Large Language Models (LLMs) Handbook (Paperback)

Name: Large Language Models (LLMs) Handbook (Paperback)
Author: Sammy Tech
ISBN: 9798255541904

Sammy Tech

Language: English

Published by Independently Published, 2026

9798255541904

Series: Book 8 of 12 - Programming AI & Development Handbook Collection

Softcover
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Paperback. Large Language Models have transformed the field of artificial intelligence. What began as research prototypes has evolved into powerful production systems driving real business value across industries. Yet moving from impressive demos to reliable, scalable, and cost-effective deployments remains one of the biggest challenges facing AI teams today.This handbook bridges that critical gap.Written for experienced developers, AI engineers, and technical leaders, Large Language Models (LLMs) Handbook delivers a complete, end-to-end guide to the modern LLM lifecycle. From transformer fundamentals and pre-training principles to advanced fine-tuning, preference alignment, retrieval-augmented generation (RAG), agentic systems, and production-grade inference optimization - every stage is covered with clarity, depth, and practical focus.You will learn how to: Build and train transformer-based models from scratchMaster parameter-efficient fine-tuning with LoRA, QLoRA, and DoRAAlign models using RLHF, DPO, ORPO, and other state-of-the-art techniquesDesign robust RAG systems - from naive retrieval to Graph RAG and Agentic RAGBuild autonomous agents with ReAct, tool use, memory, planning, and multi-agent collaborationOptimize inference with quantization (GPTQ, AWQ, GGUF), speculative decoding, and continuous batchingDeploy and serve models efficiently using vLLM, TensorRT-LLM, and OllamaEstablish production-grade LLMOps with monitoring, observability, versioning, and governanceNavigate security, ethics, cost optimization, and enterprise scaling challengesEach chapter combines rigorous technical explanations with complete, production-ready code examples, architectural diagrams, real-world production tips, hands-on exercises, and checklists you can apply immediately.Whether you are fine-tuning open-source models, building sophisticated agentic workflows, or scaling LLM applications to thousands of users, this handbook provides the practical knowledge and battle-tested patterns needed to succeed in 2026 and beyond.Perfect for: Machine Learning Engineers and AI PractitionersSoftware Engineers moving into AI systemsTechnical leaders and architects responsible for LLM initiativesAnyone building production-grade applications with Large Language ModelsStay ahead in one of the fastest-moving fields in technology. Master the full LLM lifecycle - from research to reliable production systems. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

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Title: Large Language Models (LLMs) Handbook (Paperback)
Author: Sammy Tech
Publisher: Independently Published
Publication year: 2026
Condition: new
Binding: Paperback
Language: English
ISBN 13: 9798255541904
Series: Book 8 of 12: Programming AI & Development Handbook Collection

Large Language Models have transformed the field of artificial intelligence. What began as research prototypes has evolved into powerful production systems driving real business value across industries. Yet moving from impressive demos to reliable, scalable, and cost-effective deployments remains one of the biggest challenges facing AI teams today.

This handbook bridges that critical gap.

Written for experienced developers, AI engineers, and technical leaders, Large Language Models (LLMs) Handbook delivers a complete, end-to-end guide to the modern LLM lifecycle. From transformer fundamentals and pre-training principles to advanced fine-tuning, preference alignment, retrieval-augmented generation (RAG), agentic systems, and production-grade inference optimization — every stage is covered with clarity, depth, and practical focus.

You will learn how to:

Build and train transformer-based models from scratch
Master parameter-efficient fine-tuning with LoRA, QLoRA, and DoRA
Align models using RLHF, DPO, ORPO, and other state-of-the-art techniques
Design robust RAG systems — from naive retrieval to Graph RAG and Agentic RAG
Build autonomous agents with ReAct, tool use, memory, planning, and multi-agent collaboration
Optimize inference with quantization (GPTQ, AWQ, GGUF), speculative decoding, and continuous batching
Deploy and serve models efficiently using vLLM, TensorRT-LLM, and Ollama
Establish production-grade LLMOps with monitoring, observability, versioning, and governance
Navigate security, ethics, cost optimization, and enterprise scaling challenges

Each chapter combines rigorous technical explanations with complete, production-ready code examples, architectural diagrams, real-world production tips, hands-on exercises, and checklists you can apply immediately.

Whether you are fine-tuning open-source models, building sophisticated agentic workflows, or scaling LLM applications to thousands of users, this handbook provides the practical knowledge and battle-tested patterns needed to succeed in 2026 and beyond.

Perfect for:

Machine Learning Engineers and AI Practitioners
Software Engineers moving into AI systems
Technical leaders and architects responsible for LLM initiatives
Anyone building production-grade applications with Large Language Models

Stay ahead in one of the fastest-moving fields in technology. Master the full LLM lifecycle — from research to reliable production systems.

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