fine-tuning-patterns

What problem does it solve?

This Skill provides comprehensive reference for making informed decisions in the process of LLM fine-tuning, from dataset curation to deployment strategies.

Core Features & Use Cases

• Fine-Tuning Decision Tree: Offers a structured approach to decide if and when to use fine-tuning over alternatives like RAG or prompting.
• Dataset Composition Guidelines: Instructs on building high-quality datasets and best practices for curation and annotation.
• Common Mistakes and Avoidance: Lists common pitfalls in fine-tuning and provides practical solutions to avoid them.

Quick Start

Fine-tune the 'financial-document-summarization' model on '2023 annual-reports' dataset using 'base-evaluation-metrics'.