Large Language Models (LLMs) based on the transformer architecture, like GPT, T5, and BERT have achieved state-of-the-art results in various Natural Language Processing (NLP) tasks. They have also started foraying into other domains, such as Computer Vision (CV) (VIT,    

Hugging Face recently launched Inference Endpoints; which as they put it: solves transformers in production. Inference Endpoints is a managed service that allows you to: Deploy (almost) any model on Hugging Face Hub To any cloud (AWS, and Azure, GCP on the way) On a range of instance types (including GPU) We’re switching some of our Machine Learning (ML) models that    

This guide introduces BLIP-2 from Salesforce Research that enables a suite of state-of-the-art visual-language models that are now available in 🤗 Transformers. We’ll show you how to use it for image captioning, prompted image captioning, visual question-answering, and chat-based    

It’s time to make AI open and accessible to all. That’s the goal of this expanded long-term strategic partnership between Hugging Face and Amazon Web Services (AWS). Together, the two leaders aim to accelerate the availability of next-generation machine learning models by making them more accessible to the machine learning community and helping developers achieve the highest performance at the lowest cost. A new generation of open, accessible AI Machine learning is quickly becoming embedded in all applications.    

If you need support in using Hugging Face and AWS, please get in touch with us here – our team will contact you to discuss your requirements! Executive Summary Fetch, a consumer rewards company, developed about 15 different AI tools to help    

Transform your text into stunning images with ease using Diffusers for Mac, a native app powered by state-of-the-art diffusion models. It leverages a bouquet of SoTA Text-to-Image models contributed by the community to the Hugging Face Hub, and converted    

Warning: This article is about red-teaming and as such contains examples of model generation that may be offensive or upsetting. Large language models (LLMs) trained on an enormous amount of text data are very good at generating realistic text. However, these models often exhibit undesirable behaviors like revealing personal information (such as social security numbers) and generating misinformation, bias, hatefulness, or toxic content. For example, earlier versions of GPT3 were known to exhibit sexist behaviors (see below) and biases against […]

If you’re interested in building ML solutions faster, visit the Expert Acceleration Program landing page and contact us here! Business Context As IT continues to evolve and reshape our world, creating a more diverse and inclusive environment within the industry is imperative. Witty Works was built in 2018 to address this challenge. Starting as a consulting company advising organizations on becoming more diverse, Witty Works first helped them write job ads using inclusive language.    

We are on a journey to make our libraries more responsible, one commit at a time! As part of the Diffusers library documentation, we are proud to announce the publication of an ethical framework. Given diffusion models’ real case applications in the world and potential negative impacts on society, this initiative aims to guide the technical decisions of the    

Ever since Stable Diffusion took the world by storm, people have been looking for ways to have more control over the results of the generation process. ControlNet provides a minimal interface allowing users to customize the generation process up to a great extent. With ControlNet, users can easily condition the generation with different spatial contexts such as a depth map, a segmentation map, a scribble, keypoints, and so on! We can turn a cartoon drawing into a realistic photo with […]