As large language models (LLMs) become increasingly integrated into our lives, it becomes crucial to assess whether they reflect the nuances and capabilities of specific language communities. For example, Filipinos are among the most active ChatGPT users globally, ranking fourth in ChatGPT traffic (behind the United States, India, and Brazil [1] [2]), but despite this strong usage, we lack a clear understanding of how LLMs perform for their languages, such as Tagalog and Cebuano. Most of the existing evidence is […]

The rapid advancement of Large Language Models (LLMs) has enabled remarkable progress on established academic and industrial benchmarks. Knowledge benchmarks, such as MMLU and GPQA, are now largely saturated, and frontier models are making significant progress    

Neural Super Sampling (NSS), a next-generation AI-powered upscaling solution from Arm is released for graphics and gaming developers to start experimenting today!

With Arm’s recent SME2 announcement, the role of Arm KleidiAI is increasingly clear as Arm’s AI accelerator layer powering the next wave of AI. By embedding into widely-used Edge    

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 […]