Overview

While foundation models provide powerful general intelligence, real-world applications often require domain adaptation, improved consistency, and controlled outputs. In Practical LLM Fine-Tuning and Adaptation, Zhao Colton presents a structured guide to customizing large language models for specialized tasks. This book explores: Fine-tuning fundamentals and model adaptation theory Supervised fine-tuning workflows Dataset preparation and curation strategies Instruction tuning and alignment techniques Evaluation benchmarks and quality measurement Parameter-efficient fine-tuning (PEFT) methods Safety and bias considerations Deployment pipelines and performance monitoring The book balances conceptual understanding with implementation strategy, helping practitioners understand not only how to fine-tune models but when it is appropriate to do so. Readers will gain clarity on designing responsible, efficient, and maintainable model adaptation pipelines suitable for production systems.

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