Releases · huggingface/huggingface_hub

⚡️ OpenAI-compatible inference client!

The InferenceClient's chat completion API is now fully compliant with OpenAI client. This means it's a drop-in replacement in your script:

- from openai import OpenAI
+ from huggingface_hub import InferenceClient

- client = OpenAI(
+ client = InferenceClient(
    base_url=...,
    api_key=...,
)


output = client.chat.completions.create(
    model="meta-llama/Meta-Llama-3-8B-Instruct",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Count to 10"},
    ],
    stream=True,
    max_tokens=1024,
)

for chunk in output:
    print(chunk.choices[0].delta.content)

Why switching to InferenceClient if you already use OpenAI then? Because it's better integrated with HF services, such as the Serverless Inference API and Dedicated Endpoints. Check out the more detailed answer in this HF Post.

For more details about OpenAI compatibility, check out this guide's section.

True OpenAI drop-in replacement by InferenceClient by @Wauplin in #2384
Promote chat_completion in inference guide by @Wauplin in #2366

(other) `InferenceClient` improvements

Some new parameters have been added to the InferenceClient, following the latest changes in our Inference API:

prompt_name, truncate and normalize in feature_extraction
model_id and response_format, in chat_completion
adapter_id in text_generation
hypothesis_template and multi_labels in zero_shot_classification

Of course, all of those changes are also available in the AsyncInferenceClient async equivalent 🤗

Support truncate and normalize in InferenceClient by @Wauplin in #2270
Add prompt_name to feature-extraction + update types by @Wauplin in #2363
Send model_id in ChatCompletion request by @Wauplin in #2302
improve client.zero_shot_classification() by @MoritzLaurer in #2340
[InferenceClient] Add support for adapter_id (text-generation) and response_format (chat-completion) by @Wauplin in #2383

Added helpers for TGI servers:

get_endpoint_info to get information about an endpoint (running model, framework, etc.). Only available on TGI/TEI-powered models.
health_check to check health status of the server. Only available on TGI/TEI-powered models and only for InferenceEndpoint or local deployment. For serverless InferenceAPI, it's better to use get_model_status.

Support /info and /health routes by @Wauplin in #2269

Other fixes:

image_to_text output type has been fixed
use wait-for-model to avoid been rate limited while model is not loaded
add proxies support

Fix InferenceClient.image_to_text output value by @Wauplin in #2285
Fix always None in text_generation output by @Wauplin in #2316
Add wait-for-model header when sending request to Inference API by @Wauplin in #2318
Add proxy support on async client by @noech373 in #2350
Remove jinja tips + fix typo in chat completion docstring by @Wauplin in #2368

💾 Serialization

The serialization module introduced in v0.22.x has been improved to become the preferred way to serialize a torch model to disk. It handles how of the box sharding and safe serialization (using safetensors) with subtleties to work with shared layers. This logic was previously scattered in libraries like transformers, diffusers, accelerate and safetensors. The goal of centralizing it in huggingface_hub is to allow any external library to safely benefit from the same naming convention, making it easier to manage for end users.

>>> from huggingface_hub import save_torch_model
>>> model = ... # A PyTorch model

# Save state dict to "path/to/folder". The model will be split into shards of 5GB each and saved as safetensors.
>>> save_torch_model(model, "path/to/folder")

# Or save the state dict manually
>>> from huggingface_hub import save_torch_state_dict
>>> save_torch_state_dict(model.state_dict(), "path/to/folder")

More details in the serialization package reference.

Serialization: support saving torch state dict to disk by @Wauplin in #2314
Handle shared layers in save_torch_state_dict + add save_torch_model by @Wauplin in #2373

Some helpers related to serialization have been made public for reuse in external libraries:

get_torch_storage_id
get_torch_storage_size

Support max_shard_size as string in split_state_dict_into_shards_factory by @SunMarc in #2286
Make get_torch_storage_id public by @Wauplin in #2304

📁 HfFileSystem

The HfFileSystem has been improved to optimize calls, especially when listing files from a repo. This is especially useful for large datasets like HuggingFaceFW/fineweb for faster processing and reducing risk of being rate limited.

