Llm Fine Tuning Guide

A comprehensive reference for adapting large language models to specific domains and tasks. Covers the full spectrum from full fine-tuning to parameter-efficient approaches like LoRA and QLoRA, with working code examples for each. The guide is practical about tradeoffs: full fine-tuning needs 1000+ examples and serious compute, while LoRA trains 99% fewer parameters in a fraction of the time. Includes data preparation scripts, validation techniques, and clear guidance on when fine-tuning makes sense versus just using better prompts or RAG. Best when you need a model specialized for legal docs, medical records, or other domain-specific work where base model performance isn't cutting it.