Fine-Tuning Large Language Models: From Custom Datasets to High-Performance AI Models Using Modern Toolchains

Are your prompts not cutting it anymore? Fine-tuning is the next level. This hands-on guide takes you from zero to deploying your own custom-trained large language model using real code, modern tools, >Whether you want to build a medical assistant, legal document analyzer, coding AI, or customer support bot, this book gives you the exact >What you'll learn: - How fine-tuning differs from prompt engineering and RAG and when to use each - LoRA and QLoRA: fine-tune powerful models on a single GPU - How to collect, clean, and format high-quality instruction datasets - Supervised Fine-Tuning (SFT), RLHF, and Direct Preference Optimization - Advanced techniques: mixed precision, gradient checkpointing, multi-GPU training - How to evaluate, quantize, and deploy your model to production - Safety, bias mitigation, and responsible AI practices - 14 chapters of step-by-step projects including a chatbot, >This is not a theory book. Every chapter includes working Python code using Hugging Face Transformers, PEFT, TRL, and modern open-source >By the end, you won't just understand fine-tuning you'll have built >Perfect for: ML engineers, Python developers, data scientists, and anyone ready to move beyond ChatGPT and take control of their AI stack.