Xet is here! (+ many cool Inference-related things!)
This might just be our biggest update in the past two years! Xet is a groundbreaking new protocol for storing large objects in Git repositories, designed to replace Git LFS. Unlike LFS, which deduplicates files, Xet operates at the chunk level—making it a game-changer for AI builders collaborating on massive models and datasets. Our Python integration is powered by xet-core, a Rust-based package that handles all the low-level details.
You can start using Xet today by installing the optional dependency:
pip install -U huggingface_hub[hf_xet]With that, you can seamlessly download files from Xet-enabled repositories! And don’t worry—everything remains fully backward-compatible if you’re not ready to upgrade yet.
Blog post: Xet on the Hub
Docs: Storage backends → Xet
[!TIP]
Want to store your own files with Xet? We’re gradually rolling out support on the Hugging Face Hub, sohf_xetuploads may need to be enabled for your repo. Join the waitlist to get onboarded soon!
This is the result of collaborative work by @bpronan, @hanouticelina, @rajatarya, @jsulz, @assafvayner, @Wauplin, + many others on the infra/Hub side!
xetEnabled as an expand property by @hanouticelina in #2907The InferenceClient has received significant updates and improvements in this release, making it more robust and easy to work with.
We’re thrilled to introduce Cerebras and Cohere as official inference providers! This expansion strengthens the Hub as the go-to entry point for running inference on open-weight models.
Novita is now our 3rd provider to support text-to-video task after Fal.ai and Replicate:
from huggingface_hub import InferenceClient
client = InferenceClient(provider="novita")
video = client.text_to_video(
"A young man walking on the street",
model="Wan-AI/Wan2.1-T2V-14B",
)
It is now possible to centralize billing on your organization rather than individual accounts! This helps companies managing their budget and setting limits at a team level. Organization must be subscribed to Enterprise Hub.
from huggingface_hub import InferenceClient
client = InferenceClient(provider="fal-ai", bill_to="openai")
image = client.text_to_image(
"A majestic lion in a fantasy forest",
model="black-forest-labs/FLUX.1-schnell",
)
image.save("lion.png")
Handling long-running inference tasks just got easier! To prevent request timeouts, we’ve introduced asynchronous calls for text-to-video inference. We are expecting more providers to leverage the same structure soon, ensuring better robustness and developer-experience.
Miscellaneous improvements:
InferenceClient docstring to reflect that token=False is no longer accepted by @abidlabs in #2853provider parameter by @hanouticelina in #2949This release also includes several other notable features and improvements.
It's now possible to pass a path with wildcard to the upload command instead of passing --include=... option:
huggingface-cli upload my-cool-model *.safetensors
Deploying an Inference Endpoint from the Model Catalog just got 100x easier! Simply select which model to deploy and we handle the rest to guarantee the best hardware and settings for your dedicated endpoints.
from huggingface_hub import create_inference_endpoint_from_catalog
endpoint = create_inference_endpoint_from_catalog("unsloth/DeepSeek-R1-GGUF")
endpoint.wait()
endpoint.client.chat_completion(...)
The ModelHubMixin got two small updates:
config until now)You can now sort by name, size, last updated and last used where using the delete-cache command:
huggingface-cli delete-cache --sort=size--sort arg to delete-cache to sort by size by @AlpinDale in #2815Since end 2024, it is possible to manage the LFS files stored in a repo from the UI (see docs). This release makes it possible to do the same programmatically. The goal is to enable users to free-up some storage space in their private repositories.
>>> from huggingface_hub import HfApi
>>> api = HfApi()
>>> lfs_files = api.list_lfs_files("username/my-cool-repo")
# Filter files files to delete based on a combination of `filename`, `pushed_at`, `ref` or `size`.
# e.g. select only LFS files in the "checkpoints" folder
>>> lfs_files_to_delete = (lfs_file for lfs_file in lfs_files if lfs_file.filename.startswith("checkpoints/"))
# Permanently delete LFS files
>>> api.permanently_delete_lfs_files("username/my-cool-repo", lfs_files_to_delete)
[!WARNING] This is a power-user tool to use carefully. Deleting LFS files from a repo is a non-revertible action.
labels has been removed from InferenceClient.zero_shot_classification and InferenceClient.zero_shot_image_classification tasks in favor of candidate_labels. There has been a proper deprecation warning for that.
Thanks to the work previously introduced by the diffusers team, we've published a GitHub Action that runs code style tooling on demand on Pull Requests, making the life of contributors and reviewers easier.
Other minor updates:
The following contributors have made significant changes to the library over the last release:
InferenceClient docstring to reflect that token=False is no longer accepted (#2853)--sort arg to delete-cache to sort by size (#2815)Fetched April 7, 2026