How to Build and Fine-Tune a Small Language Model
How to Build and Fine-Tune a Small Language Model
A Step-by-Step Guide for Beginners, Researchers, and Non-Programmers
About the Book
How to Build and Fine-Tune a Small Language Model is a practical, end-to-end guide for anyone who wants to move from AI user to AI builder. Based on real graduate courses, research workshops, and industry bootcamps, this book shows that you don’t need billion-dollar infrastructure to create powerful AI systems—you only need a laptop, curiosity, and the right roadmap.
The book demystifies modern language models by guiding you through the full pipeline:
- understanding transformer architecture step-by-step (you build a working GPT in Chapter 2),
- pretraining and fine-tuning small models on your own data,
- aligning models with DPO,
- and deploying real applications on consumer hardware.
The examples are drawn from real scientific and professional use cases—geohazard analysis, legal document processing, historical text analysis, multilingual customer support, and more—reflecting how this material was used successfully by scientists, researchers, and practitioners in workshops and production settings .
All code is provided as Google Colab notebooks, allowing readers to click, run, and learn without installation or expensive hardware .
By the end, readers will know how to build, train, align, and deploy small language models (125M–350M parameters) with confidence—and more importantly, will understand the trade-offs behind practical AI systems and how to choose the right design for their needs .
This is not a research monograph—it is a builder’s manual. A clear, hands-on path for beginners, researchers, and non-programmers who want to bring AI to their own domain.
About the Author
Table of Contents
- From the Author
- A Journey from “Black Box” to Builder
- AI Use Disclaimer
- Chapter 1: Introduction
- Welcome to the World of Small Language Models
- Why Small Language Models Matter
- Use Cases: When to Use SLMs vs Giant LLMs
- What You’ll Build in This Book
- Modern SLM Architectures: GPT-2 & MiniMind
- Hardware Requirements and Cost Breakdown
- Your Learning Journey: The Path Ahead
- Is This Book for You?
- How to Use This Book
- References & Further Reading (Updated for 2025)
- Chapter 2: Let’s Build GPT from Scratch
- Understanding What You’ll Build Before You Fine-Tune
- Part 1: The Big Picture
- Part 2: Setting Up Your Workspace
- Part 3: Tokenization - Converting Text to Numbers
- Part 4: Train/Validation Split
- Part 5: Understanding Context Windows
- Part 6: Batching for Efficiency
- Part 7: A Simple Baseline - The Bigram Model
- Part 8: Training the Bigram Model
- Part 9: The Mathematical Trick - Self-Attention
- Part 10: Implementing Self-Attention
- Part 11: Building a Complete Transformer Block
- Part 12: The Complete GPT Model
- Part 13: Training the Full Model
- Part 14: Plot Training Loss vs. Epochs
- Understanding What We Built
- Exercises
- Common Issues
- What You Built
- Key Takeaways
- References and Further Reading
- Chapter 3: Quick Start–Fine-Tune Your First Model
- From Building to Customizing
- Where This Fits in Your Learning Journey
- Why Fine-Tune Instead of Building from Scratch?
- Step 1: Open Google Colab
- Step 2: Install Required Libraries
- Step 3: Load a Pre-trained Model
- Step 4: Prepare Your Dataset
- Step 5: Tokenize the Data
- Step 6: Fine-Tune the Model
- Step 7: Save Your Model
- Step 8: Test Your Model
- Step 9: Compare With Original GPT-2
- Understanding What Just Happened
- What You Just Accomplished
- Try It Yourself: Exercises
- Common Issues and Solutions
- Save Your Work
- Complete Code (All-in-One)
- What’s Next: Your Learning Path
- Chapter 4: Dataset Preparation
- From Quick Experiments to Real-World Training
- Where This Fits in Your Learning Journey
- Introduction: Data Is Everything
- Scaling Up: From Toy Data to Production Data
- Part 1: Understanding Tokenization
- Part 2: Practical Tokenization with Hugging Face
- Part 3: Where to Find Training Data
- 🔑 Getting Your Hugging Face Token and Saving It in Google Colab
- Part 4: Cleaning and Formatting Data
- Part 5: Complete Data Pipeline
- Part 6: Real Examples - Three Complete Datasets
- Part 7: Quality Checks
- Part 8: Common Issues and Solutions
- Summary and Best Practices
- What’s Next: The Path Forward
- Key Takeaways
- Chapter 5: Model Architecture & Configuration
- From Understanding to Design
- Where This Fits in Your Learning Journey
- Introduction: Size Matters (But Not How You Think)
- Reflecting on What You’ve Built
- Part 1: Understanding Parameters
- Part 2: The Four Key Configuration Decisions
- Part 3: Pre-configured Architectures
- Part 4: Building Configurable Models
- Part 5: Decision Framework
- Part 6: Advanced Topics
- Chapter 6: Training Loop & Monitoring
- From Configuration to Trained Model
- Where This Fits in Your Learning Journey
- Introduction: Scaling Up Your Training
- Part 1: From Simple to Production Training
- Part 2: Understanding Key Components
- Part 3: Monitoring and Logging
- Part 4: Checkpointing and Recovery
- Part 5: Troubleshooting Common Issues
- Part 6: Complete Training Pipeline
- Summary and Next Steps
- Chapter 7: Evaluation & Benchmarks
- Introduction: How Do You Know If Your Model Is Good?
