Improve model card with metadata and description
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by
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HF Staff
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README.md
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license: apache-2.0
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base_model:
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- mistralai/Mistral-7B-Instruct-v0.2
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---
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## Citation
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@article{yang2025mix,
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title={Mix Data or Merge Models? Balancing the Helpfulness, Honesty, and Harmlessness of Large Language Model via Model Merging},
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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},
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journal={arXiv preprint arXiv:2502.06876},
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year={2025}
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}
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---
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base_model:
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- mistralai/Mistral-7B-Instruct-v0.2
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license: apache-2.0
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library_name: transformers
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pipeline_tag: text-generation
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---
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# Mistral-7B-RESM
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This model is a 3H-aligned 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).
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## Model Description
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Mistral-7B-RESM is based on [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) and was optimized across three critical dimensions: **Helpfulness, Honesty, and Harmlessness (3H optimization)**.
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The model was created using a novel parameter-level merging strategy called **RESM** (**R**eweighting **E**nhanced task **S**ingular **M**erging). RESM addresses challenges in 3H-aligned LLM merging, such as preference noise accumulation and layer sparsity adaptation, through outlier weighting and sparsity-aware rank selection strategies.
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## Citation
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```bibtex
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@article{yang2025mix,
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title={Mix Data or Merge Models? Balancing the Helpfulness, Honesty, and Harmlessness of Large Language Model via Model Merging},
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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},
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journal={arXiv preprint arXiv:2502.06876},
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year={2025}
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}
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```
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