We’re excited to share our first custom Deep Reinforcement Learning environment: Snowball Fight 1vs1 🎉. Snowball Fight is a game made with Unity ML-Agents, where you shoot snowballs against a Deep Reinforcement Learning agent. The game is hosted on Hugging Face Spaces. 👉 You can play it online here In this post, we’ll    

In this blog post we’ll take a look at what it takes to build the technology behind GitHub CoPilot, an application that provides suggestions to programmers as they code. In this step by step guide, we’ll learn how to train a large GPT-2 model called CodeParrot 🦜, entirely from scratch. CodeParrot can auto-complete your Python code – give    

TLDR We’ve added Perceiver IO to Transformers, the first Transformer-based neural network that works on all kinds of modalities (text, images, audio, video, point clouds,…) and combinations thereof. Take a look at the following Spaces to view some examples: We    

  Gradio is joining Hugging Face! By acquiring Gradio, a machine learning startup, Hugging Face will be able to offer users, developers, and data scientists the tools needed to get to high level results and create better models and tools… Hmm, paragraphs about acquisitions like the one above are so common that an algorithm could write them. In fact, one did!!    

Almost 6 months ago to the day, EleutherAI released GPT-J 6B, an open-source alternative to OpenAIs GPT-3. GPT-J 6B is the 6 billion parameter successor to EleutherAIs GPT-NEO family, a family of transformer-based language models based on the GPT architecture for text generation. EleutherAI‘s primary goal is to train a model that    

Wav2Vec2 is a popular pre-trained model for speech recognition. Released in September 2020 by Meta AI Research, the novel architecture catalyzed progress in self-supervised pretraining for speech recognition, e.g. G. Ng et al., 2021, Chen et al, 2021, Hsu et al., 2021 and

December 2022 Update: Infinity is no longer offered by Hugging Face as a commercial inference solution. To deploy and accelerate your models, we recommend the following new solutions: Introduction Transfer learning has changed Machine Learning by reaching new levels of accuracy from Natural Language Processing (NLP) to Audio and Computer Vision tasks. At Hugging Face, we work hard to make these new complex models and large checkpoints as easily accessible and usable as possible. But    

At Hugging Face, we are contributing to the ecosystem for Deep Reinforcement Learning researchers and enthusiasts. That’s why we’re happy to announce that we integrated Stable-Baselines3 to the Hugging Face Hub. Stable-Baselines3 is one of the most popular PyTorch Deep Reinforcement Learning library that makes it easy to train and test your agents in a variety of environments    

The huggingface_hub library is a lightweight interface that provides a programmatic approach to exploring the hosting endpoints Hugging Face provides: models, datasets, and Spaces. Up until now, searching on the Hub through this interface was tricky to pull off, and there were many aspects of it a user had to “just know” and get