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Releases: huggingface/huggingface_hub

0.20.3 hot-fix: Fix HfFolder login when env variable not set

22 Jan 08:58
@Wauplin Wauplin
v0.20.3
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0.20.3 hot-fix: Fix HfFolder login when env variable not set

This patch release fixes an issue when retrieving the locally saved token using huggingface_hub.HfFolder.get_token. For the record, this is a "planned to be deprecated" method, in favor of huggingface_hub.get_token which is more robust and versatile. The issue came from a breaking change introduced in #1895 meaning only 0.20.x is affected.

For more details, please refer to #1966.

Full Changelog: v0.20.2...v0.20.3

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0.20.2 hot-fix: Fix concurrency issues in google colab login

05 Jan 10:58
@Wauplin Wauplin
v0.20.2
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0.20.2 hot-fix: Fix concurrency issues in google colab login

A concurrency issue when using userdata.get to retrieve HF_TOKEN token led to deadlocks when downloading files in parallel. This hot-fix release fixes this issue by using a global lock before trying to get the token from the secrets vault. More details in #1953.

Full Changelog: v0.20.1...v0.20.2

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0.20.1: hot-fix Fix circular import

20 Dec 11:46
@Wauplin Wauplin
v0.20.1
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0.20.1: hot-fix Fix circular import

This hot-fix release fixes a circular import error happening when import login or logout helpers from huggingface_hub.

Related PR: #1930

Full Changelog: v0.20.0...v0.20.1

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v0.20.0: Authentication, speed, safetensors metadata, access requests and more.

20 Dec 10:16
@Wauplin Wauplin
v0.20.0
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v0.20.0: Authentication, speed, safetensors metadata, access requests and more.

(Discuss about the release in our Community Tab. Feedback welcome!! 🤗)

🔐 Authentication

Authentication has been greatly improved in Google Colab. The best way to authenticate in a Colab notebook is to define a HF_TOKEN secret in your personal secrets. When a notebook tries to reach the Hub, a pop-up will ask you if you want to share the HF_TOKEN secret with this notebook -as an opt-in mechanism. This way, no need to call huggingface_hub.login and copy-paste your token anymore! 🔥🔥🔥

In addition to the Google Colab integration, the login guide has been revisited to focus on security. It is recommended to authenticate either using huggingface_hub.login or the HF_TOKEN environment variable, rather than passing a hardcoded token in your scripts. Check out the new guide here.

🏎️ Faster HfFileSystem

HfFileSystem is a pythonic fsspec-compatible file interface to the Hugging Face Hub. Implementation has been greatly improved to optimize fs.find performances.

Here is a quick benchmark with the bigcode/the-stack-dedup dataset:

v0.19.4 v0.20.0
hffs.find("datasets/bigcode/the-stack-dedup", detail=False) 46.2s 1.63s
hffs.find("datasets/bigcode/the-stack-dedup", detail=True) 47.3s 24.2s

🚪 Access requests API (gated repos)

Models and datasets can be gated to monitor who's accessing the data you are sharing. You can also filter access with a manual approval of the requests. Access requests can now be managed programmatically using HfApi. This can be useful for example if you have advanced user request screening requirements (for advanced compliance requirements, etc) or if you want to condition access to a model based on completing a payment flow.

Check out this guide to learn more about gated repos.

>>> from huggingface_hub import list_pending_access_requests, accept_access_request

# List pending requests
>>> requests = list_pending_access_requests("meta-llama/Llama-2-7b")
>>> requests[0]
[
    AccessRequest(
        username='clem',
        fullname='Clem 🤗',
        email='***',
        timestamp=datetime.datetime(2023, 11, 23, 18, 4, 53, 828000, tzinfo=datetime.timezone.utc),
        status='pending',
        fields=None,
    ),
    ...
]

# Accept Clem's request
>>> accept_access_request("meta-llama/Llama-2-7b", "clem")

🔍 Parse Safetensors metadata

Safetensors is a simple, fast and secured format to save tensors in a file. Its advantages makes it the preferred format to host weights on the Hub. Thanks to its specification, it is possible to parse the file metadata on-the-fly. HfApi now provides get_safetensors_metadata, an helper to get safetensors metadata from a repo.

