PaliGemma – Google’s Cutting-Edge Open Vision Language Model

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 […]

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Unlocking Longer Generation with Key-Value Cache Quantization

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    

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Hugging Face on AMD Instinct MI300 GPU

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    

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Build AI on premise with Dell Enterprise Hub

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    

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CyberSecEval 2 – A Comprehensive Evaluation Framework for Cybersecurity Risks and Capabilities of Large Language Models

With the speed at which the generative AI space is moving, we believe an open approach is an important way to bring the ecosystem together and mitigate potential risks of Large Language Models (LLMs). Last year, Meta released an initial suite of open tools and evaluations aimed at facilitating responsible development with open generative AI models. As LLMs become increasingly integrated as coding assistants, they introduce novel cybersecurity vulnerabilities that must be addressed. To tackle this challenge, comprehensive benchmarks are […]

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Falcon 2: An 11B parameter pretrained language model and VLM, trained on over 5000B tokens and 11 languages

The Falcon 2 Models TII is launching a new generation of models, Falcon 2, focused on providing the open-source community with a series of smaller models with enhanced performance and multi-modal support. Our goal is to enable cheaper inference and encourage the development of more downstream applications with improved usability. The first generation of Falcon models, featuring Falcon-40B and Falcon-180B, made a significant contribution to the open-source community, promoting the    

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Training and Finetuning Embedding Models with Sentence Transformers v3

Sentence Transformers is a Python library for using and training embedding models for a wide range of applications, such as retrieval augmented generation, semantic search, semantic textual similarity, paraphrase mining, and more. Its v3.0 update is the largest since the project’s inception, introducing a new training approach. In this blogpost, I’ll show you how to use it to finetune Sentence    

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