Enhancing LLM Performance: Efficacy, Fine-Tuning, and Inference Techniques - Couverture souple

Couverture souple

ISBN 10 : 3031857496 ISBN 13 : 9783031857492

Editeur : Springer, 2026

Synopsis

This book is a pioneering exploration of the state-of-the-art techniques that drive large language models (LLMs) toward greater efficiency and scalability. Edited by three distinguished experts―Peyman Passban, Mehdi Rezagholizadeh, and Andy Way―this book presents practical solutions to the growing challenges of training and deploying these massive models. With their combined experience across academia, research, and industry, the authors provide insights into the tools and strategies required to improve LLM performance while reducing computational demands.

This book is more than just a technical guide; it bridges the gap between research and real-world applications. Each chapter presents cutting-edge advancements in inference optimization, model architecture, and fine-tuning techniques, all designed to enhance the usability of LLMs in diverse sectors. Readers will find extensive discussions on the practical aspects of implementing and deploying LLMs in real-world scenarios. The book serves as a comprehensive resource for researchers and industry professionals, offering a balanced blend of in-depth technical insights and practical, hands-on guidance. It is a go-to reference book for students, researchers in computer science and relevant sub-branches, including machine learning, computational linguistics, and more.

Détails de l'édition

• Éditeur: Springer
• Date d'édition: 2026
• Langue: anglais
• ISBN 10: 3031857496
• ISBN 13: 9783031857492
• Reliure: Broché
• Nombre de pages: 204
• Éditeur: Passban Peyman, Way Andy, Rezagholizadeh Mehdi
• Coordonnées du fabricant: Springer Nature Customer Service Center GmbH; ProductSafety@springernature.com; ; Europaplatz 3; Heidelberg; 69115; Allemagne