Overview

This book will only be available for a short time. I just need 10 serious readers to grab it before I take it down to revise for private release. Be one of the few. Fine-Tuning LLMs Crash Course is your advanced, practical guide to mastering the art and science of optimizing large language models (LLMs) for real-world performance. Whether you're a machine learning engineer, AI researcher, or developer exploring custom AI solutions, this crash course gives you everything you need to train, align, and deploy smarter, domain-specific models at scale. What's Inside: - Step-by-step workflows for fine-tuning models like GPT, LLaMA, and Falcon - Techniques for aligning models with specific tasks, tone, and business goals - Strategies for dataset preparation, prompt engineering, and hyperparameter tuning - Real-world examples from industries like finance, healthcare, customer service, and more - Deployment tips for scalable inference, safety, and monitoring in production environments Key Features: - Covers both open-source and proprietary LLMs - Code examples using Hugging Face, OpenAI API, and custom training loops - Best practices for reducing hallucinations and improving model accuracy - Insights into evaluation, benchmarking, and responsible AI fine-tuning Who This Book Is For: - AI engineers and data scientists ready to move beyond off-the-shelf models - Developers building intelligent apps, agents, or internal AI tools - Startups and enterprises looking to tailor LLMs to their domain - Anyone interested in deploying AI that actually fits their users and context Why You Should Get This Book: Generic AI is powerful but custom AI is unbeatable. This book shows you how to unlock the true potential of LLMs by fine-tuning them for your needs. If you want your AI to be smarter, faster, safer, and laser-focused on your use case, *Fine-Tuning LLMs Crash Course* is your blueprint.

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