Foundation models

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

Amazon Bedrock, Best Practices, Foundation models, Generative AI /

Multimodal fine-tuning represents a powerful approach for customizing foundation models (FMs) to excel at specific tasks that involve both visual and textual information. Although base multimodal models offer impressive general capabilities, they often fall short when faced with specialized visual tasks, domain-specific content, or particular output formatting requirements. Fine-tuning addresses these limitations by adapting models […]

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Amazon Bedrock Model Distillation: Boost function calling accuracy while reducing cost and latency

Amazon Bedrock, Foundation models, Generative AI, Launch /

Amazon Bedrock Model Distillation is generally available, and it addresses the fundamental challenge many organizations face when deploying generative AI: how to maintain high performance while reducing costs and latency. This technique transfers knowledge from larger, more capable foundation models (FMs) that act as teachers to smaller, more efficient models (students), creating specialized models that

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AWS Field Experience reduced cost and delivered low latency and high performance with Amazon Nova Lite foundation model

Amazon Bedrock, Amazon Nova, Customer Solutions, Foundation models, Generative AI /

AWS Field Experience (AFX) empowers Amazon Web Services (AWS) sales teams with generative AI solutions built on Amazon Bedrock, improving how AWS sellers and customers interact. The AFX team uses AI to automate tasks and provide intelligent insights and recommendations, streamlining workflows for both customer-facing roles and internal support functions. Their approach emphasizes operational efficiency

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Reduce ML training costs with Amazon SageMaker HyperPod

Amazon Machine Learning, Amazon SageMaker, Amazon SageMaker AI, Artificial Intelligence, Foundation models, Generative AI /

Training a frontier model is highly compute-intensive, requiring a distributed system of hundreds, or thousands, of accelerated instances running for several weeks or months to complete a single job. For example, pre-training the Llama 3 70B model with 15 trillion training tokens took 6.5 million H100 GPU hours. On 256 Amazon EC2 P5 instances (p5.48xlarge,

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Build a Multi-Agent System with LangGraph and Mistral on AWS

Amazon Bedrock, Amazon Machine Learning, Foundation models, Generative AI, Technical How-to /

Agents are revolutionizing the landscape of generative AI, serving as the bridge between large language models (LLMs) and real-world applications. These intelligent, autonomous systems are poised to become the cornerstone of AI adoption across industries, heralding a new era of human-AI collaboration and problem-solving. By using the power of LLMs and combining them with specialized

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