Isbn: 9798249390600 - fine tuning large language models: adapting foundation models for domain-specific intelligence and performance optimization: 1 (6 risultati)

Paperback. Condizione: New.

Paperback. Condizione: New.

Condizione: New. Print on Demand.

Paperback. Condizione: new. Paperback. 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 controlSupervised fine-tuning (SFT) workflowsParameter-efficient tuning techniquesInstruction tuning and conversational alignmentEvaluation metrics and benchmarking strategiesOverfitting mitigation and safety considerationsCost optimization and compute planningReaders 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

PAP. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback. Condizione: new. Paperback. 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 controlSupervised fine-tuning (SFT) workflowsParameter-efficient tuning techniquesInstruction tuning and conversational alignmentEvaluation metrics and benchmarking strategiesOverfitting mitigation and safety considerationsCost optimization and compute planningReaders 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…