At Hugging Face, we are working on tackling various problems in open-source machine learning, including, hosting    

Introduction Language models are becoming larger all the time. At the time of    

This blog post will show how easy it is to fine-tune pre-trained Transformer models for your dataset using the Hugging Face Optimum library on Graphcore Intelligence Processing Units (IPUs). As an example, we will show a step-by-step guide and provide a notebook that takes a large, widely-used chest X-ray dataset and trains a vision transformer (ViT) model. Introducing vision transformer (ViT)    

In the previous posts, we showed how to deploy a Vision Transformers (ViT) model from 🤗 Transformers locally and on a Kubernetes cluster. This post will show you    

In this Tutorial, you will learn how to pre-train BERT-base from scratch using a Habana Gaudi-based DL1 instance on AWS to take advantage of the cost-performance benefits of Gaudi. We will use the Hugging Face Transformers, Optimum Habana and Datasets libraries to pre-train a BERT-base model using masked-language modeling, one of the two original BERT pre-training    

Stable Diffusion 🎨 …using 🧨 Diffusers Stable Diffusion is a text-to-image latent diffusion model created by the researchers and engineers from CompVis, Stability AI and LAION. It is trained on 512×512 images from a subset of the LAION-5B database. LAION-5B is the largest, freely accessible multi-modal dataset that currently exists. In this post, we want to show how to use Stable Diffusion with the 🧨 Diffusers library, explain how the model works and finally dive a bit deeper into how […]

Open & Responsible AI licenses (“OpenRAIL”) are AI-specific licenses enabling open access, use and distribution of AI artifacts while requiring a responsible use of the latter. OpenRAIL licenses could be for open and responsible ML what current open software licenses are to code and Creative Commons to general content: a widespread community licensing tool. Advances in machine learning and other    

Training large language models in Pytorch requires more than a simple training loop. It is usually distributed across multiple devices, with many optimization techniques for a stable and efficient training. Hugging Face 🤗 Accelerate library was created to support distributed training across GPUs and TPUs with very easy integration into the training loops. 🤗 Transformers also support distributed    

In a previous post, we announced the launch of Decision Transformers in the transformers library. This new technique of using a Transformer as a Decision-making model is getting increasingly popular. So today, you’ll learn to train your first