The Practical Guide to Large Language Models: Hands-On AI Applications with Hugging Face Transformers

Build Real LLM Applications with Hugging Face Transformers

If you’re ready to move beyond API calls and understand how to shape large language models for practical tasks, this guide offers a direct path. Ivan Gridin combines conceptual clarity with project-based learning, showing how to leverage the Hugging Face ecosystem to create applications that generate, classify, and interact with text in meaningful ways.

What You’ll Work Through

• Transformer Foundations: Understand what makes modern LLMs tick, from attention mechanisms to full architecture.
• LLM Internals and Evaluation: Explore models like GPT, BERT, RoBERTa, and T5, and learn to select the right model for a given task.
• Improving Chat Responses: Work with base and instruction-tuned models, and use judge-based approaches to pick better outputs.
• Retrieval-Augmented Generation: Add context and external knowledge to your LLM applications using embeddings and vector stores.
• Agent Systems: Design LLM-powered agents that can control program flow and complete multi-step tasks.
• Model Training: Fine-tune models for classification, causal language modeling, and domain adaptation.
• Deep Architecture Dive: Build a transformer from scratch to solidify your understanding of how the pieces fit together.

Hands-On Projects Throughout

The book is packed with practical exercises that turn theory into usable code. You’ll build a simple LLM chat app, an interactive data mining tool, a Hugging Face model mentor assistant, a human resources agent, an Ancient Rome AI assistant, and a transformer from scratch. Each project reinforces the concepts introduced earlier and helps you internalize the workflow of developing real NLP applications.

Who This Book Is For

Developers, data scientists, and AI enthusiasts with some Python experience will get the most out of this guide. If you want to implement LLM solutions rather than just consume cloud APIs, the step-by-step projects and clear explanations will give you a solid foundation. The focus on open-source models and the Hugging Face framework makes the skills adaptable to a wide range of business and research problems.

A Practical, Code-First Approach

Rather than staying purely theoretical, the book emphasizes actually building things. You’ll learn to work with tokenizers, pipelines, checkpoints, and fine-tuning techniques in a structured way. The progression from understanding transformers to deploying RAG and agents means you’ll finish with a toolkit of reusable patterns for your own projects.