SimNPO-MUSE-Books-iclm-7b

SimNPO-Unlearned Model on Task "MUSE - Books"

Model Details

• Unlearning:
  • Task: 🤗datasets/muse-bench/MUSE-Books
  • Method: SimNPO
• Origin Model: 🤗muse-bench/MUSE-books_target
• 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: 1e-5
• beta: 0.7
• lambda: 1.0
• gamma: 0.0

Loading the Model

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("OPTML-Group/SimNPO-MUSE-Books-iclm-7b", torch_dtype=torch.bfloat16, device_map='auto')

Evaluation Results

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