The pace of AI development today is breathtaking. Every single day, hundreds of new models appear on the Hugging Face Hub, some are specialized variants of popular base models like Llama or Qwen, others feature novel architectures or have been trained from scratch for specific domains. Whether it’s a medical AI trained on clinical data, a coding assistant optimized for a particular programming language, or a multilingual model fine-tuned for specific cultural contexts, the Hugging Face Hub has become the […]

OpenAI recently released their GPT-OSS series of models. The models feature some novel techniques like MXFP4 quantization, efficient kernels, a brand new chat format, and more. To enable the release of gpt-oss through transformers, we have upgraded the library considerably. The updates make it very efficient to load, run, and fine-tune the models. In this blog post, we talk about all the upgrades in-depth, and how they become part of the transformers toolkit so other models (current and future) can […]

The team at WRITER is thrilled to announce the release of three new open models in the Palmyra-mini family. These models are designed to be powerful, lightweight, and highly performant for their size (1.5B to 1.7B),    

Last year, we shared a blogpost on watermarking, explaining what it means to watermark generative AI content, and why it’s important. The need for watermarking has become even more critical as people all over the world have begun to generate and share AI-generated images, video, audio, and text. Images and video have become so realistic    

TL;DR Today we release LeRobotDataset:v3! In our previous LeRobotDataset:v2 release, we stored one episode per file, hitting file-system limitations when scaling datasets to millions of episodes. LeRobotDataset:v3 packs multiple episodes in a single file, using relational metadata to retrieve information at the individual episode level from multi-episode files. The new format also natively supports accessing datasets in streaming mode, allowing to process large datasets on the fly.We provide a one-liner util to convert all datasets in the LeRobotDataset format to […]

We’re thrilled to share that Public AI is now a supported Inference Provider on the Hugging Face Hub! Public AI joins our growing ecosystem, enhancing the breadth and capabilities of serverless inference directly on the Hub’s model pages. Inference Providers are also seamlessly integrated into our client SDKs (for both JS and Python), making it super easy to use a wide variety of models with your preferred providers. This launch makes it easier than ever to access public and sovereign […]

Building trust in the open model ecosystem through standardized risk assessment More than 500,000 models can be found on the Hugging Face hub, but it’s not always clear to users how to choose the best model for them, notably on the security aspects. Developers might find a model that perfectly fits their use case, but have no systematic way to evaluate its    

We’re thrilled to share that Scaleway is now a supported Inference Provider on the Hugging Face Hub! Scaleway joins our growing ecosystem, enhancing the breadth and capabilities of serverless inference directly on the Hub’s model pages. Inference Providers are also seamlessly integrated into our client SDKs (for both JS and Python), making it super easy to use a wide variety of models with your preferred providers. This launch makes it easier than ever to access popular open-weight models like gpt-oss, […]

In an ideal world, AI agents would be reliable assistants. When given a query, they would easily manage ambiguity in instructions, construct step-by-step plans, correctly identify necessary resources, execute those plans without getting sidetracked, and adapt to unexpected events, all while maintaining accuracy and avoiding hallucinations. However, developing agents and testing these behaviors is no small feat: if you have ever tried to debug your own agent, you’ve probably observed how tedious and frustrating this can be. Existing evaluation environments […]

When we think about building a model - be it a Large Language Model (LLM) or a Small Language Model (SLM) - the first thing we need is data. While a vast amount of open data is available, it rarely comes in the exact format required to train or align models. In practice, we often face scenarios where the raw data isn’t enough. We need data that is more structured, domain-specific, complex, or aligned with the task at hand. Let’s look at some common […]