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Enhancing LLM performanceefficacy, fine-tuning, and inference techniques /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Enhancing LLM performanceedited by Peyman Passban, Andy Way, Mehdi Rezagholizadeh.
Reminder of title:	efficacy, fine-tuning, and inference techniques /
other author:	Passban, Peyman.
Published:	Cham :Springer Nature Switzerland :2025.
Description:	xvii, 183 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Machine learning.
Online resource:	https://doi.org/10.1007/978-3-031-85747-8
ISBN:	9783031857478$q(electronic bk.)

Enhancing LLM performanceefficacy, fine-tuning, and inference techniques /
Enhancing LLM performanceefficacy, fine-tuning, and inference techniques /[electronic resource] :edited by Peyman Passban, Andy Way, Mehdi Rezagholizadeh. - Cham :Springer Nature Switzerland :2025. - xvii, 183 p. :ill. (some col.), digital ;24 cm. - Machine translation: technologies and applications,v. 72522-803X ;. - Machine translation: technologies and applications ;v.2..

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.

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.

ISBN: 9783031857478$q(electronic bk.)

Standard No.: 10.1007/978-3-031-85747-8doiSubjects--Topical Terms:

188639
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Enhancing LLM performanceefficacy, fine-tuning, and inference techniques /
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505 0 $a 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.
520 $a 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.
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