LLM fine-tuning
Understand when fine-tuning is the right tool, and what it means for serving.
Draft
Model preparation
Draft page
This page is an outline. It describes what will be covered and is not yet complete technical documentation.
Fine-tuning adapts a pretrained model to a narrower task or style. It is frequently reached for when prompt engineering or retrieval would have solved the problem more cheaply, and it carries serving consequences that are easy to overlook.
This page covers when fine-tuning earns its cost and what it changes downstream.
What you will learn
- When fine-tuning is the appropriate tool and when it is not.
- How full fine-tuning and parameter-efficient methods differ operationally.
- What serving a fine-tuned or adapter-based model requires.
- How to evaluate whether a fine-tune actually improved your task.
Recommended outline
This page is an outline. The subsections below are the planned structure; they are filled in as the handbook is written.