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    

ggml is a machine learning (ML) library written in C and C++ with a focus on Transformer inference. The project is open-source and is being actively developed by a growing community. ggml is similar to ML libraries such as PyTorch and TensorFlow, though it is still in its early stages of development and some of its fundamentals are still changing rapidly. Over time, ggml has gained popularity alongside other projects like llama.cpp and whisper.cpp. Many other projects also use ggml […]

TLDR: Infini-attention’s performance gets worse as we increase the number of times we compress the memory, and to the best of our knowledge, ring attention, YaRN and rope scaling are still the best ways for extending a pretrained model to longer context length. Section 0: Introduction The context length of language models is one of the central attributes besides the model’s performance. Since the emergence of in-context learning, adding relevant information to    

Meta Llama 3.1 is the latest open LLM from Meta, released in July 2024. Meta 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 distillation; among other use cases. Some of its key features include: a large context length of 128K tokens (vs original 8K), multilingual capabilities, tool usage capabilities, and a more permissive license. In this blog […]

tl;dr The Hugging Face Hub has a number of tools and integrations that are often overlooked that can make it easier to build many types of AI solutions The Hugging Face Hub boasts over 850K public models, with ~50k new ones added every month, and that just seems to be climbing higher and higher. We also offer an Enterprise Hub subscription