# Parse repo with single weights file
>>> metadata = get_safetensors_metadata("bigscience/bloomz-560m")
>>> metadata
SafetensorsRepoMetadata(
    metadata=None,
    sharded=False,
    weight_map={'h.0.input_layernorm.bias': 'model.safetensors', ...},
    files_metadata={'model.safetensors': SafetensorsFileMetadata(...)}
)
>>> metadata.files_metadata["model.safetensors"].metadata
{'format': 'pt'}

Other improvements

List and filter collections

You can now list collections on the Hub. You can filter them to return only collection containing a given item, or created by a given author.

>>> collections = list_collections(item="models/TheBloke/OpenHermes-2.5-Mistral-7B-GGUF", sort="trending", limit=5):
>>> for collection in collections:
...   print(collection.slug)
teknium/quantized-models-6544690bb978e0b0f7328748
AmeerH/function-calling-65560a2565d7a6ef568527af
PostArchitekt/7bz-65479bb8c194936469697d8c
gnomealone/need-to-test-652007226c6ce4cdacf9c233
Crataco/favorite-7b-models-651944072b4fffcb41f8b568

Respect .gitignore

upload_folder now respect gitignore files!

Previously it was possible to filter which files should be uploaded from a folder using the allow_patterns and ignore_patterns parameters. This can now automatically be done by simply creating a .gitignore file in your repo.

Robust uploads

Uploading LFS files has also gotten more robust with a retry mechanism if a transient error happen while uploading to S3.

Target language in InferenceClient.translation

InferenceClient.translation now supports src_lang/tgt_lang for applicable models.

>>> from huggingface_hub import InferenceClient
>>> client = InferenceClient()
>>> client.translation("My name is Sarah Jessica Parker but you can call me Jessica", model="facebook/mbart-large-50-many-to-many-mmt", src_lang="en_XX", tgt_lang="fr_XX")
"Mon nom est Sarah Jessica Parker mais vous pouvez m'appeler Jessica"
>>> client.translation("My name is Sarah Jessica Parker but you can call me Jessica", model="facebook/mbart-large-50-many-to-many-mmt", src_lang="en_XX", tgt_lang="es_XX")
'Mi nombre es Sarah Jessica Parker pero puedes llamarme Jessica'

Support source in reported EvalResult

EvalResult now support source_name and source_link to provide a custom source for a reported result.

  • Support source in EvalResult for model cards by @Wauplin in #1874

🛠️ Misc

Fetch all pull requests refs with list_repo_refs.

  • Add include_pull_requests to list_repo_refs by @Wauplin in #1822

Filter discussion when listing them with get_repo_discussions.

# List opened PR from "sanchit-gandhi" on model repo "openai/whisper-large-v3"
>>> from huggingface_hub import get_repo_discussions
>>> discussions = get_repo_discussions(
...     repo_id="openai/whisper-large-v3",
...     author="sanchit-gandhi",
...     discussion_type="pull_request",
...     discussion_status="open",
... )

New field createdAt for ModelInfo, DatasetInfo and SpaceInfo.

It's now possible to create an inference endpoint running on a custom docker image (typically: a TGI container).

