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Springer - Enhancing LLM Performance Book by Passban et al.

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Description

Enhancing LLM Performance: Efficacy, Fine-Tuning, and Inference Techniques provides a pioneering exploration of state-of-the-art methods for large language models. Edited by experts Peyman Passban, Mehdi Rezagholizadeh, and Andy Way, this Springer publication addresses the practical challenges of training and deploying massive models. Readers gain insights into tools and strategies designed to improve LLM performance while reducing computational demands. By focusing on efficiency and scalability, this book offers solutions for researchers and professionals working across academia and industry. It serves as a technical resource for mastering the fine-tuning and inference techniques necessary to drive modern AI models toward greater effectiveness.

Key Features

Edited by experts Peyman Passban, Mehdi Rezagholizadeh, and Andy Way to provide professional insights.

Explores state-of-the-art techniques for increasing the efficiency and scalability of large language models.

Provides practical solutions for the growing challenges of training and deploying massive AI models.

Offers strategies to improve LLM performance while simultaneously reducing computational demands.

Part of the Machine Translation: Technologies and Applications series published by Springer.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
05 July 2025
Listed Since
23 January 2025

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