Overview

Fine-Tuning LLMs with Hugging Face Clear, Complete, and Practical Techniques for Data Scientists and Engineers to Adapt Large Language Models with Transformers, LoRA, and Modern Workflows Large Language Models (LLMs) like GPT, LLaMA, and Falcon are revolutionizing industries, but for most practitioners, the challenge is not just using them - it's adapting them to real-world tasks. Full fine-tuning is expensive, complex, and often impractical. That's where Hugging Face and parameter-efficient techniques like LoRA and QLoRA come in. This book is your complete, hands-on guide to fine-tuning LLMs for tasks such as chatbots, summarization, classification, and domain adaptation. You'll learn how to prepare data, train efficiently on consumer GPUs, monitor performance, and deploy production-ready models - without requiring a supercomputer. Inside, you'll discover: Why pretraining alone isn't enough, and how fine-tuning unlocks domain-specific intelligence. The Hugging Face ecosystem - Transformers, Datasets, Tokenizers, Accelerate, PEFT - explained with real projects. Step-by-step workflows for fine-tuning with the Trainer API, LoRA, and QLoRA. How to prepare and clean domain datasets (e.g., legal, medical, customer support). Scaling strategies: distributed training, mixed precision, and gradient checkpointing. Deployment options with Hugging Face Spaces, Inference API, ONNX, TensorRT, and FastAPI. Responsible AI practices: managing bias, hallucinations, safety filters, and monitoring models in production. Advanced topics: multimodal fine-tuning (Whisper, CLIP, BLIP), retrieval-augmented generation (RAG), and adapter fusion. With practical examples, exercises, and production-oriented tips, this book transforms you from a model consumer into a model adapter - someone who can confidently fine-tune, optimize, and deploy LLMs at scale. Whether you're a data scientist, ML engineer, or AI enthusiast, this book gives you the tools to build models that are efficient, reliable, and tailored to your needs.

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