[HfFileSystem] Less /paths-info calls by @lhoestq in #2271
Update token type definition and arg description in hf_file_system.py by @lappemic in #2278
[HfFileSystem] Faster fs.walk() by @lhoestq in #2346

Thanks to @lappemic, HfFileSystem methods are now properly documented. Check it out here!

Document more HfFilesyStem Methods by @lappemic in #2380

✨ HfApi & CLI improvements

Commit API

A new mechanism has been introduced to prevent empty commits if no changes have been detected. Enabled by default in upload_file, upload_folder, create_commit and the huggingface-cli upload command. There is no way to force an empty commit.

Prevent empty commits if files did not change by @Wauplin in #2389

Resource groups

Resource Groups allow organizations administrators to group related repositories together, and manage access to those repos. It is now possible to specify a resource group ID when creating a repo:

from huggingface_hub import create_repo

create_repo("my-secret-repo", private=True, resource_group_id="66670e5163145ca562cb1988")

Support resource_group_id in create_repo by @Wauplin in #2324

Webhooks API

Webhooks allow you to listen for new changes on specific repos or to all repos belonging to particular set of users/organizations (not just your repos, but any repo). With the Webhooks API you can create, enable, disable, delete, update, and list webhooks from a script!

from huggingface_hub import create_webhook

# Example: Creating a webhook
webhook = create_webhook(
    url="https://webhook.site/your-custom-url",
    watched=[{"type": "user", "name": "your-username"}, {"type": "org", "name": "your-org-name"}],
    domains=["repo", "discussion"],
    secret="your-secret"
)

[wip] Implement webhooks API by @lappemic in #2209

Search API

The search API has been slightly improved. It is now possible to:

filter datasets by tags
filter which attributes should be returned in model_info/list_models (and similarly for datasets/Spaces). For example, you can ask the server to return downloadsAllTime for all models.

>>> from huggingface_hub import list_models

>>> for model in list_models(library="transformers", expand="downloadsAllTime", sort="downloads", limit=5):
...     print(model.id, model.downloads_all_time)
MIT/ast-finetuned-audioset-10-10-0.4593 1676502301
sentence-transformers/all-MiniLM-L12-v2 115588145
sentence-transformers/all-MiniLM-L6-v2 250790748
google-bert/bert-base-uncased 1476913254
openai/clip-vit-large-patch14 590557280

Support filtering datasets by tags by @Wauplin in #2266
Support expand parameter in xxx_info and list_xxxs (model/dataset/Space) by @Wauplin in #2333
Add InferenceStatus to ExpandModelProperty_T by @Wauplin in #2388
Do not mention gitalyUid in expand parameter by @Wauplin in #2395

CLI

It is now possible to delete files from a repo using the command line:

Delete a folder:

>>> huggingface-cli repo-files Wauplin/my-cool-model delete folder/  
Files correctly deleted from repo. Commit: https://huggingface.co/Wauplin/my-cool-mo...

Use Unix-style wildcards to delete sets of files:

>>> huggingface-cli repo-files Wauplin/my-cool-model delete *.txt folder/*.bin 
Files correctly deleted from repo. Commit: https://huggingface.co/Wauplin/my-cool-mo...

fix/issue 2090 : Add a repo_files command, with recursive deletion. by @OlivierKessler01 in #2280

ModelHubMixin

The ModelHubMixin, allowing for quick integration of external libraries with the Hub have been updated to fix some existing bugs and ease its use. Learn how to integrate your library from this guide.

Don't override 'config' in model_kwargs by @alexander-soare in #2274
Support custom kwargs for model card in save_pretrained by @qubvel in #2310
ModelHubMixin: Fix attributes lost in inheritance by @Wauplin in #2305
Fix ModelHubMixin coders by @gorold in #2291
Hot-fix: do not share tags between ModelHubMixin siblings by @Wauplin in #2394
Fix: correctly encode/decode config in ModelHubMixin if custom coders by @Wauplin in #2337