Improve model card: add metadata, paper link, and description

README.md

@@ -1,14 +1,32 @@
 ---
-license: apache-2.0
 base_model:
 - mistralai/Mistral-7B-Instruct-v0.2
+license: apache-2.0
+library_name: transformers
+pipeline_tag: text-generation
 ---
 
+# RESM-Mistral-7B
+
+This repository contains the model weights for RESM-Mistral-7B, introduced in the paper [Mix Data or Merge Models? Balancing the Helpfulness, Honesty, and Harmlessness of Large Language Model via Model Merging](https://huggingface.co/papers/2502.06876).
+
+## Model Description
+
+RESM-Mistral-7B is an aligned Large Language Model (LLM) based on the Mistral-7B-Instruct-v0.2 architecture. It is designed to achieve a balanced alignment of **Helpfulness, Honesty, and Harmlessness** (3H optimization). 
+
+The model was created using a novel parameter-level merging method called **RESM** (**R**eweighting **E**nhanced task **S**ingular **M**erging). RESM addresses the challenges of preference noise accumulation and layer sparsity adaptation through:
+- **Outlier weighting**: Mitigating the impact of preference noise.
+- **Sparsity-aware rank selection**: Adapting to layer-wise variations in parameter importance.
+
+This approach allows the model to better navigate the collaborative and conflicting relationships between the 3H dimensions compared to traditional data mixture strategies.
+
 ## Citation
-```
+
+```bibtex
 @article{yang2025mix,
   title={Mix Data or Merge Models? Balancing the Helpfulness, Honesty, and Harmlessness of Large Language Model via Model Merging},
   author={Yang, Jinluan and Jin, Dingnan and Tang, Anke and Shen, Li and Zhu, Didi and Chen, Zhengyu and Wang, Daixin and Cui, Qing and Zhang, Zhiqiang and Zhou, Jun and others},
   journal={arXiv preprint arXiv:2502.06876},
   year={2025}
-}
+}
+```