In our mission to democratize good machine learning (ML), we examine how supporting ML community work also empowers examining and preventing possible harms. Open development and science decentralizes power so that many people can collectively work on AI that reflects their needs and values. While openness enables broader perspectives to contribute to research and AI overall, it faces the tension of less risk control. Moderating ML artifacts presents unique challenges due to the dynamic and rapidly evolving nature of these […]

Models such as ChatGPT, GPT-4, and Claude are powerful language models that have been fine-tuned using a method called Reinforcement Learning from Human Feedback (RLHF) to be better aligned with how we expect them to behave and would like to use them. In this blog post, we show all the steps involved in training a LlaMa model to answer questions on Stack Exchange with RLHF through a combination of: Supervised Fine-tuning (SFT) Reward / preference modeling (RM) Reinforcement Learning from […]

This article is a cross-post from an originally published post on April 6, 2023 in Snorkel’s blog, by Friea Berg . As OpenAI releases GPT-4 and Google debuts Bard in beta, enterprises around the world are excited to leverage the power of foundation models. As that excitement builds, so does the realization that most companies and organizations are    

With the recent rise of generative techniques, machine learning is at an incredibly exciting point in its history. The models powering this rise require even more data to produce impactful results, and thus it’s becoming increasingly important to explore new methods of ethically gathering data while ensuring that data privacy and security remain a top priority. In many domains that deal with sensitive information, such as healthcare, there often isn’t enough high quality data accessible to train these data-hungry models. […]

In the previous blog, we explored some of the theoretical aspects of machine learning on graphs. This one will explore how you can do graph classification using the Transformers library. (You can also follow along by downloading the demo notebook here!) At the moment, the only graph transformer model available in Transformers is Microsoft’s Graphormer, so this    

In the last five years, Transformer models [1] have become the de facto standard for many machine learning (ML) tasks, such as natural language processing (NLP), computer vision (CV), speech, and more. Today, many data scientists and    

Did you know you can host a Unity game in a Hugging Face Space? No? Well, you can! Hugging Face Spaces are an easy way to build, host, and share demos. While they are typically used for Machine Learning demos, they can also host playable Unity games. Here are some examples: Here’s how you can host your own Unity game in a    

We are delighted to introduce Hugging Face’s new blog for Chinese speakers: hf.co/blog/zh! A committed group of volunteers has made this possible by translating our invaluable resources, including blog posts and comprehensive courses on transformers, diffusion, and reinforcement learning. This step aims to make our content accessible to the ever-growing Chinese AI community, fostering mutual learning and collaboration. Recognizing the Chinese AI Community’s Accomplishments We want to highlight the remarkable achievements and contributions of the Chinese AI community, which has […]