A Study of Optimizations for Fine-tuning Large Language Models

Understand

Fine-tuning large language models is a popular choice among users trying to adapt them for specific applications.

• However, fine-tuning these models is a demanding task because the user has to examine several factors, such as resource budget, runtime, model size and context length among others.
• A specific challenge is that fine-tuning is memory intensive, imposing constraints on the required hardware memory and context length of training data that can be handled.
• In this work, we share a detailed study on a variety of fine-tuning optimizations across different fine-tuning scenarios.