# Start an Inference Endpoint running Zephyr-7b-beta on TGI
>>> from huggingface_hub import create_inference_endpoint
>>> endpoint = create_inference_endpoint(
...     "aws-zephyr-7b-beta-0486",
...     repository="HuggingFaceH4/zephyr-7b-beta",
...     framework="pytorch",
...     task="text-generation",
...     accelerator="gpu",
...     vendor="aws",
...     region="us-east-1",
...     type="protected",
...     instance_size="medium",
...     instance_type="g5.2xlarge",
...     custom_image={
...         "health_route": "/health",
...         "env": {
...             "MAX_BATCH_PREFILL_TOKENS": "2048",
...             "MAX_INPUT_LENGTH": "1024",
...             "MAX_TOTAL_TOKENS": "1512",
...             "MODEL_ID": "/repository"
...         },
...         "url": "ghcr.io/huggingface/text-generation-inference:1.1.0",
...     },
... )
  • Allow create inference endpoint from docker image by @Wauplin in #1861

Upload CLI: create branch when revision does not exist

  • Create branch if missing in hugginface-cli upload by @Wauplin in #1857

🖥️ Environment variables

huggingface_hub.constants.HF_HOME has been made a public constant (see reference).

Offline mode has gotten more consistent. If HF_HUB_OFFLINE is set, any http call to the Hub will fail. The fallback mechanism is snapshot_download has been refactored to be aligned with the hf_hub_download workflow. If offline mode is activated (or a connection error happens) and the files are already in the cache, snapshot_download returns the corresponding snapshot directory.

DO_NOT_TRACK environment variable is now respected to deactivate telemetry calls. This is similar to HF_HUB_DISABLE_TELEMETRY but not specific to Hugging Face.

📚 Documentation

Doc fixes

  • Fixing gated attribute type in docs by @ademait in #1848
  • Update modelcard_templa...
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pkking, Wauplin, and 10 other contributors
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v0.19.4 - Hot-fix: do not fail if pydantic install is corrupted

16 Nov 16:22
@Wauplin Wauplin
v0.19.4
a0ca2ba
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v0.19.4 - Hot-fix: do not fail if pydantic install is corrupted

On Python3.8, it is fairly easy to get a corrupted install of pydantic (more specificially, pydantic 2.x cannot run if tensorflow is installed because of an incompatible requirement on typing_extensions). Since pydantic is an optional dependency of huggingface_hub, we do not want to crash at huggingface_hub import time if pydantic install is corrupted. However this was the case because of how imports are made in huggingface_hub. This hot-fix releases fixes this bug. If pydantic is not correctly installed, we only raise a warning and continue as if it was not installed at all.

Related PR: #1829

Full Changelog: v0.19.3...v0.19.4

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v0.19.3 - Hot-fix: pin `pydantic<2.0` on Python3.8

15 Nov 15:01
@Wauplin Wauplin
v0.19.3
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v0.19.3 - Hot-fix: pin `pydantic<2.0` on Python3.8

Hot-fix release after #1828.

In 0.19.0 we've loosen pydantic requirements to accept both 1.x and 2.x since huggingface_hub is compatible with both. However, it started to cause issues when installing both huggingface_hub[inference] and tensorflow in a Python3.8 environment. The problem comes from the fact that on Python3.8, Pydantic>=2.x and tensorflow don't seem to be compatible. Tensorflow depends on
typing_extension<=4.5.0 while pydantic 2.x requires typing_extensions>=4.6. This causes a ImportError: cannot import name 'TypeAliasType' from 'typing_extensions'. when importing huggingface_hub.

As a side note, tensorflow support for Python3.8 has been dropped since 2.14.0. Therefore this issue should affect less and less users over time.

Full Changelog: v0.19.2...v0.19.3

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v0.19.2 - Patch: expose HF_HOME in constants

15 Nov 09:05
@Wauplin Wauplin
v0.19.2
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v0.19.2 - Patch: expose HF_HOME in constants

Not a hot-fix.

In #1786 (already release in 0.19.0), we harmonized the environment variables in the HF ecosystem with the goal to propagate this harmonization to other HF libraries. In this work, we forgot to expose HF_HOME as a constant value that can be reused, especially by transformers or datasets. This release fixes this (see #1825).

