Fine-tune and deploy Llama 2 models cost-effectively in Amazon SageMaker JumpStart with AWS Inferentia and AWS Trainium

This article discusses how to fine-tune and deploy Llama 2 models on AWS Trainium and AWS Inferentia instances using Amazon SageMaker JumpStart. It provides a cost-effective solution for running large language models.

Specifically, the article covers:

• Deploying the pre-trained Llama 2 model on AWS Inferentia instances through SageMaker Studio UI and Python SDK
• Fine-tuning the Llama 2 model on Trainium instances using SageMaker Studio UI and Python SDK
• Comparing the performance and responses of pre-trained vs fine-tuned models
• Cleaning up resources after use