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Building Small Language Models from Scratch: A Practical Guide

The Illustrated Guide to Building LLMs from Scratch

Most books teach you how to use language models. This one teaches you how to build them yourself, from the ground up.

By the end of this comprehensive 854-page guide, you'll have implemented a complete 283M parameter Qwen3 model trained on real data. You'll understand every component, write every line of code, and train a working language model that generates coherent text.

Why This Book is Different

Unlike tutorials that show you how to use existing models, this book takes you through building every component yourself:

- Implement attention mechanisms from scratch

- Build positional encodings (RoPE) yourself

- Create feed-forward networks and normalization layers

- Write the complete transformer architecture

- Train a real model on the TinyStories dataset

What You'll Build

By working through this book, you'll create a complete Qwen3-based language model with:

- 283M parameters

- Modern architecture: Grouped Query Attention (GQA), RoPE, RMSNorm, SwiGLU

- 32,768 token context length

- Full training pipeline from data preprocessing to model evaluation

What's Inside (14 Comprehensive Chapters)

1. Neural Networks - Build a solid foundation from first principles

2. PyTorch - Master the deep learning framework

3. GPU Computing - Optimize for performance

4. Data - Collect, process, and prepare training data

5. Model Scale - Understand the relationship between size and capability

6. Tokenization & Embeddings - Process text for language models

7. Positional Encodings - Implement RoPE and understand alternatives

8. Attention Mechanisms - Build the heart of transformers

9. KV Cache, MQA, GQA - Optimize attention for efficiency

10. Building Blocks - RMSNorm, SwiGLU, and modern components

11. Building Qwen from Scratch - Complete model implementation

12. Quantization - Make models efficient and deployable

13. Mixture of Experts - Scale with efficiency

14. Training Small Language Models - Complete training pipeline

Key Features

✅ Complete Implementation - Every component built from scratch, no black boxes

✅ Modern Architecture - State-of-the-art techniques (GQA, RoPE, RMSNorm, SwiGLU)

✅ Real Training - Train on the TinyStories dataset with full training loops

✅ Production-Ready Code - All examples work on Google Colab or your local GPU

✅ Comprehensive Coverage - From neural network basics to advanced topics

✅ Hands-On Learning - Understand by doing, not just reading

Perfect For

- Developers who want to understand transformers at a fundamental level

- Researchers building custom language models

- Students learning deep learning and NLP

- Engineers who need to modify and optimize language models

- Anyone tired of using models without understanding how they work

What You'll Gain

- Deep understanding of how transformers work internally

- Practical skills in data processing, training loops, and optimization

- Ability to modify, optimize, and adapt models for your needs

- Real implementation experience with a working trained model

- Foundation that scales from small models to large systems

Technical Details

- Model Size: 283M parameters

- Training Time: ~5-6 hours on NVIDIA A100 (longer on consumer GPUs)

- Memory: ~8GB VRAM required (works on RTX 3060, RTX 3070, RTX 4090)

- Dataset: TinyStories (2.14M examples, ~1GB)

- Framework: PyTorch with Python 3.8+

- Platform: Google Colab compatible (free GPU access)

Includes

- Complete source code for all implementations

- Google Colab notebooks for easy setup

- Detailed explanations of every design decision

- Training scripts and optimization techniques

- Troubleshooting guides and best practices

Prerequisites

- Basic Python programming

- Fundamental machine learning concepts

- No prior transformer experience required—we build that knowledge together

Start Building Today

Stop using language models as black boxes. Start understanding them from the inside out. By the end of this book, you'll have built a working language model yourself—and you'll understand every component that makes it work.

Note: This is a comprehensive 854-page guide. We recommend taking your time with each chapter, especially the foundational early chapters, to build a solid understanding before moving to advanced topics.

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