Improve model card metadata and description

Hi! I'm Niels from the Hugging Face community science team. I noticed this model repository is associated with the paper "Mix Data or Merge Models? Balancing the Helpfulness, Honesty, and Harmlessness of Large Language Model via Model Merging".

This PR updates the model card to include:
- `library_name: transformers` and `pipeline_tag: text-generation` metadata for better discoverability.
- Relevant tags such as `model-merging` and `RESM`.
- A description of the model and its purpose (achieving balanced Helpfulness, Honesty, and Harmlessness alignment via the RESM method).
- A proper BibTeX citation for the associated paper.

Please review and merge if this looks good!

README.md

@@ -1,13 +1,39 @@
 ---
-license: apache-2.0
 base_model:
 - meta-llama/Meta-Llama-3-8B-Instruct
+license: apache-2.0
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- model-merging
+- RESM
+- alignment
 ---
+
+# RESM Llama-3-8B-Instruct
+
+This model is a 3H-aligned (Helpfulness, Honesty, and Harmlessness) Large Language Model (LLM) developed as part of the research 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
+
+This model utilizes a novel merging method called **RESM** (**R**eweighting **E**nhanced task **S**ingular **M**erging). RESM addresses challenges in preference alignment such as noise accumulation and layer sparsity adaptation through outlier weighting and sparsity-aware rank selection strategies.
+
+The goal of this model is to achieve a balanced alignment across the three pillars of responsible AI:
+*   **Helpfulness**
+*   **Honesty**
+*   **Harmlessness**
+
+The paper systematically reveals the collaborative and conflict relationships among these dimensions and demonstrates that parameter-level merging using RESM outperforms standard data mixture methods.
+
 ## Citation
-```
+
+If you find this work useful, please cite the following paper:
+
+```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}
-}
+}
+```