Applied LLM Fine-Tuning: A Comprehensive Guide: Hands-On Methods, Open-Source Tools, and Real-World Use Cases

Reactive PublishingApplied LLM Fine-Tuning is a hands-on guide to adapting large language models for real production use. This book focuses on practical methods, tooling, and workflows used to fine-tune LLMs for specific tasks, domains, and constraints without unnecessary complexity.Rather than debating abstract model theory, the book shows how fine-tuning is actually performed in practice. You’ll learn how to prepare and clean datasets, design effective training objectives, apply parameter-efficient fine-tuning techniques, and select the right approach based on latency, cost, and deployment requirements.The book walks through real-world workflows using modern open-source tools and frameworks, including training pipelines, evaluation methods, and iteration strategies that improve model reliability. It also covers common failure modes such as overfitting, hallucation amplification, data leakage, and silent performance degradation, along with concrete ways to detect and correct them.Applied LLM Fine-Tuning is written for engineers, data scientists, and technical teams who need models that behave consistently in real systems. Whether you are building internal tools, domain-specific assistants, or customer-facing AI products, this book provides clear, repeatable methods to move from experimentation to dependable results. Read more