June 2023 Update: The Private Hub is now called Enterprise Hub. The Enterprise Hub is a hosted solution that combines the best of Cloud Managed services (SaaS) and Enterprise security. It lets customers deploy specific services like Inference Endpoints on a wide scope of compute options, from on-cloud to on-prem. It offers advanced user administration and access controls through    

⚠️ A new updated version of this article is available here 👉 https://huggingface.co/deep-rl-course/unit1/introduction This article is part of the Deep Reinforcement Learning Class. A free course from beginner to expert. Check the syllabus here. ⚠️ A new updated version of this article is available here 👉 https://huggingface.co/deep-rl-course/unit1/introduction This article is part of the Deep Reinforcement Learning    

This guide is only suited for Sentence Transformers before v3.0. Read Training and Finetuning Embedding Models with Sentence Transformers v3 for an updated guide. Check out this tutorial with the Notebook Companion: Training or fine-tuning a Sentence Transformers model highly depends on the available data and the target task. The    

Introduction Despite increasing competition from PyTorch and JAX, TensorFlow remains the most-used deep learning framework. It also differs from those other two libraries in some very important ways. In particular, it’s quite tightly integrated with its high-level API Keras,    

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