Overview

Large language models provide powerful general capabilities, but real-world applications often require domain alignment, improved accuracy, and controlled behavior. Fine-Tuning Large Language Models provides a practical engineering roadmap for adapting foundation models to specialized use cases while maintaining stability, efficiency, and measurable performance. This book covers the full fine-tuning lifecycle, including: Dataset curation and quality control Supervised fine-tuning (SFT) workflows Parameter-efficient tuning techniques Instruction tuning and conversational alignment Evaluation metrics and benchmarking strategies Overfitting mitigation and safety considerations Cost optimization and compute planning Readers will gain structured insight into designing reproducible fine-tuning pipelines and assessing model performance in real environments. The focus remains on engineering discipline, experimentation rigor, and responsible model adaptation rather than hype or unrealistic performance claims. This volume is ideal for machine learning engineers, applied researchers, and technical professionals seeking to move beyond prompt engineering toward controlled, measurable model customization.

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