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

hf download HuggingFaceH4/zephyr-7b-beta

See the 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 Xet-backed Git repositories, you can clone the models locally by installing git-xet and running:

git xet install
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

hf_xet is a Rust-based package leveraging the Xet storage backend to optimize file transfers with chunk-based deduplication. By default, hf_xet uses adaptive concurrency — it automatically tunes the number of parallel transfer streams based on real-time network conditions, starting conservatively (1 stream) and scaling up to 64 concurrent streams as bandwidth permits.

For most machines — including data center environments — the default settings will already saturate the available network bandwidth. For advanced users on machines with high bandwidth and at least 64 GB of RAM, HF_XET_HIGH_PERFORMANCE=1 raises concurrency bounds and significantly increases memory buffer sizes, which can help when downloading many large files in parallel.

HF_XET_HIGH_PERFORMANCE=1 hf download ...