SimNPO-WMDP-llama3-8b

SimNPO-Unlearned Model on Task "WMDP"

Model Details

• Unlearning:
  • Task: 🤗datasets/cais/wmdp
  • Method: SimNPO
• Origin Model: 🤗meta-llama/Meta-Llama-3-8B-Instruct
• Code Base: github.com/OPTML-Group/Unlearn-Simple
• Research Paper: "Simplicity Prevails: Rethinking Negative Preference Optimization for LLM Unlearning"

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: 3.5e-5
• beta: 0.1
• lambda: 1.0
• gamma: 0.0
• steps: 125

Loading the Model

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("OPTML-Group/SimNPO-WMDP-llama3-8b", use_flash_attention_2=True, torch_dtype=torch.bfloat16, trust_remote_code=True)

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