Download files from the Hub

The huggingface_hub library provides functions to download files from the repositories
stored on the Hub. You can use these functions independently or integrate them into your
own library, making it more convenient for your users to interact with the Hub. This
guide will show you how to:

• Download and cache a single file.
• Download and cache an entire repository.
• Download files to a local folder.

Download a single file

The hf_hub_download() function is the main function for downloading files from the Hub.
It downloads the remote file, caches it on disk (in a version-aware way), and returns its local file path.

The returned filepath is a pointer to the HF local cache. Therefore, it is important to not modify the file to avoid
having a corrupted cache. If you are interested in getting to know more about how files are cached, please refer to our
caching guide.

From latest version

Select the file to download using the repo_id, repo_type and filename parameters. By default, the file will
be considered as being part of a model repo.

from
 huggingface_hub 
import
 hf_hub_download
 hf_hub_download(repo_id=
"lysandre/arxiv-nlp"
, filename=
"config.json"
)
'/root/.cache/huggingface/hub/models--lysandre--arxiv-nlp/snapshots/894a9adde21d9a3e3843e6d5aeaaf01875c7fade/config.json'

 
 hf_hub_download(repo_id=
"google/fleurs"
, filename=
"fleurs.py"
, repo_type=
"dataset"
)
'/root/.cache/huggingface/hub/datasets--google--fleurs/snapshots/199e4ae37915137c555b1765c01477c216287d34/fleurs.py'

From specific version

By default, the latest version from the main branch is downloaded. However, in some cases you want to download a file
at a particular version (e.g. from a specific branch, a PR, a tag or a commit hash).
To do so, use the revision parameter:

 
 hf_hub_download(repo_id=
"lysandre/arxiv-nlp"
, filename=
"config.json"
, revision=
"v1.0"
)

 
 hf_hub_download(repo_id=
"lysandre/arxiv-nlp"
, filename=
"config.json"
, revision=
"test-branch"
)

 
 hf_hub_download(repo_id=
"lysandre/arxiv-nlp"
, filename=
"config.json"
, revision=
"refs/pr/3"
)

 
 hf_hub_download(repo_id=
"lysandre/arxiv-nlp"
, filename=
"config.json"
, revision=
"877b84a8f93f2d619faa2a6e514a32beef88ab0a"
)

Note: When using the commit hash, it must be the full-length hash instead of a 7-character commit hash.

Construct a download URL

In case you want to construct the URL used to download a file from a repo, you can use hf_hub_url() which returns a URL.
Note that it is used internally by hf_hub_download().

Download an entire repository

snapshot_download() downloads an entire repository at a given revision. It uses internally hf_hub_download() which
means all downloaded files are also cached on your local disk. Downloads are made concurrently to speed-up the process.

To download a whole repository, just pass the repo_id and repo_type:

from
 huggingface_hub 
import
 snapshot_download
 snapshot_download(repo_id=
"lysandre/arxiv-nlp"
)
'/home/lysandre/.cache/huggingface/hub/models--lysandre--arxiv-nlp/snapshots/894a9adde21d9a3e3843e6d5aeaaf01875c7fade'

 
 snapshot_download(repo_id=
"google/fleurs"
, repo_type=
"dataset"
)
'/home/lysandre/.cache/huggingface/hub/datasets--google--fleurs/snapshots/199e4ae37915137c555b1765c01477c216287d34'

snapshot_download() downloads the latest revision by default. If you want a specific repository revision, use the
revision parameter:

from
 huggingface_hub 
import
 snapshot_download
 snapshot_download(repo_id=
"lysandre/arxiv-nlp"
, revision=
"refs/pr/1"
)

Filter files to download

snapshot_download() provides an easy way to download a repository. However, you don’t always want to download the
entire content of a repository. For example, you might want to prevent downloading all .bin files if you know you’ll
only use the .safetensors weights. You can do that using allow_patterns and ignore_patterns parameters.

