Tuning Large Language Models for Real-World Applications: Fine-Tuning, Alignment, and Deployment: Build, Align, and Deploy LLMs with Hands-On Projects
Language: English
Published by Staten House, 2026
- Softcover
- New
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- Title
- Tuning Large Language Models for Real-World Applications: Fine-Tuning, Alignment, and Deployment: Build, Align, and Deploy LLMs with Hands-On Projects
- Author
- Technologies, Cuantum
- Publisher
- Staten House
- Publication year
- 2026
- Condition
- New
- Binding
- Soft cover
- Language
- English
- ISBN 13
- 9798904170622
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You’ve looked under the hood.
Now it’s time to tune the engine—and put it on the road.
Large language models are powerful, but raw models are only the starting point. To build real AI applications, you need more than prompts and APIs. You need to know how to customize models, align their behavior, evaluate their outputs, and deploy them efficiently in production environments.
This book shows you exactly how.
Tuning Large Language Models: Fine-Tuning, Alignment, and Deployment is a hands-on guide designed for developers, AI engineers, and machine learning practitioners who want to move beyond theory and start building real-world systems with LLMs.
In this volume, you’ll follow the complete lifecycle of modern LLM engineering—from adapting base models to deploying scalable AI services. Each concept is explained clearly and reinforced through practical examples and projects, so you can apply what you learn immediately.
Inside, you’ll learn how to:
- Design and curate high-quality instruction datasets for supervised fine-tuning
- Apply Supervised Fine-Tuning (SFT) to improve instruction-following behavior
- Use parameter-efficient fine-tuning (PEFT) techniques like LoRA, QLoRA, adapters, and prefix tuning
- Align model behavior using Reinforcement Learning with Human Feedback (RLHF) and Direct Preference Optimization (DPO)
- Build and evaluate preference datasets to guide model responses
- Measure performance using modern benchmarks such as MT-Bench and HELM
- Detect and reduce hallucinations, bias, and unsafe outputs
- Optimize inference using quantization, distillation, and high-performance serving frameworks like vLLM and TensorRT-LLM
- Deploy models as scalable APIs and monitor latency, token usage, and production cost
This book is designed to be practical.
You won’t just read about concepts—you’ll implement them.
Through 9 hands-on projects, you’ll build complete systems that reflect real-world AI workflows, including:
- A domain-specific Q&A assistant using LoRA
- A preference-aligned chatbot trained with DPO
- A production-ready API for serving a fine-tuned model
- Evaluation pipelines for measuring model performance and alignment
- Deployment setups with monitoring and cost tracking
Each project reinforces key ideas and helps you develop the skills needed to move from experimentation to production.
By the end of this book, you will be able to:
- Transform general-purpose LLMs into specialized AI systems
- Control model behavior through fine-tuning and alignment techniques
- Evaluate models with confidence using modern metrics and frameworks
- Deploy and operate LLMs as real-world AI services
Whether you’re building internal tools, AI-powered products, or exploring advanced machine learning workflows, this book will give you the practical knowledge to turn powerful models into reliable applications.
Because understanding the engine is only the beginning.
The real advantage comes from knowing how to tune it—and how to drive it.
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