WWDC’ 24 is the moment Apple officially unveiled Apple Intelligence and reiterated their commitment to efficient, private, and on-device AI. During the keynote and the sessions that followed, they demonstrated Apple Intelligence, which powers a huge array of AI-enhanced features that show practical uses for everyday tasks. These are not *AI-for-the-sake-of-AI* shiny demos. These are time-saving, appropriate (and fun!) helpers that are deeply integrated with apps and the OS, that also offer developers a number of ways to include these […]

Llama 3.1 is out! Today we welcome the next iteration of the Llama family to Hugging Face. We are excited to collaborate with Meta to ensure the best integration in the Hugging Face ecosystem. Eight open-weight models (3 base models and 5 fine-tuned ones) are available on the Hub. Llama 3.1 comes in three sizes: 8B for efficient deployment and development on consumer-size GPU, 70B for large-scale AI native applications, and 405B for synthetic data, LLM as a Judge or […]

While developing Docmatix, we noticed that fine-tuning Florence-2 on it yielded great performance on DocVQA, but resulted in low scores on the benchmark. To enhance performance, we had to fine-tune the model further on DocVQA to learn the syntax    

Update: This service is deprecated and no longer available as of April 10th, 2025. For an alternative, you should consider Inference Providers Today, we are thrilled to announce the launch of Hugging Face NVIDIA NIM API    

Over the past few months, we have seen an emergence in the use of Transformer-based diffusion backbones for high-resolution text-to-image (T2I) generation. These models use the transformer architecture as the building block for the diffusion process, instead of the    

One month after the release of Gemma 2, Google has expanded their set of Gemma models to include the following new additions: Gemma 2 2B – The 2.6B parameter version of Gemma 2, making it a great candidate for on-device use. ShieldGemma – A series of safety classifiers, trained on top of Gemma 2, for developers to filter inputs and outputs of their applications. Gemma Scope – A comprehensive, open suite of sparse autoencoders for Gemma 2 2B and 9B. […]

In this blog post, we provide a tutorial on how to use a new data augmentation technique for document images, developed in collaboration with Albumentations AI. Motivation Vision Language Models (VLMs) have an immense range of applications, but they often need to be fine-tuned to specific use-cases, particularly for datasets containing document images, i.e., images with high textual content. In these cases, it is crucial for text and image to interact with each other at all stages of    

Security is a top priority at Hugging Face, and we’re committed to continually enhancing our defenses to safeguard our users. In our ongoing security efforts, we have developed a range of security features designed to empower users to protect themselves and their assets. In this blog post, we’ll take a look at our current security landscape as of August 6th, 2024, and    

We are super excited to officially announce that Hugging Face acquired XetHub 🔥 XetHub is a Seattle-based company founded by Yucheng Low, Ajit Banerjee, Rajat Arya who previously worked at Apple where they built and scaled Apple’s internal ML infrastructure.    

There is now a unified tool use API across several popular families of models. This API means the same code is portable – few or no model-specific changes are needed to use tools in chats with Mistral, Cohere, NousResearch or Llama models. In addition, Transformers now includes helper functionality to make tool calling even easier, as well as