These parameters accept either a single pattern or a list of patterns. Patterns are Standard Wildcards (globbing
patterns) as documented here. The pattern matching is
based on fnmatch.

For example, you can use allow_patterns to only download JSON configuration files:

from
 huggingface_hub 
import
 snapshot_download
 snapshot_download(repo_id=
"lysandre/arxiv-nlp"
, allow_patterns=
"*.json"
)

On the other hand, ignore_patterns can exclude certain files from being downloaded. The
following example ignores the .msgpack and .h5 file extensions:

from
 huggingface_hub 
import
 snapshot_download
 snapshot_download(repo_id=
"lysandre/arxiv-nlp"
, ignore_patterns=[
"*.msgpack"
, 
"*.h5"
])

Finally, you can combine both to precisely filter your download. Here is an example to download all json and markdown
files except vocab.json.

from
 huggingface_hub 
import
 snapshot_download
 snapshot_download(repo_id=
"gpt2"
, allow_patterns=[
"*.md"
, 
"*.json"
], ignore_patterns=
"vocab.json"
)

Download file(s) to local folder

The recommended (and default) way to download files from the Hub is to use the cache-system.
You can define your cache location by setting cache_dir parameter (both in hf_hub_download() and snapshot_download()).

However, in some cases you want to download files and move them to a specific folder. This is useful to get a workflow
closer to what git commands offer. You can do that using the local_dir and local_dir_use_symlinks parameters:

• local_dir must be a path to a folder on your system. The downloaded files will keep the same file structure as in the
  repo. For example if filename="data/train.csv" and local_dir="path/to/folder", then the returned filepath will be
  "path/to/folder/data/train.csv".
• local_dir_use_symlinks defines how the file must be saved in your local folder.
  • The default behavior ("auto") is to duplicate small files (<5MB) and use symlinks for bigger files. Symlinks allow
    to optimize both bandwidth and disk usage. However manually editing a symlinked file might corrupt the cache, hence
    the duplication for small files. The 5MB threshold can be configured with the HF_HUB_LOCAL_DIR_AUTO_SYMLINK_THRESHOLD
    environment variable.
  • If local_dir_use_symlinks=True is set, all files are symlinked for an optimal disk space optimization. This is
    for example useful when downloading a huge dataset with thousands of small files.
  • Finally, if you don’t want symlinks at all you can disable them (local_dir_use_symlinks=False). The cache directory
    will still be used to check wether the file is already cached or not. If already cached, the file is duplicated
    from the cache (i.e. saves bandwidth but increases disk usage). If the file is not already cached, it will be
    downloaded and moved directly to the local dir. This means that if you need to reuse it somewhere else later, it
    will be re-downloaded.

Here is a table that summarizes the different options to help you choose the parameters that best suit your use case.

Parameters
File already cached
Returned path
Can read path?
Can save to path?
Optimized bandwidth
Optimized disk usage
local_dir=None

symlink in cache
✅
❌
(save would corrupt the cache)
✅
✅
local_dir="path/to/folder"
local_dir_use_symlinks="auto"

file or symlink in folder
✅
✅ (for small files)
⚠️ (for big files do not resolve path before saving)
✅
✅
local_dir="path/to/folder"
local_dir_use_symlinks=True

symlink in folder
✅
⚠️
(do not resolve path before saving)
✅
✅
local_dir="path/to/folder"
local_dir_use_symlinks=False
No
file in folder
✅
✅
❌
(if re-run, file is re-downloaded)
⚠️
(multiple copies if ran in multiple folders)
local_dir="path/to/folder"
local_dir_use_symlinks=False
Yes
file in folder
✅
✅
⚠️
(file has to be cached first)
❌
(file is duplicated)

Note: if you are on a Windows machine, you need to enable developer mode or run huggingface_hub as admin to enable
symlinks. Check out the cache limitations section for more details.