README.md

---
license: mit
datasets:
- cais/wmdp
language:
- en
base_model:
- HuggingFaceH4/zephyr-7b-beta
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
---

# SimNPO-Unlearned Model on Task "WMDP"

## Model Details

- **Unlearning**:
  - **Task**: [🤗datasets/cais/wmdp](https://huggingface.co/datasets/cais/wmdp)
  - **Method**: [SimNPO](https://arxiv.org/abs/2410.07163)
- **Origin Model**: [🤗HuggingFaceH4/zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta)
- **Code Base**: [github.com/OPTML-Group/Unlearn-Simple](https://github.com/OPTML-Group/Unlearn-Simple)
- **Research Paper**: ["Simplicity Prevails: Rethinking Negative Preference Optimization for LLM Unlearning"](https://arxiv.org/abs/2410.07163)

## Unlearning Algorithm

This model uses the `SimNPO` unlearning algorithm with the following optimization objective:
$$\ell_{SimNPO}(\mathbf{\theta}) = \mathbb{E}_{(x, y) \in \mathcal{D}_f}\left[-\frac{2}{\beta}\log\sigma\left(-\frac{\beta}{|y|}\log\pi_{\mathbf{\theta}}(y|x) - \gamma\right)\right] + \lambda \mathbb{E}_{(x, y) \in \mathcal{D}_r}[-\log\pi_{\mathbf{\theta}} (y|x)]$$
Unlearning hyper-parameters:
- Learning Rate: `4e-6`
- beta: `5.5`
- lambda: `5.0`
- gamma: `0.0`

## Loading the Model

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("OPTML-Group/SimNPO-WMDP-zephyr-7b-beta", use_flash_attention_2=True, torch_dtype=torch.bfloat16, trust_remote_code=True)
```

## Evaluation Results
||1 - AccBio|1 - AccCyber|MMLU|
|---|---|---|---|
|Origin|0.352|0.608|0.585|
|NPO|0.581|0.616|0.476|
|**SimNPO**|0.584|0.678|0.471|

## Citation

If you use this model in your research, please cite: 
```
@article{fan2024simplicity,
  title={Simplicity Prevails: Rethinking Negative Preference Optimization for LLM Unlearning},
  author={Fan, Chongyu and Liu, Jiancheng and Lin, Licong and Jia, Jinghan and Zhang, Ruiqi and Mei, Song and Liu, Sijia},
  journal={arXiv preprint arXiv:2410.07163},
  year={2024}
}
```

## Reporting Issues

Reporting issues with the model: [github.com/OPTML-Group/Unlearn-Simple](https://github.com/OPTML-Group/Unlearn-Simple)