Name: Fine Tuning Large Language Models for Domain Specific Applications. Training Data Preparation, Adaptation Techniques, and Performance Optimization for LLM Systems (en Inglés)
SKU: 67462608
Price: 40.64 USD
Availability: InStock
Author: Marcus Linford

portada Fine Tuning Large Language Models for Domain Specific Applications. Training Data Preparation, Adaptation Techniques, and Performance Optimization for LLM Systems (en Inglés)

Fine Tuning Large Language Models for Domain Specific Applications. Training Data Preparation, Adaptation Techniques, and Performance Optimization for LLM Systems (en Inglés)

Formato

Libro Físico

Editorial

Independently published

Autor

Marcus Linford

Año

2026

Idioma

Inglés

N° páginas

352

Encuadernación

Tapa Blanda

Dimensiones

25.4x17.8x1.8 cm

ISBN13

9798251249903

Fine Tuning Large Language Models for Domain Specific Applications. Training Data Preparation, Adaptation Techniques, and Performance Optimization for LLM Systems (en Inglés)

Marcus Linford (Autor) · Independently published · Tapa Blanda

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Reseña del libro "Fine Tuning Large Language Models for Domain Specific Applications. Training Data Preparation, Adaptation Techniques, and Performance Optimization for LLM Systems (en Inglés)"

Large language models provide powerful general-purpose capabilities, but many real-world applications require models that understand specialized terminology, structured tasks, or domain-specific information. Fine-tuning enables developers to adapt pre-trained models so they perform more effectively within targeted environments.
Fine-Tuning Large Language Models for Domain-Specific Applications examines the technical processes used to customize language models through structured training methods and dataset preparation strategies.
The book explains how developers can refine model behavior, improve response quality, and adapt large language models to specialized use cases while maintaining responsible and efficient development practices.
Topics explored throughout this book include:Foundations of language model training and adaptationPreparing datasets for supervised fine-tuning workflowsStructuring prompts and instruction-based training dataEvaluation methods for measuring model performanceStrategies for improving accuracy and consistency in outputsDeployment considerations for fine-tuned models in applicationsBy providing a clear explanation of the fine-tuning process and its practical implementation, this book helps developers move from general-purpose AI models toward more specialized and effective intelligent systems.