We’re very excited to announce that Hugging Face has collaborated with arXiv to make papers more    

Enterprises are full of documents containing knowledge that isn’t accessible by digital workflows. These documents can vary from letters, invoices, forms, reports, to receipts. With the improvements in text, vision, and multimodal AI, it’s now possible to unlock that information. This post shows you how your teams can use open-source models to build custom solutions for free! Document AI includes many data science tasks from image classification, image to text, document question answering, table question answering, and visual question answering. […]

If you’re interested in building ML solutions faster visit: hf.co/support today! 👋 Welcome back to our Director of ML Insights Series! If you missed earlier Editions you can find them here: 🚀 In this fourth installment, you’ll hear what the following top Machine Learning Directors say about Machine Learning’s impact on their respective industries: Javier Mansilla, Shaun Gittens, Samuel Franklin, and Evan Castle.    

Want to help build the future at — if we may say so ourselves — one of the coolest places in AI? Today we’re announcing our internship program for 2023. Together with your Hugging Face mentor(s), we’ll be working on cutting    

Vector Quantized Diffusion (VQ-Diffusion) is a conditional latent diffusion model developed by the University of Science and Technology of China and Microsoft. Unlike most commonly studied diffusion models, VQ-Diffusion’s noising and denoising processes operate on a quantized latent space, i.e., the latent space is composed of a discrete set of vectors. Discrete diffusion models are less explored than their continuous counterparts and    

Time series forecasting is an essential scientific and business problem and as such has also seen a lot of innovation recently with the use of deep learning based models    

Thanks to Apple engineers, you can now run Stable Diffusion on Apple Silicon using Core ML! This Apple repo provides conversion scripts and inference code based on 🧨 Diffusers, and we love it! To make it as easy as possible for you, we converted the weights ourselves and put the Core ML versions of the models in

I have two audiences in mind while writing this. One is biologists who are trying to get into machine learning, and the other is machine learners who are trying to get into biology. If you’re not familiar with either biology or machine learning then you’re still welcome to come along, but you might find it a bit confusing at times! And if