初始化项目，由ModelHub XC社区提供模型

Model: OPTML-Group/NPO-SAM-MUSE-NEWS
Source: Original Platform

README.md

@@ -0,0 +1,57 @@
+---
+license: mit
+language:
+- en
+base_model:
+- muse-bench/MUSE-news_target
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- unlearn
+- machine-unlearning
+- llm-unlearning
+- data-privacy
+- large-language-models
+- trustworthy-ai
+- trustworthy-machine-learning
+- language-model
+datasets:
+- muse-bench/MUSE-News
+---
+
+# NPO-Unlearned w/ SAM Model on Task "MUSE NEWS"
+
+## Model Details
+
+- **Unlearning**:
+  - **Task**: [🤗datasets/muse-bench/MUSE-News](https://huggingface.co/datasets/muse-bench/MUSE-News)
+  - **Method**: NPO
+  - **Smoothness Optimization**: Sharpness-aware Minimization (SAM) 
+- **Origin Model**: [🤗muse-bench/MUSE-news_target](https://huggingface.co/muse-bench/MUSE-news_target)
+- **Code Base**: [github.com/OPTML-Group/Unlearn-Smooth](https://github.com/OPTML-Group/Unlearn-Smooth)
+- **Research Paper**: ["Towards LLM Unlearning Resilient to Relearning Attacks: A Sharpness-Aware Minimization Perspective and Beyond"](https://arxiv.org/abs/2502.05374)
+
+## Loading the Model
+
+```python
+import torch
+from transformers import AutoModelForCausalLM
+
+model = AutoModelForCausalLM.from_pretrained("OPTML-Group/NPO-SAM-MUSE-NEWS", torch_dtype=torch.bfloat16, trust_remote_code=True)
+```
+
+## Citation
+
+If you use this model in your research, please cite: 
+```
+@article{fan2025towards,
+  title={Towards LLM Unlearning Resilient to Relearning Attacks: A Sharpness-Aware Minimization Perspective and Beyond},
+  author={Fan, Chongyu and Jia, Jinghan and Zhang, Yihua and Ramakrishna, Anil and Hong, Mingyi and Liu, Sijia},
+  journal={arXiv preprint arXiv:2502.05374},
+  year={2025}
+}
+```
+
+## Reporting Issues
+
+Reporting issues with the model: [github.com/OPTML-Group/Unlearn-Smooth](https://github.com/OPTML-Group/Unlearn-Smooth)