9783031857461 - Enhancing LLM Performance: Efficacy, Fine-Tuning, and Inference Techniques: 7 (Machine Translation: Technologies and Applications, 7) (9 resultados)

Condición: New.

Buch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.

Condición: new. Questo è un articolo print on demand.

Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 204 pp. Englisch.

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt.

Buch. Condición: Neu. Enhancing LLM Performance | Efficacy, Fine-Tuning, and Inference Techniques | Peyman Passban (u. a.) | Buch | Machine Translation: Technologies and Applications | xvii | Englisch | 2025 | Springer | EAN 9783031857461 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand.

Buch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Introduction and Fundamentals.- SPEED: Speculative Pipelined Execution for Efficient Decoding.- Efficient LLM Inference on CPUs.- KronA: Parameter-Efficient Tuning with Kronecker Adapter.- LoDA: Low-Dimensional Adaptation of Large Language Models.- Sparse Fine-Tuning for Inference Acceleration of Large Language Models.- TCNCA: Temporal CNN with Chunked Attention for Efficient Training on Long Sequences.- Class-Based Feature Knowledge Distillation.- On the Use of Cross-Attentive Fusion Techniques for Audio-Visual Speaker Verification.- An Efficient Clustering Algorithm for Self-Supervised Speaker Recognition.- Remaining Issues for AI.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 204 pp. Englisch.

Condición: New. Print on Demand.

Condición: New. PRINT ON DEMAND.