# Downloading models

## Integrated libraries

If a model on the Hub is tied to a [supported library](./models-libraries), 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`](https://github.com/huggingface/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
```bash
hf download HuggingFaceH4/zephyr-7b-beta
```
See the [CLI download documentation](https://huggingface.co/docs/huggingface_hub/en/guides/cli#download-an-entire-repository) 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.
```py
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](./xet/using-xet-storage#git-xet) and running:

```bash
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](https://huggingface.co/settings/keys) to push changes and/or access private repos.

## Faster downloads

`hf_xet` is a Rust-based package leveraging the [Xet storage backend](https://huggingface.co/docs/hub/en/xet/index) 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.

```bash
HF_XET_HIGH_PERFORMANCE=1 hf download ...
```

## Using hf-mount

For large models, you can mount a repo as a local filesystem with [hf-mount](https://github.com/huggingface/hf-mount) instead of downloading the full repo. Files are fetched lazily — only the bytes your code reads hit the network.

```bash
brew install hf-mount
hf-mount start repo openai-community/gpt2 /tmp/gpt2
```

Repos are mounted read-only. See [Mount as a Local Filesystem](./storage-buckets-access#mount-as-a-local-filesystem) for full setup details, backend options, and caching.