Today at Microsoft Build we are happy to announce a broad set of new features and collaborations as Microsoft and Hugging Face deepen their strategic collaboration to make open models and open source AI easier to use everywhere. Together,    

Join the next Hugging Cast on June 6th to ask questions to the post authors, watch a live demo deploying Llama 3 on MI300X on Azure, plus a bonus demo deploying models locally on Ryzen AI PC! Register at https://streamyard.com/watch/iMZUvJnmz8BV Introduction At Hugging Face we want to make it easy to build AI with open models and open source, whichever framework, cloud and stack you want to use. A key component is    

Today we announce the Dell Enterprise Hub, a new experience on Hugging Face to easily train and deploy open models on-premise using Dell platforms. Try it out at dell.huggingface.co Enterprises need to build AI with open models When building AI systems, open models is the best solution to meet security, compliance and privacy requirements of enterprises: Building upon open models allows companies to understand, own    

Hugging Face Spaces makes it easy for you to create and deploy AI-powered demos in minutes. Over 500,000 Spaces have been created by the Hugging Face community and it keeps growing! As part of Hugging Face Spaces, we recently released support for “Dev Mode”, to make your experience of building Spaces even more seamless. Spaces Dev Mode lets you connect with    

AWS Inferentia2 is the latest AWS machine learning chip available through the Amazon EC2 Inf2 instances on Amazon Web Services. Designed from the ground up for AI workloads, Inf2 instances    

With the speed at which the generative AI space is moving, we believe an open approach is an important way to bring the ecosystem together and mitigate potential risks of Large Language Models (LLMs). Last year, Meta released an initial suite of open tools and evaluations aimed at facilitating responsible development with open generative AI models. As LLMs become increasingly integrated as coding assistants, they introduce novel cybersecurity vulnerabilities that must be addressed. To tackle this challenge, comprehensive benchmarks are […]

The Falcon 2 Models TII is launching a new generation of models, Falcon 2, focused on providing the open-source community with a series of smaller models with enhanced performance and multi-modal support. Our goal is to enable cheaper inference and encourage the development of more downstream applications with improved usability. The first generation of Falcon models, featuring Falcon-40B and Falcon-180B, made a significant contribution to the open-source community, promoting the    

Sentence Transformers is a Python library for using and training embedding models for a wide range of applications, such as retrieval augmented generation, semantic search, semantic textual similarity, paraphrase mining, and more. Its v3.0 update is the largest since the project’s inception, introducing a new training approach. In this blogpost, I’ll show you how to use it to finetune Sentence    

In this blog we will be exploring Text Generation Inference’s (TGI) little brother, the TGI Benchmarking tool. It will help us understand how to profile TGI beyond simple throughput to better understand the tradeoffs to make decisions on how to tune your deployment for your needs. If you have ever felt like LLM deployments cost too much or    

Earlier this week our team detected unauthorized access to our Spaces platform, specifically related to Spaces secrets. As a consequence, we have suspicions that a subset of Spaces’ secrets could have been accessed without authorization. As a first step of remediation, we have revoked a number of HF tokens present in those secrets. Users whose tokens have been revoked already received an email