OPTML-Group/SimNPO-TOFU-forget05-Llama-2-7b-chat

SimNPO-Unlearned Model on Task "TOFU - Forget05"

Model Details

• —Unlearning:
• —Task: 🤗datasets/locuslab/TOFU - Forget05
• —Method: SimNPO
• —Origin Model: 🤗OPTML-Group/TOFU-origin-Llama-2-7b-chat
• —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: 2.5
• —lambda: 0.1375
• —gamma: 0.0

Loading the Model

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("OPTML-Group/SimNPO-TOFU-forget05-Llama-2-7b-chat", use_flash_attention_2=True, torch_dtype=torch.bfloat16, trust_remote_code=True)

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