We’re excited to introduce a new tool we created: ⚔️ AI vs. AI ⚔️, a deep reinforcement learning multi-agents competition system. This tool, hosted on Spaces, allows us to create multi-agent competitions.    

Welcome to AI for Game Development! In this series, we’ll be using AI tools to create a fully functional farming game in just 5 days. By the end of this series, you will have learned how you can incorporate a variety of AI tools into your game development workflow. I will show you how you can use AI tools for: Art Style    

We’re happy to announce that SpeechT5 is now available in 🤗 Transformers, an open-source library that offers easy-to-use implementations of state-of-the-art machine learning models. SpeechT5 was originally described in the paper SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing by Microsoft Research Asia. The official checkpoints published by the paper’s authors are available on the Hugging Face Hub.    

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 […]