Concept Lancet: Image Editing with Compositional Representation Transplant
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CCB
authored
a
paper
13 days ago
Post
2076
‼️ huggingface_hub's v0.30.0 is out with our biggest update of the past two years!
Full release notes: https://github.com/huggingface/huggingface_hub/releases/tag/v0.30.0.
🚀 Ready. Xet. Go!
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](https://github.com/huggingface/xet-core), a Rust-based package that handles all the low-level details.
You can start using Xet today by installing the optional dependency:
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: https://huggingface.co/blog/xet-on-the-hub
Docs: https://huggingface.co/docs/hub/en/storage-backends#xet
⚡ Inference Providers
- 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.
- Centralized billing: manage your budget and set team-wide spending limits for Inference Providers! Available to all Enterprise Hub organizations.
- No more timeouts when generating videos, thanks to async calls. Available right now for Fal.ai, expecting more providers to leverage the same structure very soon!
Full release notes: https://github.com/huggingface/huggingface_hub/releases/tag/v0.30.0.
🚀 Ready. Xet. Go!
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](https://github.com/huggingface/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: https://huggingface.co/blog/xet-on-the-hub
Docs: https://huggingface.co/docs/hub/en/storage-backends#xet
⚡ Inference Providers
- 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.
- Centralized billing: manage your budget and set team-wide spending limits for Inference Providers! Available to all Enterprise Hub organizations.
from huggingface_hub import InferenceClient
client = InferenceClient(provider="fal-ai", bill_to="my-cool-company")
image = client.text_to_image(
"A majestic lion in a fantasy forest",
model="black-forest-labs/FLUX.1-schnell",
)
image.save("lion.png")- No more timeouts when generating videos, thanks to async calls. Available right now for Fal.ai, expecting more providers to leverage the same structure very soon!
- 6 replies
brucewoods
updated
a
model
27 days ago
CCB
authored
2
papers
about 2 months ago
ontenkutsenko
updated
3
models
4 months ago
vishalbakshi
updated
a
model
5 months ago
balajivai
updated
3
models
6 months ago
Post
3131
What a great milestone to celebrate! The huggingface_hub library is slowly becoming a cornerstone of the Python ML ecosystem when it comes to interacting with the
@huggingface
Hub. It wouldn't be there without the hundreds of community contributions and feedback! No matter if you are loading a model, sharing a dataset, running remote inference or starting jobs on our infra, you are for sure using it! And this is only the beginning so give a star if you wanna follow the project 👉 https://github.com/huggingface/huggingface_hub
- 1 reply
CCB
authored
a
paper
7 months ago
Post
4688
🚀 Exciting News! 🚀
We've just released 𝚑𝚞𝚐𝚐𝚒𝚗𝚐𝚏𝚊𝚌𝚎_𝚑𝚞𝚋 v0.25.0 and it's packed with powerful new features and improvements!
✨ 𝗧𝗼𝗽 𝗛𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝘀:
• 📁 𝗨𝗽𝗹𝗼𝗮𝗱 𝗹𝗮𝗿𝗴𝗲 𝗳𝗼𝗹𝗱𝗲𝗿𝘀 with ease using
• 🔎 𝗦𝗲𝗮𝗿𝗰𝗵 𝗔𝗣𝗜: new search filters (gated status, inference status) and fetch trending score.
• ⚡𝗜𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲𝗖𝗹𝗶𝗲𝗻𝘁: major improvements simplifying chat completions and handling async tasks better.
We’ve also introduced tons of bug fixes and quality-of-life improvements - thanks to the awesome contributions from our community! 💪
💡 Check out the release notes: Wauplin/huggingface_hub#8
Want to try it out? Install the release with:
We've just released 𝚑𝚞𝚐𝚐𝚒𝚗𝚐𝚏𝚊𝚌𝚎_𝚑𝚞𝚋 v0.25.0 and it's packed with powerful new features and improvements!
✨ 𝗧𝗼𝗽 𝗛𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝘀:
• 📁 𝗨𝗽𝗹𝗼𝗮𝗱 𝗹𝗮𝗿𝗴𝗲 𝗳𝗼𝗹𝗱𝗲𝗿𝘀 with ease using
huggingface-cli upload-large-folder. Designed for your massive models and datasets. Much recommended if you struggle to upload your Llama 70B fine-tuned model 🤡• 🔎 𝗦𝗲𝗮𝗿𝗰𝗵 𝗔𝗣𝗜: new search filters (gated status, inference status) and fetch trending score.