Full Changelog: v0.19.1...v0.19.2

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v0.19.1 - Hot-fix: ignore TypeError when listing models with corrupted ModelCard

13 Nov 16:06
@Wauplin Wauplin
v0.19.1
29bdc65
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v0.19.1 - Hot-fix: ignore TypeError when listing models with corrupted ModelCard

Full Changelog: v0.19.0...v0.19.1.

Fixes a regression bug (PR #1821) introduced in 0.19.0 that made looping over models with list_models fail. The problem came from the fact that we are now parsing the data returned by the server into Python objects. However for some models the metadata in the model card is not valid. This is usually checked by the server but some models created before we started to enforce correct metadata are not valid. This hot-fix fixes the issue by ignoring the corrupted data, if any.

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v0.19.0: Inference Endpoints and robustness!

08 Nov 13:27
@Wauplin Wauplin
v0.19.0
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v0.19.0: Inference Endpoints and robustness!

(Discuss about the release in our Community Tab. Feedback welcome!! 🤗)

🚀 Inference Endpoints API

Inference Endpoints provides a secure solution to easily deploy models hosted on the Hub in a production-ready infrastructure managed by Huggingface. With huggingface_hub>=0.19.0 integration, you can now manage your Inference Endpoints programmatically. Combined with the InferenceClient, this becomes the go-to solution to deploy models and run jobs in production, either sequentially or in batch!

Here is an example how to get an inference endpoint, wake it up, wait for initialization, run jobs in batch and pause back the endpoint. All of this in a few lines of code! For more details, please check out our dedicated guide.

>>> import asyncio
>>> from huggingface_hub import get_inference_endpoint

# Get endpoint + wait until initialized
>>> endpoint = get_inference_endpoint("batch-endpoint").resume().wait()

# Run inference
>>> async_client = endpoint.async_client
>>> results = asyncio.gather(*[async_client.text_generation(...) for job in jobs])

# Pause endpoint
>>> endpoint.pause()

⏬ Improved download experience

huggingface_hub is a library primarily used to transfer (huge!) files with the Huggingface Hub. Our goal is to keep improving the experience for this core part of the library. In this release, we introduce a more robust download mechanism for slow/limited connection while improving the UX for users with a high bandwidth available!

More robust downloads

Getting a connection error in the middle of a download is frustrating. That's why we've implemented a retry mechanism that automatically reconnects if a connection get closed or a ReadTimeout error is raised. The download restart exactly where it stopped without having to redownload any bytes.

  • Retry on ConnectionError/ReadTimeout when streaming file from server by @Wauplin in #1766
  • Reset nb_retries if data has been received from the server by @Wauplin in #1784

In addition to this, it is possible to configure huggingface_hub with higher timeouts thanks to @Shahafgo. This should help getting around some issues on slower connections.

  • Adding the ability to configure the timeout of get request by @Shahafgo in #1720
  • Fix a bug to respect the HF_HUB_ETAG_TIMEOUT. by @Shahafgo in #1728

Progress bars while using hf_transfer

hf_transfer is a Rust-based library focused on improving upload and download speed on machines with a high bandwidth available. Once installed (pip install -U hf_transfer), it can transparently be used with huggingface_hub simply by setting HF_HUB_ENABLE_HF_TRANSFER=1 as environment variable. The counterpart of higher performances is the lack of some user-friendly features such as better error handling or a retry mechanism -meaning it is recommended only to power-users-. In this release we still ship a new feature to improve UX: progress bars. No need to update any existing code, a simple library upgrade is enough.

📚 Documentation

huggingface-cli guide

huggingface-cli is the CLI tool shipped with huggingface_hub. It recently got some nice improvement, especially with commands to download and upload files directly from the terminal. All of this needed a guide, so here it is!

Environment variables

Environment variables are useful to configure how huggingface_hub should work. Historically we had some inconsistencies on how those variables were named. This is now improved, with a backward compatible approach. Please check the package reference for more details. The goal is to propagate those changes to the whole HF-ecosystem, making configuration easier for everyone.

