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PEFT fine tuning of Llama 3 on SageMaker HyperPod with AWS Trainium

AWS Machine Learning Blog

To simplify infrastructure setup and accelerate distributed training, AWS introduced Amazon SageMaker HyperPod in late 2023. In this blog post, we showcase how you can perform efficient supervised fine tuning for a Meta Llama 3 model using PEFT on AWS Trainium with SageMaker HyperPod. architectures/5.sagemaker-hyperpod/LifecycleScripts/base-config/

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Get started quickly with AWS Trainium and AWS Inferentia using AWS Neuron DLAMI and AWS Neuron DLC

AWS Machine Learning Blog

Starting with the AWS Neuron 2.18 release , you can now launch Neuron DLAMIs (AWS Deep Learning AMIs) and Neuron DLCs (AWS Deep Learning Containers) with the latest released Neuron packages on the same day as the Neuron SDK release. AWS Systems Manager Parameter Store support Neuron 2.18

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Process formulas and charts with Anthropic’s Claude on Amazon Bedrock

AWS Machine Learning Blog

AWS Lambda AWS Lambda is a compute service that runs code in response to triggers such as changes in data, changes in application state, or user actions. Prerequisites If youre new to AWS, you first need to create and set up an AWS account. We use Amazon S3 to store sample documents that are used in this solution.

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How Carrier predicts HVAC faults using AWS Glue and Amazon SageMaker

AWS Machine Learning Blog

In this post, we show how the Carrier and AWS teams applied ML to predict faults across large fleets of equipment using a single model. We first highlight how we use AWS Glue for highly parallel data processing. AWS Glue allowed us to easily run parallel data preprocessing and feature extraction. Additionally, 10.4%

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Simple guide to training Llama 2 with AWS Trainium on Amazon SageMaker

AWS Machine Learning Blog

Llama2 by Meta is an example of an LLM offered by AWS. To learn more about Llama 2 on AWS, refer to Llama 2 foundation models from Meta are now available in Amazon SageMaker JumpStart. Virginia) and US West (Oregon) AWS Regions, and most recently announced general availability in the US East (Ohio) Region.

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Generative AI Models Are Built to Hallucinate: The Question is How to Control Them

insideBIGDATA

In this contributed article, Stefano Soatto, Professor of Computer Science at the University of California, Los Angeles and a Vice President at Amazon Web Services, discusses generative AI models and how they are designed and trained to hallucinate, so hallucinations are a common product of any generative model.

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Map Earth’s vegetation in under 20 minutes with Amazon SageMaker

AWS Machine Learning Blog

See Amazon SageMaker geospatial capabilities to learn more. About the Author Xiong Zhou is a Senior Applied Scientist at AWS. He leads the science team for Amazon SageMaker geospatial capabilities. Janosch Woschitz is a Senior Solutions Architect at AWS, specializing in AI/ML. He is an ACM Fellow and IEEE Fellow.

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