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    

Welcome DeepSeek-V3 0324 This week, a new model from DeepSeek silently landed on the Hub. It’s an updated version of DeepSeek-V3, the base model underlying the R1 reasoning model. There isn’t much information shared yet on this new model, but we do know a few things! What we know so far    

We’re excited to announce the native integration of Intel Gaudi hardware support directly into Text Generation Inference (TGI), our production-ready serving solution for Large Language Models (LLMs). This integration brings the power of Intel’s specialized AI accelerators to our high-performance inference stack, enabling more deployment options for the open-source AI community 🎉 ✨ What’s New? We’ve fully integrated Gaudi support into TGI’s main codebase in PR #3091. Previously, we maintained a separate fork for Gaudi devices    

Hugging Face has become synonymous with advancing AI at scale. With over 4 million builders deploying models on the Hub, the rapid growth of the platform necessitated a rethinking of how sensitive configuration data —secrets— are managed. Last year, the engineering teams set out to improve the handling of their secrets and credentials. After evaluating tools like HashiCorp Vault, they ultimately chose    

Serving LLMs to many applications and users in parallel is challenging because they compete for limited GPU resources. This article is the first in a series on LLM performance, based on our experience with serving self-hosted LLMs at TNG Technology Consulting GmbH. In the first part, we focus on the impact of queuing and discuss different scheduling strategies.

Education has always been at the heart of Hugging Face’s mission to democratize AI and we’re doubling down on that by giving hf.co/learn a big upgrade! Our NLP course has been a go-to resource for the open-source AI community for the past 3 years, and it’s now time for a refresh. We’re updating and expanding it to keep up with all the exciting stuff happening in AI (which is not easy when there are breakthroughs every week!) We felt the […]

5 years ago, we launched Gradio as a simple Python library to let researchers at Stanford easily demo computer vision models with a web interface. Today, Gradio is used by >1 million developers each month to build and share AI web apps. This includes some of the most popular open-source projects of all time, like Automatic1111,

We are incredibly excited to welcome the next generation of large language models from Meta to the Hugging Face Hub: Llama 4 Maverick (~400B) and Llama 4 Scout (~109B)! 🤗 Both are Mixture of Experts (MoE) models with 17B active parameters. Released today, these powerful, natively multimodal models represent a significant leap forward. We’ve worked closely with Meta to ensure seamless integration into the Hugging Face ecosystem, including both transformers and TGI from day one. This is just the start […]