MUSE

MUSE

Installation

To create a conda environment for Python 3.10, run:

conda env create -f environment.yml
conda activate muse

Get the data & origin models

Two corpora News and Books and the associated target models are available as follows:

Domain	Target Model for Unlearning	Dataset
News	Target model	Dataset
Books	Target model	Dataset

Before proceeding, load all the data from HuggingFace to the root of this repostiory by running the following instruction:

python load_data.py

Get the unlearned model

Run unlearn.py in the baselines folder.

# news
python unlearn.py --algo simnpo_gdr --model_dir muse-bench/MUSE-News_target --tokenizer_dir meta-llama/Llama-2-7b-hf --data_file ../data/news/raw/forget.txt --retain_data_file ../data/news/raw/retain1.txt --out_dir ./ckpt/news/simnpo_gdr --max_len 2048 --epochs 10 --lr 1e-5 --per_device_batch_size 4 --beta 0.75 --gamma 3.0 --coeff 0.1 --npo_coeff 1.0

# books
python unlearn.py --algo simnpo_gdr --model_dir muse-bench/MUSE-Books_target --tokenizer_dir meta-llama/Llama-2-7b-hf --data_file ../data/books/raw/forget.txt --retain_data_file ../data/books/raw/retain1.txt --out_dir ./ckpt/books/simnpo_gdr --max_len 2048 --epochs 10 --lr 1e-5 --per_device_batch_size 4 --beta 0.7 --gamma 0.0 --coeff 0.1 --npo_coeff 1.0

• algo: Unlearning algorithm to run (simnpo, simnpo_gdr, ga, ga_gdr, ga_klr, npo, npo_gdr, npo_klr, or tv).
• model_dir: Directory of the target model.
• tokenizer_dir: Directory of the tokenizer.
• data_file: Forget set.
• retain_data_file: Retain set for GDR/KLR regularizations if required by the algorithm.
• out_dir: Directory to save the unlearned model (default: ckpt).
• max_len: Maximum input length (default: 2048).
• per_device_batch_size, epochs, lr: Hyperparameters.

---

Resulting models are saved in the ckpt folder as shown:

ckpt
├── news/
│   ├── simnpo_gdr/
│   │   ├── checkpoint-102
│   │   ├── checkpoint-204
│   │   ├── checkpoint-306
│   │   └── ...
│   └── npo/
│       └── ...
└── books/
    ├── simnpo_gdr
    └── ...

Evaluate the unlearned model

• To evaluate your unlearned model(s), run eval.py from the root of this repository with the following command-line arguments:

  • --model_dirs: A list of directories containing the unlearned models. These can be either HuggingFace model directories or local storage paths.
  • --names: A unique name assigned to each unlearned model in --model_dirs. The length of --names should match the length of --model_dirs.
  • --corpus: The corpus to use for evaluation. Options are news or books.
  • --out_file: The name of the output file. The file will be in CSV format, with each row corresponding to an unlearning method from --model_dirs, and columns representing the metrics specified by --metrics.
  • --tokenizer_dir (Optional): The directory of the tokenizer. Defaults to meta-llama/Llama-2-7b-hf, which is the default tokenizer for LLaMA.
  • --metrics (Optional): The metrics to evaluate. Options are verbmem_f (VerbMem Forget), privleak (PrivLeak), knowmem_f (KnowMem Forget), and knowmem_r (Knowmem Retain, i.e., Utility). Defaults to evaluating all these metrics.
  • --temp_dir (Optional): The directory for saving intermediate computations. Defaults to temp.

• Run the following command with placeholder values:

  python eval.py \
  --model_dirs "repo/model1" "repo/model2" \
  --names "model1" "model2" \
  --corpus books \
  --out_file "out.csv"

• For News, we select the result of simnpo from epoch 8. For Books, we select the result of simnpo from epoch 10.

Download the unlearned model directly