LLM Fine-Tuning
Part 1: Eight Major Methods For FineTuning an LLM
I delve into eight methods that use targeted parameter fine-tuning of LLMs. I discuss in detail Gradient-based, LoRA, QLoRA, and four others as advanced variations of ULMFiT: selecting a small subset of the available parameters in a trained LLM.
Overview of FineLarge Language Models
Large Language Models (LLMs) are leading the AI movement.
These LLMs vary widely in the tasks that they can accomplish, but all of them, currently, are described in terms of the number of parameters and the amount of text they were trained on.
Fine-tuning LLMs has emerged as a crucial technique to adapt these models to specific tasks and improve their performance.
In Part 1 of three planned posts, I review the evolution of targeted parameter fine-tuning of LLMs, describe in detail five of these fine-tuning methods, and ponder where we might be headed in fine-tuning.
Early Fine-tuning Methods
In the early days, fine-tuning was considered a finesse or trick to boost performance in data science competitions, such as Kaggle.
The earliest fine-tuning methods were simple and straightforward. They involved taking a…
