We are releasing Transformers Agents 2.0! ⇒ 🎁 On top of our existing agent type, we introduce two new agents that can iterate based on past observations to solve complex tasks. ⇒ 💡 We aim for the code to be clear and modular, and for common attributes like the final prompt and tools to be transparent. ⇒ 🤝 We add sharing options to boost community agents. ⇒ 💪 Extremely performant new agent framework, allowing a Llama-3-70B-Instruct agent to outperform GPT-4 […]

The Open Arabic LLM Leaderboard (OALL) is designed to address the growing need for specialized benchmarks in the Arabic language processing domain. As the field of Natural Language Processing (NLP) progresses, the focus often remains heavily skewed towards English, leaving a significant gap in resources for other languages. The OALL aims to balance this by providing a platform specifically for evaluating and comparing the performance of Arabic Large Language Models (LLMs), thus promoting research and development in Arabic NLP. This […]

We are thrilled to announce the launch of langchain_huggingface, a partner package in LangChain jointly maintained by Hugging Face and LangChain. This new Python package is designed to bring the power of the latest development of Hugging Face into LangChain and keep it up to date. All Hugging Face-related classes in LangChain were coded by the community, and while we thrived on this, over time, some of them became deprecated because of the lack of an insider’s perspective. By becoming […]

Updated on 23-05-2024: We have introduced a few changes to the transformers PaliGemma implementation around fine-tuning, which you can find in this notebook. PaliGemma is a new family of vision language models from Google. PaliGemma can take in an image and a text and output text. The team at Google has released three types of models: the pretrained (pt) models, the mix models, and the fine-tuned (ft) models, each with different resolutions and available in multiple precisions for convenience. All […]

At Hugging Face, we are excited to share with you a new feature that’s going to take your language models to the next level: KV Cache Quantization. TL;DR: KV Cache Quantization reduces memory usage for long-context text generation in LLMs with minimal impact on quality, offering customizable trade-offs between memory efficiency and generation speed. Have you ever tried generating a lengthy piece    

Today at Microsoft Build we are happy to announce a broad set of new features and collaborations as Microsoft and Hugging Face deepen their strategic collaboration to make open models and open source AI easier to use everywhere. Together,    

Join the next Hugging Cast on June 6th to ask questions to the post authors, watch a live demo deploying Llama 3 on MI300X on Azure, plus a bonus demo deploying models locally on Ryzen AI PC! Register at https://streamyard.com/watch/iMZUvJnmz8BV Introduction At Hugging Face we want to make it easy to build AI with open models and open source, whichever framework, cloud and stack you want to use. A key component is    

Today we announce the Dell Enterprise Hub, a new experience on Hugging Face to easily train and deploy open models on-premise using Dell platforms. Try it out at dell.huggingface.co Enterprises need to build AI with open models When building AI systems, open models is the best solution to meet security, compliance and privacy requirements of enterprises: Building upon open models allows companies to understand, own    

Hugging Face Spaces makes it easy for you to create and deploy AI-powered demos in minutes. Over 500,000 Spaces have been created by the Hugging Face community and it keeps growing! As part of Hugging Face Spaces, we recently released support for “Dev Mode”, to make your experience of building Spaces even more seamless. Spaces Dev Mode lets you connect with    

AWS Inferentia2 is the latest AWS machine learning chip available through the Amazon EC2 Inf2 instances on Amazon Web Services. Designed from the ground up for AI workloads, Inf2 instances