Mastering LLM Fine-Tuning: A Practical Guide to PEFT, Alignment, and Specialized Model Optimization

Name: Mastering LLM Fine-Tuning: A Practical Guide to PEFT, Alignment, and Specialized Model Optimization
Brand: Independently Published
SKU: 9798245595344
Price: 2666.0 INR
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ISBN13: 9798245595344
Binding: Paperback
Subject: N/A
Publisher: Independently Published
Publisher Imprint: Independently Published
Publication Date: January 25, 2026
Pages: 226
Original Price: USD 25.51
Language: English
Edition: N/A
Item Weight: 400 grams
BISAC Subject(s): Data Science / Neural Networks

About The Book

Mastering LLM Fine-Tuning: A Practical Guide to PEFT, Alignment, and Specialized Model Optimization
Stop building generic AI and start engineering specialized intelligence.
The transition from a basic pre-trained model to a production-grade, domain-specific asset is the most critical hurdle in Generative AI. Whether you are a machine learning engineer, a technical architect, or a data scientist, the ability to fine-tune Large Language Models (LLMs) effectively is what separates a prototype from a profitable application.
Mastering LLM Fine-Tuning is your hands-on roadmap to the cutting edge of AI specialization. This book strips away the academic jargon to deliver a direct, exhaustive guide to the tools and techniques used by industry leaders in 2026. From the mathematical efficiency of PEFT (LoRA, QLoRA) to the strategic precision of DPO and Federated Learning, you will learn how to squeeze maximum performance out of any model on any budget.
What You Will Master:

Parameter-Efficient Fine-Tuning (PEFT): Dive deep into LoRA, QLoRA, and Adapter-based methods to specialize 70B+ models on consumer hardware.
Alignment & Human Feedback: Master Direct Preference Optimization (DPO) and RLHF to ensure your models are safe, grounded, and helpful.
Production Serving & Monitoring: Deploy high-throughput APIs using vLLM, TGI, and Ollama while implementing real-time hallucination detection.
Next-Gen Architectures: Explore On-Device fine-tuning and Continuous Learning pipelines to move beyond static checkpoints.
Ethical Automation: Engineer automated guardrails for bias detection, data sanitization, and PII redaction in autonomous tuning systems.

Why This Book?
While other guides focus on surface-level API calls, this book provides the "why" behind the "how." You will find real-world hardware selection matrices (Budget vs. Performance), comprehensive hyperparameter cheat sheets, and CI/CD workflows specifically designed for LLM lifecycles.
Mastering LLM Fine-Tuning isn't just a manual-it is your unfair advantage in the AI era. Whether you are deploying on a local workstation or a massive H100 cluster, the frameworks and blueprints inside will help you build smarter, faster, and more reliable AI.
Join the elite rank of engineers who don't just use AI-they specialize it. Scroll up and secure your copy today.

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