We are delighted to introduce Hugging Face’s new blog for Chinese speakers: hf.co/blog/zh! A committed group of volunteers has made this possible by translating our invaluable resources, including blog posts and comprehensive courses on transformers, diffusion, and reinforcement learning. This step aims to make our content accessible to the ever-growing Chinese AI community, fostering mutual learning and collaboration. Recognizing the Chinese AI Community’s Accomplishments We want to highlight the remarkable achievements and contributions of the Chinese AI community, which has […]

Generative AI has been taking the world by storm. As the data and AI company, we have been on this journey with the release of the open source large language model Dolly, as well as the internally crowdsourced    

TL;DR: We show how to run one of the most powerful open-source text to image models IF on a free-tier Google Colab with 🧨 diffusers. You can also explore the capabilities of the model directly in the Hugging Face Space. Image compressed from official IF GitHub repo. Introduction IF is a pixel-based text-to-image generation model and was released in late    

The Hugging Face Unity API is an easy-to-use integration of the Hugging Face Inference API, allowing developers to access and use Hugging Face AI models in their Unity projects. In this blog post, we’ll walk through the steps to install and use the Hugging Face Unity API.

StarCoder and StarCoderBase are Large Language Models for Code (Code LLMs) trained on permissively licensed data from GitHub, including from 80+ programming languages, Git commits, GitHub issues, and Jupyter notebooks. Similar to LLaMA, we trained    

Video samples generated with ModelScope. Text-to-video is next in line in the long list of incredible advances in generative models. As self-descriptive as it is, text-to-video is a fairly new computer vision task that involves generating a sequence of images from text descriptions that are both temporally and spatially consistent. While this task might seem    

Large language models are all the rage these days, with many companies investing significant resources to scale them up and unlock new capabilities. However, as humans with ever-decreasing attention spans, we also dislike their slow response times. Latency is critical for a good user experience, and smaller models are often used despite their lower quality (e.g. in code completion). Why    

ChatGPT and chatbot-powered applications have captured significant attention in the Natural Language Processing (NLP) domain. The community is constantly seeking strong, reliable and open-source models for their applications and use cases. The rise of these powerful models stems from the democratization and widespread adoption of transformer-based models, first introduced by Vaswani et al. in 2017. These models significantly outperformed previous SoTA NLP models based on Recurrent Neural Networks (RNNs), which were considered dead after that paper. Through this blogpost, we […]