  • Harmonize environment variables by @Wauplin in #1786
  • Ensure backward compatibility for HUGGING_FACE_HUB_TOKEN env variable by @Wauplin in #1795
  • Do not promote HF_ENDPOINT environment variable by @Wauplin in #1799

Hindi translation

Hindi documentation landed on the Hub thanks to @aneeshd27! Checkout the Hindi version of the quickstart guide here.

  • Added translation of 3 files as mentioned in issue by @aneeshd27 in #1772

Minor docs fixes

💔 Breaking changes

Legacy ModelSearchArguments and DatasetSearchArguments have been completely removed from huggingface_hub. This shouldn't cause problem as they were already not in use (and unusable in practice).

  • Removed GeneralTags, ModelTags and DatasetTags by @VictorHugoPilled in #1761

Classes containing details about a repo (ModelInfo, DatasetInfo and SpaceInfo) have been refactored by @mariosasko to be more Pythonic and aligned with the other classes in huggingface_hub. In particular those objects are now based the dataclass module instead of a custom ReprMixin class. Every change is meant to be backward compatible, meaning no breaking changes is expected. However, if you detect any inconsistency, please let us know and we will fix it asap.

The legacy Repository and InferenceAPI classes are now deprecated but will not be removed before the next major release (v1.0).
Instead of the git-based Repository, we advice to use the http-based HfApi. Check out this guide explaining the reasons behind it. For InferenceAPI, we recommend to switch to InferenceClient which is much more feature-complete and will keep getting improved.

⚙️ Miscellaneous improvements, fixes and maintenance

InferenceClient

HfFileSystem

  • [hffs] Raise NotImplementedError on transaction commits by @Wauplin in #1736
  • Fix huggingface filesystem repo_type not forwarded by @Wauplin in #1791
  • Fix HfFileSystemFile when init fails + improve error message by @Wauplin in #1805

FIPS compliance

  • Set usedforsecurity=False in hashlib methods (FIPS compliance) by @Wauplin in #1782

Misc fixes

Internal

🤗 Significant community contributions

The following contributors have made significant changes to the library over the last release:

  • @VictorHugoPilled
    • Removed GeneralTags, ModelTags and DatasetTags (#1761)
  • @aneeshd27
    • Added translation of 3 files as mentioned in issue (#1772)

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julien-c, beeender, and 11 other contributors
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v0.18.0: Collection API, translated documentation and more!

06 Oct 11:56
@Wauplin Wauplin
v0.18.0
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v0.18.0: Collection API, translated documentation and more!

(Discuss about the release and provide feedback in the Community Tab!)

Collection API 🎉

Collection API is now fully supported in huggingface_hub!

A collection is a group of related items on the Hub (models, datasets, Spaces, papers) that are organized together on the same page. Collections are useful for creating your own portfolio, bookmarking content in categories, or presenting a curated list of items you want to share. Check out this guide to understand in more detail what collections are and this guide to learn how to build them programmatically.

Create/get/update/delete collection:

  • get_collection
  • create_collection: title, description, namespace, private
  • update_collection_metadata: title, description, position, private, theme
  • delete_collection

Add/update/remove item from collection:

  • add_collection_item: item id, item type, note
  • update_collection_item: note, position
  • delete_collection_item

Usage

>>> from huggingface_hub import get_collection
>>> collection = get_collection("TheBloke/recent-models-64f9a55bb3115b4f513ec026")
>>> collection.title
'Recent models'
>>> len(collection.items)
37
>>> collection.items[0]
CollectionItem: {
    {'_id': '6507f6d5423b46492ee1413e',
    'id': 'TheBloke/TigerBot-70B-Chat-GPTQ',
    'author': 'TheBloke',
    'item_type': 'model',
    'lastModified': '2023-09-19T12:55:21.000Z',
    (...)
}}
>>> from huggingface_hub import create_collection

