Coder-lite-base
-pipeline_tag: text-generation
 library_name: transformers
+license: mit
+pipeline_tag: text-generation
 tags:
 - code
 - moe
 ---
+
 # Ling-Coder-lite
 
 <p align="center">
@@ -29,6 +30,8 @@ tags:
 
 ## Introduction
 
+This repository contains the model 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).
+
 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.
 
 ## Model Downloads
@@ -109,4 +112,4 @@ This code repository is licensed under [the MIT License](https://huggingface.co/
       primaryClass={cs.LG},
       url={https://arxiv.org/abs/2503.17793}, 
 }
-```
+```