We are thrilled to announce the release of our latest collaborative features: pull requests and discussions on the Hugging Face Hub! Pull requests and discussions are available today under the community tab for all repository types: models, datasets, and Spaces. Any member of the community can create and participate in    

In this blog post, we’ll take a deeper look into Denoising Diffusion Probabilistic Models (also known as DDPMs, diffusion models, score-based generative models or simply autoencoders) as researchers have    

There are currently three ways to convert your Hugging Face Transformers models to ONNX. In this section, you will learn how to export distilbert-base-uncased-finetuned-sst-2-english for text-classification using all three methods going from the low-level torch API to the most user-friendly high-level API of optimum. Each method will do exactly the same Export with torch.onnx (low-level) torch.onnx enables you to convert model checkpoints to an ONNX graph by the export method. But you have to provide    

Check out this tutorial with the Notebook Companion: Understanding embeddings An embedding is a numerical representation of a piece of information, for example, text, documents, images, audio, etc. The representation captures the semantic    

TL;DR: Today we introduce Evaluation on the Hub, a new tool powered by AutoTrain that lets you evaluate any model on any dataset on the Hub without writing a single line of code! Evaluate all the models 🔥🔥🔥! Progress in AI has been nothing short of amazing, to the point where some people are now seriously debating whether AI models may be better than humans at certain tasks. However, that progress has not at all been even: to a machine […]

In this post we will look at how we can leverage the Accelerate library for training large models which enables users to leverage the ZeRO features of DeeSpeed.