A slimmed-down training pipeline from Kimina Prover, with core features and full compatibility with verl. We are happy to introduce kimina-prover-rl, an open-source training pipeline for formal theorem proving in Lean 4, based on a structured reasoning-then-generation paradigm inspired by DeepSeek-R1. This training pipelinee is a simplified version of the system we used to train Kimina Prover, preserving the key components of the system and offering full compatibility with the open-source Verl framework. It is released as part of a […]

Academic research involves frequent research discovery: finding papers, code, related models and datasets. This typically means switching between platforms like arXiv, GitHub, and Hugging Face, manually piecing together connections. The Model Context Protocol (MCP) is a standard that allows agentic models to communicate with external tools and data sources. For research discovery, this means AI can    

Custom CUDA kernels give your models a serious performance edge, but building them for the real world can feel daunting. How do you move beyond a simple GPU function to create a robust, scalable system without getting    

TL;DR: It’s easier than ever to generate detailed pictures with state-of-the-art AI models by connecting Claude to Hugging Face Spaces. This article describes how and why, and introduces recently launched models which excel at producing natural images or images that include text. Update October 2025: Following an update to Anthropic’s Connector Directory Policy, you    

Authors: Dhruv Nathawani, Shuoyang Ding US, Vitaly Lavrukhin US, Jane Polak Scowcroft US, Oleksii Kuchaiev US NVIDIA continues releasing permissive datasets in support of the open ecosystem with 6 Million Multilingual Reasoning Dataset. Continuing the success of the recent Nemotron Post-Training Dataset v1 release used in Llama Nemotron Super model, and our Llama Nemotron Post-Training Dataset release earlier this year, we’re excited to release the reasoning dataset translated into five target languages: French, Spanish, German, Italian, and Japanese. The newly […]

ZeroGPU lets anyone spin up powerful Nvidia H200 hardware in Hugging Face Spaces without keeping a GPU locked for idle traffic. It’s efficient, flexible, and ideal for demos but it doesn’t always make full use of everything the GPU and CUDA stack can offer. Generating images or videos can take a significant amount of time. Being able to squeeze out more performance, taking advantage of the H200 hardware, does matter in this case. This is where PyTorch ahead-of-time (AoT) compilation […]

This summer, SandboxAQ released the Structurally Augmented IC50 Repository (SAIR), the largest dataset of co-folded 3D protein-ligand structures paired with experimentally measured IC₅₀ labels, directly linking molecular structure to drug potency and overcoming a longstanding scarcity in training data. This dataset is now available on Hugging Face, and for the first time, researchers have open access to more than 5 million AI‑generated, high‑accuracy protein-ligand 3D structures, each paired with validated empirical binding potency data. SAIR is an open-sourced dataset and […]

Today, Google releases EmbeddingGemma, a state-of-the-art multilingual embedding model perfect for on-device use cases. Designed for speed and efficiency, the model features a compact size of 308M parameters and a 2K context window, unlocking new possibilities for mobile RAG pipelines, agents, and more. EmbeddingGemma is trained to support over 100 languages and is the highest-ranking text-only multilingual embedding model under 500M on the Massive Text Embedding Benchmark (MTEB) at the time of writing. Table of Contents

This blog post introduces mmBERT, a state-of-the-art massively multilingual encoder model trained on 3T+ tokens of text in over 1800 languages. It shows significant performance and speed improvements over previous multilingual models, being the first to improve upon XLM-R, while also developing new strategies for effectively learning low-resource languages. mmBERT builds upon ModernBERT for a blazingly fast architecture, and adds novel components to enable efficient multilingual learning. If you are interested in trying out the models yourself, some example boilerplate […]

The past year has been all about giving LLMs more tools and autonomy to solve more complex and open ended tasks. The goal of the Jupyter Agent is to give the model the ultimate tool: code execution. A natural way to display multi-step code execution together with reasoning is within a Jupyter Notebook, which consists of code and markdown cells. So we built Jupyter Agent to act as an agent that can execute code directly inside a Jupyter notebook and […]