Whether language models, large language models, or foundation models, transformers require significant computation for pre-training, fine-tuning, and inference. To help developers and organizations get the most performance bang for their infrastructure bucks, Hugging Face has long been working with hardware companies to leverage acceleration features present on their respective chips. Today, we’re happy to announce that AMD has officially    

The Elixir community has been making great strides towards Machine Learning and Hugging Face is playing an important role on making it possible. To showcase what you can already achieve with Elixir and Machine Learning today, we use Livebook to build a Whisper-based chat app and then deploy it to Hugging Face Spaces. All under 15 minutes, check    

WWDC’23 (Apple Worldwide Developers Conference) was held last week. A lot of the news focused on the Vision Pro announcement during the keynote, but there’s much more to it. Like every year, WWDC week is packed with more than 200 technical sessions that dive deep inside the upcoming features across Apple operating systems and frameworks. This year we are particularly excited about    

A few months ago, we introduced the Informer model (Zhou, Haoyi, et al., 2021), which is a Time Series Transformer that won the AAAI 2021 best paper award. We also provided an example for multivariate probabilistic forecasting with Informer. In this post, we discuss the question: Are Transformers Effective for Time Series Forecasting? (AAAI 2023). As we will see, they are. Firstly, we will provide empirical evidence that Transformers are indeed Effective for Time Series Forecasting. Our comparison shows that […]

New (06/2023): This blog post is strongly inspired by “Fine-tuning XLS-R on Multi-Lingual ASR” and can be seen as an improved version of it. Wav2Vec2 is a pretrained model for Automatic Speech Recognition (ASR) and was released in September 2020 by Alexei Baevski, Michael Auli, and Alex Conneau. Soon    

On June 12th, Hugging Face submitted a response to the US Department of Commerce NTIA    

We are thrilled to announce the collaboration between Panel and Hugging Face! 🎉 We have integrated a Panel template in Hugging Face Spaces to help you get started building Panel apps and deploy them on Hugging Face effortlessly.

Recently an interesting discussion arose on Twitter following the release of Falcon 🦅 and its addition to the Open LLM Leaderboard, a public leaderboard comparing open access large language models. The discussion centered around one of the four evaluations displayed on the leaderboard: a benchmark for measuring Massive Multitask Language Understanding (shortname: MMLU). The community was surprised that MMLU evaluation numbers of the current top model on the leaderboard, the LLaMA model 🦙, were significantly lower than the numbers in […]