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,    

It’s been fantastic to see the community dive into our new MiniMax M2, with many highlighting its impressive skills in complex agentic tasks. This is particularly exciting for me, as my work was centered on the agent alignment part of its post-training. In this post, I’d like to share some of the key insights and lessons we learned during that process.

Today, we are happy to announce a new and deeper partnership with Google Cloud, to enable companies to build their own AI with open models. “Google has made some of the most impactful contributions to open AI, from    

Looking to show off your robotics aptitude? The AMD Open Robotics Hackathon hosted by AMD, Hugging Face, and Data Monsters is the place to do it. Whether you’re a student, hobbyist, startup    

We converted our 15B reasoning model to a Mamba hybrid achieving 2.1x throughput with minimal quality loss. The key? A non-obvious insight about what data to distill on, and why intuition fails here. When MiniMax published their M2 post-mortem in October explaining why they abandoned efficient attention at 230B scale, the narrative briefly became “efficient attention is dead.” Within days, Kimi Linear proved otherwise. The real lesson: it depends on your constraints. Our constraint was simple: we had a strong […]