Mastering Hugging Face Transformers and LLMs: The Complete Hands-On Guide to Fine-Tuning, RAG Pipelines, Multimodal AI, Agentic Workflows, and Scalabl
The Complete Hands-On Guide to Fine-Tuning, RAG Pipelines, Multimodal AI, Agentic Workflows, and Scalable Deployment with Python and PyTorch
Unlock the full power of modern AI with this definitive, hands-on guide to building, fine-tuning, and deploying cutting-edge Transformer and LLM systems using the Hugging Face ecosystem. Whether you are developing intelligent applications, designing multimodal AI pipelines, or architecting scalable RAG and agentic workflows, this book shows you exactly how to do it with clarity, depth, and real-world precision.
Built for developers, AI engineers, and data scientists, this book goes far beyond surface-level tutorials. You will learn how state-of-the-art models actually work, how to optimize them for specialized tasks using LoRA and QLoRA, how to build production-grade RAG systems with FAISS and Hugging Face Datasets, and how to orchestrate complex workflows that combine reasoning, retrieval, and action. Every chapter includes runnable end-to-end examples in Python and PyTorch, best practices for scaling and security, and expert guidance you can immediately apply to real-world projects.
From model internals to deployment patterns, from multimodal pipelines to agentic automation, this is the most complete practical guide you will find on transforming Hugging Face technology into powerful, reliable, production-ready systems.
What You Will Learn
How Transformers, embeddings, tokenization, and attention mechanisms actually work under the hood.
How to fine-tune LLMs efficiently using LoRA, QLoRA, and PEFT workflows.
How to build high-performance RAG systems using FAISS, Hugging Face Datasets, and custom retrievers.
How to integrate vision, text, audio, and multimodal models into unified pipelines.
How to design and deploy agentic systems that retrieve, reason, plan, and take actions.
How to scale inference with quantization, batching, and optimized serving strategies.
How to deploy your models using containerized, serverless, or GPU-backed environments.
How to apply safety, governance, and robust evaluation throughout the model lifecycle.
Who This Book Is For
This book is for professionals who want to build real AI systems, not toy demos. Ideal for:
AI engineers, full-stack developers, data scientists, machine learning practitioners, and technical founders looking to leverage Transformer and LLM technologies in production environments.
If you know Python and understand the basics of machine learning, you are ready to master the world of Hugging Face and modern LLM development.
Why This Book Is Different
Most AI books stop at simple examples. This one takes you all the way to production:
Scalable architectures, deployment patterns, agentic workflows, observability, security, evaluation, responsible AI, and the full engineering lifecycle. You learn not just how to build modern AI systems-but how to make them reliable, safe, and performant at scale.
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