• ⚡𝗜𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲𝗖𝗹𝗶𝗲𝗻𝘁: major improvements simplifying chat completions and handling async tasks better.
We’ve also introduced tons of bug fixes and quality-of-life improvements - thanks to the awesome contributions from our community! 💪
💡 Check out the release notes: Wauplin/huggingface_hub#8
Want to try it out? Install the release with:
pip install huggingface_hub==0.25.0
- 1 reply
sanketsans
updated
a
model
7 months ago
BingyuanWang
updated
a
model
8 months ago
Post
2025
🚀 Just released version 0.24.0 of the 𝚑𝚞𝚐𝚐𝚒𝚗𝚐𝚏𝚊𝚌𝚎_𝚑𝚞𝚋 Python library!
Exciting updates include:
⚡ InferenceClient is now a drop-in replacement for OpenAI's chat completion!
✨ Support for response_format, adapter_id , truncate, and more in InferenceClient
💾 Serialization module with a save_torch_model helper that handles shared layers, sharding, naming convention, and safe serialization. Basically a condensed version of logic scattered across safetensors, transformers , accelerate
📁 Optimized HfFileSystem to avoid getting rate limited when browsing HuggingFaceFW/fineweb
🔨 HfApi & CLI improvements: prevent empty commits, create repo inside resource group, webhooks API, more options in the Search API, etc.
Check out the full release notes for more details:
Wauplin/huggingface_hub#7
👀
Exciting updates include:
⚡ InferenceClient is now a drop-in replacement for OpenAI's chat completion!
✨ Support for response_format, adapter_id , truncate, and more in InferenceClient
💾 Serialization module with a save_torch_model helper that handles shared layers, sharding, naming convention, and safe serialization. Basically a condensed version of logic scattered across safetensors, transformers , accelerate
📁 Optimized HfFileSystem to avoid getting rate limited when browsing HuggingFaceFW/fineweb
🔨 HfApi & CLI improvements: prevent empty commits, create repo inside resource group, webhooks API, more options in the Search API, etc.
Check out the full release notes for more details:
Wauplin/huggingface_hub#7
👀
-
- 6 replies
Post
3385
🚀 I'm excited to announce that huggingface_hub's InferenceClient now supports OpenAI's Python client syntax! For developers integrating AI into their codebases, this means you can switch to open-source models with just three lines of code. Here's a quick example of how easy it is.
Why use the InferenceClient?
🔄 Seamless transition: keep your existing code structure while leveraging LLMs hosted on the Hugging Face Hub.
🤗 Direct integration: easily launch a model to run inference using our Inference Endpoint service.
🚀 Stay Updated: always be in sync with the latest Text-Generation-Inference (TGI) updates.
More details in https://huggingface.co/docs/huggingface_hub/main/en/guides/inference#openai-compatibility
Why use the InferenceClient?
🔄 Seamless transition: keep your existing code structure while leveraging LLMs hosted on the Hugging Face Hub.
🤗 Direct integration: easily launch a model to run inference using our Inference Endpoint service.
🚀 Stay Updated: always be in sync with the latest Text-Generation-Inference (TGI) updates.
More details in https://huggingface.co/docs/huggingface_hub/main/en/guides/inference#openai-compatibility
-
- 3 replies
Post
1836
🚀 Just released version 0.23.0 of the huggingface_hub Python library!
Exciting updates include:
📁 Seamless download to local dir!
💡 Grammar and Tools in InferenceClient!
🌐 Documentation full translated to Korean!
👥 User API: get likes, upvotes, nb of repos, etc.!
🧩 Better model cards and encoding for ModelHubMixin!
Check out the full release notes for more details:
Wauplin/huggingface_hub#6
👀
Exciting updates include:
📁 Seamless download to local dir!
💡 Grammar and Tools in InferenceClient!
🌐 Documentation full translated to Korean!
👥 User API: get likes, upvotes, nb of repos, etc.!
🧩 Better model cards and encoding for ModelHubMixin!
Check out the full release notes for more details:
Wauplin/huggingface_hub#6
👀