Today, we are announcing that Sentence Transformers is transitioning from Iryna Gurevych’s Ubiquitous Knowledge Processing (UKP) Lab at the TU Darmstadt to Hugging Face. Hugging Face’s Tom Aarsen has already been maintaining the library since late 2023 and will continue to lead the project. At its new home, Sentence Transformers will benefit from Hugging Face’s robust infrastructure, including continuous    

With tools like TRL, TorchForge and verl, the open-source community has shown how to scale AI across complex compute infrastructure. But compute is only one side of the coin. The other side is the developer community; the people and tools that make agentic systems possible. That’s why Meta and Hugging Face are partnering to launch the OpenEnv Hub: a shared and open community hub for agentic environments. Agentic environments define everything an agent needs to perform a task: the tools, […]

We’re thrilled to announce a series of significant advancements across LeRobot, designed to make open-source robot learning more powerful, scalable, and user-friendly than ever before! From revamped datasets to versatile editing tools, new simulation environments, and a groundbreaking plugin system for hardware, LeRobot is continuously evolving to meet the demands of cutting-edge embodied AI. TL;DR LeRobot v0.4.0 delivers a major upgrade for open-source robotics, introducing scalable Datasets v3.0, powerful new VLA models like PI0.5 and GR00T N1.5, and    

TL;DR: After five years of development, huggingface_hub has reached v1.0 – a milestone that marks the library’s maturity as the Python package powering 200,000 dependent libraries and providing core functionality for accessing over 2 million public models, 0.5 million public datasets, and 1 million public Spaces. This release introduces breaking changes designed to support the next decade of open machine learning, driven by a global community of almost 300 contributors and millions of users. 🚀 We highly recommend upgrading to […]

We boosted load_dataset(‘dataset’, streaming=True), streaming datasets without downloading them with one line of code! Start training on multi-TB datasets immediately, without complex setups, downloading, no “disk out of space”, or 429 “stop requesting!” errors.It’s super fast! Outrunning our local SSDs when training on 64xH100 with 256 workers downloading data. We’ve improved streaming to have 100x fewer requests, → 10× faster data resolution → 2x sample/sec, → 0 worker crashes at 256 concurrent workers. Loading data, especially at the terabyte scale, […]

In this blog post, we introduce the idea of a ‘voice consent gate’ to support voice cloning with consent. We provide an example Space and accompanying code to start the ball rolling on the idea.

Today we are excited to share Granite 4.0 Nano, our smallest models yet, released as part of IBM’s Granite 4.0 model family. Designed for the edge and on-device applications, these models demonstrate excellent performance for    

Simulation has been a cornerstone in medical imaging to address the data gap. However, in healthcare robotics until now, it’s often been too slow, siloed, or difficult to translate into real-world systems. That’s now changing. With new advances in GPU-accelerated simulation and digital twins, developers can design, test, and validate robotic workflows entirely in virtual environments – reducing prototyping time from months to days,    

A hands-on guide to collecting data, training policies, and deploying autonomous medical robotics workflows on real hardware Table-of-Contents    

The status quo of AI chip usage, that was once almost entirely U.S.-based, is changing. China’s immense progress in open-weight AI development is now being met with rapid domestic AI chip development. In the past few months,