With the release of the Aya Vision family, our new 8B and 32B parameter vision-language models (VLMs), we are addressing one of the biggest challenges in AI: bringing multilingual performance to multimodal models. Aya Vision is Cohere For AI‘s latest open-weight multilingual and multimodal model family, designed to be a strong foundation for language and vision understanding across 23 languages. It builds on the success of Aya Expanse, state-of-the-art multilingual language models, and extends it using a combination of advanced […]

TL;DR of L2D, the world’s largest self-driving dataset! 90+ TeraBytes of multimodal data (5000+ hours of driving) from 30 cities in Germany 6x surrounding HD cameras and complete vehicle state: Speed/Heading/GPS/IMU Continuous: Gas/Brake/Steering and discrete actions: Gear/Turn Signals    

Over the last few weeks, we have focused our efforts on reproducing the competitive programming (code reasoning) aspects of the DeepSeek-R1 recipe. In this post, we are excited to share: The construction of CodeForces-CoTs: a dataset of nearly 100k high-quality samples distilled from R1 to produce solutions in C++ and Python. The IOI benchmark: a new benchmark of challenging problems from the 2024 International Olympiad in Informatics (IOI). OlympicCoder: two fine-tuned 7B and 32B code models that outperform closed-source frontier […]

Today Google releases Gemma 3, a new iteration of their Gemma family of models. The models range from 1B to 27B parameters, have a context window up to 128k tokens, can accept images and text, and support 140+ languages. Try out Gemma 3 now 👉🏻 Gemma 3 Space Gemma 2 Gemma 3 Size Variants 2B 9B 27B 1B 4B 12B 27B Context Window Length 8k 32k (1B) 128k (4B, 12B, 27B) Multimodality (Images and Text) ❌ ❌ (1B)    

Want to skip the details and get straight to faster uploads and downloads with bigger files than ever before? Click here to read about joining the Xet waitlist (or head over to join immediately). Over the past few weeks, Hugging Face’s Xet Team took a major step forward by migrating the first Model and Dataset repositories off LFS and to Xet storage. This marks one of many steps to fulfill Hugging Face’s vision for the Hub by empowering AI builders […]

On March 14, we submitted Hugging Face’s response to the White House Office of Science and Technology Policy’s request for information on the White House AI Action Plan. We took this opportunity to (re-)assert the fundamental role that open AI systems and open science play in enabling the technology to be more performant and efficient, broadly and reliably adopted, and meeting the highest standards of security. This blog post provides a summary of our response, the full text is available […]

Everyone’s been using Claude and OpenAI as coding assistants for the last few years, but there’s less appeal if you look at the developments coming out of open source projects like Open R1. If we look at the evaluation on LiveCodeBench below, we can see that the 7B parameter variant outperforms Claude 3.7 Sonnet and GPT-4o. These models are the daily drivers of many engineers in applications like Cursor and VSCode. Evals are great and all, but I want to […]

Analytics and metrics are the cornerstone of understanding what’s happening with your deployment. Are your Inference Endpoints overloaded? How many requests are they handling? Having well-visualized, relevant metrics displayed in real-time is crucial for monitoring and debugging. We realized that our analytics dashboard needed a refresh. Since we debug a lot of endpoints ourselves, we’ve felt the same pain as our users. That’s why we sat down to plan and make several improvements to provide a better experience for you. […]

Gradio’s gr.Dataframe component is one of our most popular components, we’ve seen it used in a variety of awesome apps, like leaderboards, dashboards, and interactive visualisations. Although we hadn’t made any changes to the dataframe in quite some time, our backlog    

Sentence Transformers is a Python library for using and training embedding and reranker models for a wide range of applications, such as retrieval augmented generation, semantic search, semantic textual similarity, paraphrase mining, and more. Its v4.0 update introduces a new training approach for rerankers, also known as cross-encoder models, similar to what the v3.0 update introduced for embedding models. In