Self-speculative decoding, proposed in LayerSkip: Enabling Early Exit Inference and Self-Speculative Decoding is a novel approach to text generation. It combines the strengths of speculative decoding with early exiting from a large language model (LLM). This method allows for efficient generation by using the same model’s early layers for drafting tokens, and later layers for verification. This technique not only speeds up text generation, but it also achieves significant memory savings and reduces computational latency. In order to obtain an […]

Hugging Face stores over 30 PB of models, datasets, and spaces in Git LFS repositories. Because Git stores and versions at the file level, any change to a file requires re-uploading the full asset –    

Current static evaluations and user-driven arenas have exhibited their limitations and biases in the previous year. Here, we explore a novel way to evaluate LLMs: debate. Debate is an excellent way to showcase reasoning strength and language abilities, used all across history, from the debates in the Athenian Ecclesia in the 5th century BCE to today’s World Universities Debating Championship. Do today’s large language models exhibit debate skills similar to humans? Which model is currently the best at debating? What […]

As part of Hugging Face’s Xet team’s work to improve Hugging Face Hub’s storage backend, we analyzed a 24 hour window of Hugging Face upload requests to better understand access patterns. On October 11th, 2024, we saw: Uploads from 88 countries 8.2 million upload requests 130.8 TB of data transferred The map below visualizes this activity, with countries colored by bytes uploaded per hour. Currently, uploads are stored in an S3 bucket in us-east-1 and optimized using S3 Transfer Acceleration. […]

Not legal advice. The EU AI Act, the world’s first comprehensive legislation on artificial intelligence, has officially come into force, and it’s set to impact the way we develop and use AI – including in the open source community. If you’re an open source developer navigating this new landscape, you’re probably wondering what this means for your projects. This guide breaks down key points of the regulation with a focus on open source development, offering a clear introduction to this […]

Overview: This article presents a deep dive into Capital Fund Management’s (CFM) use of open-source large language models (LLMs) and the Hugging Face (HF) ecosystem to optimize Named Entity Recognition (NER) for financial data. By leveraging LLM-assisted labeling with HF Inference Endpoints and refining data with Argilla, the team improved accuracy by up to 6.4% and reduced operational costs, achieving solutions up to 80x cheaper than large LLMs alone. In this post, you will learn: How to use LLMs for […]

In the rapidly evolving landscape of large language models (LLMs), comprehensive and robust evaluation methodologies remain a critical challenge, particularly for low-resource languages. In this blog, we introduce AraGen, a generative tasks benchmark and leaderboard for Arabic LLMs, based on 3C3H, a new evaluation measure for NLG which we hope will inspire work for other languages as well. The AraGen leaderboard makes three key contributions: 3C3H Measure: The 3C3H measure scores a model’s response and is central to this framework. […]

👉 You can play with the Keras chatbot arenawhile you read. Click here to open it in a new tab. 👈 Table of contents   1. Introduction   2. The experiment   3. Keras chatbot arena tech: Spaces, Gradio, TPUs, JAX and Keras      3.1 Why TPUs?      3.2 Why JAX and Keras?      3.3    

We are excited to welcome Google’s all-new vision language models, PaliGemma 2, a new iteration of PaliGemma. Like its predecessor, PaliGemma 2 uses the same powerful SigLIP for vision, but it upgrades to the latest Gemma 2 for the text decoder part. PaliGemma 2 comes with new pre-trained (pt) models, in sizes of 3B, 10B, and 28B parameters. All of them support various input resolutions: 224×224, 448×448, and 896×896. These combinations provide a lot of flexibility for different use cases, […]

The Data is Better Together community releases yet another important dataset for open source development. Due to the lack of open preference datasets for text-to-image generation, we set out to release an Apache 2.0 licensed dataset for text-to-image generation. This dataset is focused on text-to-image preference pairs across common image generation categories, while mixing different model families and varying prompt complexities. TL;DR? All results can be found in this collection on the Hugging Face Hub and code for pre- and […]