- Part 1: Understanding Perplexity
- Part 2: Basic Evaluation Metrics
- Part 3: Advanced Metrics
- Part 4: Comparing Against Baselines
- Part 5: Text Generation Quality
- Part 6: Model Readiness Check
- Part 7: Complete Evaluation Summary
- Key Takeaways
- Common Pitfalls to Avoid
- What’s Next
- Chapter 8: Stage 1 - Pre-traini Your Own Language Model from Scratch
- Introduction: The Joy of Building from Zero
- What is MiniMind?
- What You’ll Build in This Chapter
- How This Differs from Previous Chapters
- Key Adaptations for English
- Prerequisites
- What You’ll Learn
- The MiniMind Architecture
- Expected Results
- Chapter Structure
- Getting Started
- Part 1: Data Preparation
- Part 2: Tokenizer Training
- Part 3: Dataset Creation and Tokenization
- Part 4: Model Training from Scratch
- Part 5: Evaluation and Analysis
- Conclusion: You Built Something Real
- Chapter 9: Stage 2 - Supervised Fine-Tuning (SFT)
- Introduction: From Language Understanding to Task Performance
- Part 1: Understanding Supervised Fine-Tuning
- Part 2: Preparing SFT Data
- Part 3: SFT Training Configuration
- Part 4: SFT Dataset and DataLoader
- Part 5: The full SFT Training Script
- Part 6: Testing Your SFT Model
- Part 7: Complete Workflow
- Summary and Key Takeaways
- Chapter 10: Stage 3 - Direct Preference Optimization (DPO)
- Introduction: From Instruction Following to Alignment
- Part 1: Understanding DPO
- Part 2: Preparing Preference Data
- Part 3: DPO Training Implementation
- Part 4: DPO Configuration and Training
- Part 5: Evaluating Alignment
- Part 6: Safety and Production Deployment
- Part 7: Complete Pipeline Summary
- Practical Exercise: Complete Pipeline
- Resources and Further Reading
- Troubleshooting FAQ
- Conclusion
- Code Repository
- Chapter 11: Production Deployment
- Introduction: From Training to Production
- What You’ll Learn
- Part 1: Understanding the Optimization Landscape
- Part 2: Model Quantization
- INT8 Quantization (Recommended Starting Point)
- Part 3: Deployment Options
- Part 4: Cost Analysis
- Part 5: Deployment Decision Tree
- Part 6: Best Practices Summary
- Summary and Next Steps
- Resources
- Chapter 12: Complete Production Projects & Ethics
- Introduction: From Learning to Building
- Project 1: Medical Q&A Assistant
- Project 2: Code Documentation Generator
- Project 3: Multilingual Customer Support Assistant
- Part 4: Ethics, Safety & Responsible AI
- Part 5: Deployment Best Practices & Conclusion
- Key Takeaways
- Common Pitfalls to Avoid
- The Path Forward
- Resources for Continued Learning
- Final Thoughts
- Appendices (Ch 13)
- Appendix A: Resource Calculator
- Appendix B: Quick Reference
- Appendix C: Dataset & Model Zoo
- Acknowledgements
- From the Author
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