---
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)