# Create collection
>>> collection = create_collection(
...     title="ICCV 2023",
...     description="Portfolio of models, papers and demos I presented at ICCV 2023",
... )

# Add item with a note
>>> add_collection_item(
...     collection_slug=collection.slug,  # e.g. "davanstrien/climate-64f99dc2a5067f6b65531bab"
...     item_id="datasets/climate_fever",
...     item_type="dataset",
...     note="This dataset adopts the FEVER methodology that consists of 1,535 real-world claims regarding climate-change collected on the internet."
... )

📚 Translated documentation

Documentation is now available in both German and Korean thanks to community contributions! This is an important milestone for Hugging Face in its mission to democratize good machine learning.

Preupload files before committing

(Disclaimer: this is a power-user usage. It is not expected to be used directly by end users.)

When using create_commit (or upload_file/upload_folder), the internal workflow has 3 main steps:

  1. List the files to upload and check if those are regular files (text) or LFS files (binaries or huge files)
  2. Upload the LFS files to S3
  3. Create a commit on the Hub (upload regular files + reference S3 urls at once). The LFS upload is important to avoid large payloads during the commit call.

In this release, we introduce preupload_lfs_files to perform step 2 independently of step 3. This is useful for libraries like datasets that generate huge files "on-the-fly" and want to preupload them one by one before making one commit with all the files. For more details, please read this guide.

Miscellaneous improvements

❤️ List repo likers

Similarly to list_user_likes (listing all likes of a user), we now introduce list_repo_likers to list all likes on a repo - thanks to @issamarabi.

>>> from huggingface_hub import list_repo_likers
>>> likers = list_repo_likers("gpt2")
>>> len(likers)
204
>>> likers
[User(username=..., fullname=..., avatar_url=...), ...]

Refactored Dataset Card template

Template for the Dataset Card has been updated to be more aligned with the Model Card template.

QOL improvements

This release also adds a few QOL improvement for the users:

  • Suggest to check firewall/proxy settings + default to local file by @Wauplin in #1670
  • debug logs to debug level by @Wauplin (direct commit on main)
  • Change TimeoutError => asyncio.TimeoutError by @matthewgrossman in #1666
  • Handle refs/convert/parquet and PR revision correctly in hffs by @Wauplin in #1712
  • Document hf_transfer more prominently by @Wauplin in #1714

Breaking change

A breaking change has been introduced in CommitOperationAdd in order to implement preupload_lfs_files in a way that is convenient for the users. The main change is that CommitOperationAdd is no longer a static object but is modified internally by preupload_lfs_files and create_commit. This means that you cannot reuse a CommitOperationAdd object once it has been committed to the Hub. If you do so, an explicit exception will be raised. You can still reuse the operation objects if the commit call failed and you retry it. We hope that it will not affect any users but please open an issue if you're encountering any problem.

⚙️ Small fixes and maintenance

Docs fixes

Misc fixes

Internal

  • bump version to 0.18.0.dev0 by @Wauplin in #1658
  • sudo apt update in CI by @Wauplin (direct commit on main)
  • fix CI tests by @Wauplin (direct commit on main)
  • Skip flaky InferenceAPI test by @Wauplin (direct commit on main)
  • Respect HTTPError spec by @Wauplin in #1693
  • skip flaky test by @Wauplin (direct commit on main)
  • Fix LFS tests after password auth deprecation by @Wauplin in #1713

🤗 Significant community contributions

The following contributors have made significant changes to the library over the last release:

  • @martinbrose
    • Correct typo in upload guide (#1677)
    • 🌐 [i18n-DE] Translate docs to German (#1646)
    • Fixes filtering by tags with list_models and adds test case (#1673)
    • Add German concepts guide (#1686)
    • Address failing _check_disk_space() when path doesn't exist yet (#1692)
  • @wonhyeongseo
    • 🌐 [i18n-KO] Translated README, landing docs to Korean (#1667)

Contributors

matthewgrossman, Wauplin, and 5 other contributors
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