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    

The Elixir community is glad to announce the arrival of several Neural Networks models, from GPT2 to Stable Diffusion, to Elixir. This is possible thanks to the just announced Bumblebee library, which is an implementation of Hugging Face Transformers in pure Elixir. To help anyone get started with those models, the team behind Livebook – a    

This article has been translated to Chinese 简体中文 and Vietnamese đọc tiếng việt. Language models have shown impressive capabilities in the past few years by generating diverse and compelling text from human input prompts. However, what makes a “good” text is inherently hard to define as it is subjective and context dependent. There are many applications such as writing stories where you want creativity, pieces of informative text which should be truthful, or code snippets that we want to be […]

🤗 Datasets is an open-source library for downloading and preparing datasets from all domains. Its minimalistic API allows users to download and prepare datasets in just one line of Python code, with a suite of functions that enable efficient pre-processing. The number of datasets available is unparalleled, with all the most popular machine learning    

Model cards are an important documentation framework for understanding, sharing, and improving machine learning models. When done    

This guide shows how you can use CLIPSeg, a zero-shot image segmentation model, using 🤗 transformers. CLIPSeg creates rough segmentation masks that can be used for robot perception, image