In this video, I delve into optimizing the fine-tuning of a Google FLAN-T5 model for legal text summarization. The focus is on employing QLoRA for parameter-efficient fine-tuning: all it takes is a few extra lines of simple code in your existing script.

This methodology allows us to train the model with remarkable cost efficiency, utilizing even modest GPU instances, which I demonstrate on AWS with Amazon SageMaker. Tune in for a detailed exploration of the technical nuances behind this process.