Preprocess and fine-tune LLMs quickly and cost-effectively using Amazon EMR Serverless and Amazon SageMaker

This article discusses how to preprocess and fine-tune large language models (LLMs) using Amazon EMR Serverless and Amazon SageMaker.

Specifically, the article covers:

• Introduction to the Common Crawl dataset and methods for exploring, filtering, and processing it using Athena and EMR Spark
• Processing the filtered Common Crawl data using Amazon EMR Serverless to prepare it for LLM fine-tuning
• Fine-tuning the Llama 2 LLM on the preprocessed data using Amazon SageMaker JumpStart, without writing any code
• Evaluating and comparing the performance of the fine-tuned model vs. the original model on a sample task