Hugging Face Hub Integration

Erasus can push unlearned models to the Hugging Face Hub and pull them for further use. Install the optional dependency:

pip install erasus[hub]
# or
pip install huggingface_hub datasets

Usage

Push an unlearned model

from erasus.integrations import HuggingFaceHub

hub = HuggingFaceHub(token="hf_...")  # or set HF_TOKEN env var

hub.push_model(
    model=unlearned_model,
    repo_id="username/my-unlearned-model",
    unlearning_info={
        "strategy": "gradient_ascent",
        "selector": "herding",
        "epochs": 5,
        "forget_size": 1000,
        "elapsed_time": 120.5,
        "metrics": {"mia_auc": 0.52, "accuracy": 0.91},
    },
    create_model_card=True,
)

This creates a repo (or updates it), uploads model.pt, unlearning_info.json, and a generated README model card.

Pull a model and metadata

path = hub.pull_model("username/my-unlearned-model", filename="model.pt")
info = hub.pull_unlearning_info("username/my-unlearned-model")

Load Hugging Face datasets

dataset = HuggingFaceHub.load_dataset("locuslab/TOFU", split="forget10")
loader = HuggingFaceHub.dataset_to_dataloader(dataset, batch_size=32)

Deployment checklist

• Token: Create a token at https://huggingface.co/settings/tokens and set HF_TOKEN or pass token=... to HuggingFaceHub().

• Repo: repo_id must be "org_or_user/repo_name". The Hub creates the repo on first push if it does not exist.

• Optional deps: Use pip install erasus[hub] or erasus[full] so huggingface_hub (and optionally datasets) are installed.