With over 200 merged PRs and over 50 new contributors since v0.4.0, LeRobot v0.5.0 is our biggest release yet — expanding in every direction at once. More robots (including our first humanoid), more policies (including the comeback of autoregressive VLAs), faster datasets, simulation environments you can load straight from the Hub, and a modernized codebase running on Python 3.12 and Transformers v5. Whether you’re training policies in simulation or deploying them on real hardware, v0.5.0 has something for you. TL;DR […]

Training large language models on long sequences has become essential for building capable AI systems. As models are increasingly used for tasks like document analysis, code understanding, complex reasoning, and RAG workloads, the need to process sequences of hundreds    

We’re excited to share Granite 4.0 1B Speech, the latest addition to IBM’s Granite Speech collection. Designed for enterprise applications on resource-constrained devices, Granite 4.0 1B Speech is a compact speech-language model built for    

TL;DR — For those of you who don’t have time to read 5,000 words about async RL plumbing (we get it, you have models to train): The problem: In synchronous RL (reinforcement learning) training, data generation (model inference to create data samples) dominates wall-clock time — a single batch of 32K-token rollouts on a 32B (32-billion parameter) model can take hours, while the GPUs used for training remain idle. The solution everyone converged on: Disaggregate (separate) inference and training onto […]

The world of data is vast, but quantitative information is often sparse or unavailable in text form online, presenting a significant challenge for deep research agents. This post shares an architecture, NVIDIA KGMON (NeMo Agent Toolkit) Data Explorer, for building autonomous data analysis agents, developed by the NVIDIA Kaggle Grandmasters (KGMON) LLM Agent Research Team. The NVIDIA KGMON (NeMo Agent Toolkit) Data Explorer project introduces an agent specialized for dataset exploration and analysis, designed to handle the complexities of multi-step […]

Hugging Face Models and Datasets repos are great for publishing final artifacts. But production ML generates a constant stream of intermediate files (checkpoints, optimizer states, processed shards, logs, traces, etc.) that change often, arrive from many jobs at once, and rarely need version control. Storage Buckets are built exactly for this: mutable, S3-like object storage you can browse on the Hub, script from Python, or manage with the hf CLI. And because they are backed by Xet, they are especially […]