#
Downloading models

##
Integrated libraries

If a model on the Hub is tied to a supported library, loading the model can be done in just a few lines. For information on accessing the model, you can click on the “Use in Library” button on the model page to see how to do so. For example, `distilbert/distilgpt2` shows how to do so with 🤗 Transformers below.

##
Using the Hugging Face Client Library

You can use the `huggingface_hub` library to create, delete, update and retrieve information from repos. For example, to download the `HuggingFaceH4/zephyr-7b-beta` model from the command line, run

    huggingface-cli download HuggingFaceH4/zephyr-7b-beta

See the huggingface-cli download documentation for more information.

You can also integrate this into your own library. For example, you can quickly load a Scikit-learn model with a few lines.

    from huggingface_hub import hf_hub_download
    import joblib

    REPO_ID = "YOUR_REPO_ID"
    FILENAME = "sklearn_model.joblib"

    model = joblib.load(
        hf_hub_download(repo_id=REPO_ID, filename=FILENAME)
    )

##
Using Git

Since all models on the Model Hub are Git repositories, you can clone the models locally by running:

    git lfs install
    git clone git@hf.co:<MODEL ID> # example: git clone git@hf.co:bigscience/bloom

If you have write-access to the particular model repo, you’ll also have the ability to commit and push revisions to the model.

Add your SSH public key to your user settings to push changes and/or access private repos.

##
Faster downloads

If you are running on a machine with high bandwidth, you can increase your download speed with `hf_transfer`, a Rust-based library developed to speed up file transfers with the Hub.

    pip install "huggingface_hub[hf_transfer]"
    HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download ...

`hf_transfer` is a power user tool! It is tested and production-ready, but it lacks user-friendly features like advanced error handling or proxies. For more details, please take a look at this guide.

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