How to Build and Fine-Tune a Small Language Model A Step-by-Step Guide for Beginners, Researchers, and Non-Programmers Build your own AI—without a PhD, expensive hardware, or industry-level resources. Whether you’re a beginner, a student, a scientist, or a domain expert, this book shows you how to create, train, fine-tune, and deploy Small Language Models (SLMs) that truly understand your field. Most AI books explain what models are. This one teaches you to build them. You’ll go from zero to a working GPT-style model, then learn how to fine-tune, align, evaluate, and deploy it for real applications. Why This Book Is Different This is a hands-on builder’s manual designed for real beginners and practical users. Everything is tested through university courses, workshops, and real production deployments. You will be able to: Build a GPT model from scratch Train real models using free/low-cost Google Colab Pretrain your own MiniMind SLM Fine-tune with Supervised FT Align with Direct Preference Optimization (DPO) Deploy models privately and efficiently All chapters include ready-to-run Google Colab notebooks. Inside the Book Part I – Foundations (Ch. 1–3) Why SLMs matter Build a complete GPT from scratch Fine-tune GPT-2 in under 30 minutes Learn tokenization, attention, batching, and training loops Part II – Training from Scratch (Ch. 4–7) Prepare real datasets Configure architecture and size Train 125M–350M parameter models Evaluate with perplexity and benchmarks Troubleshoot training issues Part III – MiniMind Pipeline (Ch. 8–10) A modern 3-stage workflow: Pretraining Supervised Fine-Tuning (SFT) Direct Preference Optimization (DPO) Part IV – Production & Ethics (Ch. 11–12) Quantization: INT8, 4-bit, GPTQ Deploy on Mac, PC, server, or cloud Cost breakdowns (from $0 to