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Cohere Embed multimodal embeddings model is now available on Amazon SageMaker JumpStart

AWS Machine Learning

Cohere Embed 3 makes it simple to locate specific UI mockups, visual templates, and presentation slides based on a text description. All text-to-image benchmarks are evaluated using Recall@5 ; text-to-text benchmarks are evaluated using NDCG@10. Generic text-to-image benchmark accuracy is based on Flickr and CoCo.

Benchmark 109
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Pixtral-12B-2409 is now available on Amazon Bedrock Marketplace

AWS Machine Learning

Overview of Pixtral 12B Pixtral 12B, Mistrals inaugural VLM, delivers robust performance across a range of benchmarks, surpassing other open models and rivaling larger counterparts, according to Mistrals evaluation. Performance metrics and benchmarks Pixtral 12B is trained to understand both natural images and documents, achieving 52.5%

Benchmark 104
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Benchmark and optimize endpoint deployment in Amazon SageMaker JumpStart 

AWS Machine Learning

This post explores these relationships via a comprehensive benchmarking of LLMs available in Amazon SageMaker JumpStart, including Llama 2, Falcon, and Mistral variants. We provide theoretical principles on how accelerator specifications impact LLM benchmarking. Additionally, models are fully sharded on the supported instance.

Benchmark 127
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LLM continuous self-instruct fine-tuning framework powered by a compound AI system on Amazon SageMaker

AWS Machine Learning

To address these challenges, we present an innovative continuous self-instruct fine-tuning framework that streamlines the LLM fine-tuning process of training data generation and annotation, model training and evaluation, human feedback collection, and alignment with human preference. Set up a SageMaker notebook instance.

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Federal Express sets a benchmark on how to improve customer experience

Vonage

Whenever I call Federal Express to arrange an outgoing shipment of Ron Kaufman books, tapes, videos and learning resources, FedEx already knows my name, address and account number … even before I tell them who is calling. When I call to make a reservation, they ask for my account or priority number each and every time.

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Accelerate digital pathology slide annotation workflows on AWS using H-optimus-0

AWS Machine Learning

This sets a new benchmark for state-of-the-art performance in critical medical diagnostic tasks, from identifying cancerous cells to detecting genetic abnormalities in tumors. Prerequisites We assume you have access to and are authenticated in an AWS account. The AWS CloudFormation template for this solution uses t3.medium

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Best practices for Meta Llama 3.2 multimodal fine-tuning on Amazon Bedrock

AWS Machine Learning

Our recommendations are based on extensive experiments using public benchmark datasets across various vision-language tasks, including visual question answering, image captioning, and chart interpretation and understanding. Prerequisites To use this feature, make sure that you have satisfied the following requirements: An active AWS account.