how-to-train-your-gpt

🧠 How to Train Your GPT

A guide to building a world-class language model from absolute scratch. Taught like you're five. Built like you're an engineer.

I made this with the goal of learning something I didn't understand completely. Specifically the attention part. I use AI a lot to understand key concepts and verifying them.

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📖 What Is This?

This is a 12-chapter, 7,500+ line interactive textbook that teaches you how to build, train and run a modern language model from absolute scratch. The same family of architecture behind ChatGPT, Claude, LLaMA and Mistral.

Alongside the chapters there are 28 standalone topic explainers covering every technique in depth. RoPE, attention, RMSNorm, SwiGLU, KV cache, AdamW, mixed precision and more. Plus two narrative walkthroughs that trace a single sentence through the entire model step by step. Each file follows the same style: child language, no jargon, a code example you can run.

You won't just read about Transformers. You'll write every line yourself: tokenizer, embeddings, attention, training loop, inference engine. Every single line annotated to explain what it does and why it's there.

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🤔 Why This Exists

Most ML tutorials fall into one of two traps:

❌ Too Shallow	❌ Too Academic	✅ This Guide
model = GPT().fit(data)	40-page papers, dense notation	5-year-old analogies → full working code
You learn to call APIs	Assumes PhD in ML	Zero ML experience required
No understanding of internals	No worked examples	Every line annotated with WHAT & WHY

The goal: After finishing, you won't just know that attention "works". You'll understand the variance argument behind 1/√d_k. How RoPE captures relative position through rotation. Why pre-norm beats post-norm for deep networks. And exactly where every gradient flows during backpropagation.

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👥 Who Is This For?

🧑‍💻 You Are...	📚 You Need...
A Python developer curious about how ChatGPT actually works	Basic Python (functions, classes, lists). No ML experience
A student who wants to deeply understand Transformers	Willingness to read ~3,500 lines of commented code
An engineer evaluating LLM architectures	Understanding of tradeoffs (RoPE vs learned, RMSNorm vs LayerNorm)
Someone who got lost at "attention" in other tutorials	Party analogy + worked numeric example with real numbers

🔧 Prerequisites: Python basics (variables, functions, classes, pip install). That's it. No calculus, no linear algebra, no PyTorch experience required. We teach those as we go.

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🗺️ Chapters

Chapter	What You'll Learn
0: Overview	What is a GPT? The big picture
1: Setup	Install tools, GPU vs CPU, venv, PyTorch basics
2: Tokenization	BPE walkthrough: how "unbelievably" becomes tokens
3: Embeddings	How numbers become meaning. king − man + woman = queen
4: Positional Encoding	RoPE: why LLaMA rotates vectors, not adds numbers
5: Attention	⭐ THE CORE. Q,K,V, scaling, causal mask, 8-step walkthrough
6: Transformer Block	RMSNorm, SwiGLU, residuals, pre-norm vs post-norm
7: Complete GPT Model	151M parameter model (with SwiGLU), weight tying, logits explained
8: Training Pipeline	Cross-entropy, backprop, AdamW, cosine warmup, mixed precision
9: Inference	KV cache, temperature, top-k/p, beam search, repetition penalty
10: Full Script	Runnable main.py: everything in one file
11: Glossary	Architecture provenance table, parameter breakdown

⭐ Start with Chapter 0 and read sequentially. Each builds on the previous.

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🏗️ What You'll Build

🧩 Component	📝 Lines	💡 What You'll Understand
BPE Tokenizer	~60	How GPT-4 splits "unbelievably" → "un" + "believ" + "ably"
Embeddings	~30	How "cat" and "dog" end up near each other in 768D space
RoPE	~70	Why LLaMA rotates vectors instead of adding position numbers
Multi-Head Attention	~120	The exact 8-step computation behind every modern LLM
Transformer Block	~50	Why residual connections are the "gradient highway"
Full GPT Model	~200	151M parameter model with SwiGLU, weight tying and pre-norm
Training Pipeline	~250	AdamW, cosine warmup, mixed precision, gradient accumulation
Inference Engine	~80	KV cache, temperature, top-k/p, beam search

💎 ~860 lines of core model code, ~2,600 lines of explanation and diagrams

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🏛️ Architecture

This guide implements the latest publicly-documented decoder-only Transformer:

🧬 Technique	📦 Source Model	⚡ Why It Matters
RoPE	LLaMA, Mistral, Qwen	Relative position without learned parameters
RMSNorm	LLaMA, Mistral, Gemma	15% faster than LayerNorm, equally effective
SwiGLU	PaLM, LLaMA, Gemini	Learns which information to pass or block
Pre-Norm	GPT-3, all modern	Stable training at 100+ layers
AdamW	GPT-3+	Better generalization than vanilla Adam
BPE	GPT-2/3/4	Handles any text. Even unseen words and emoji
Weight Tying	GPT-2/3	Saves 30% parameters, improves training signal
Mixed Precision	All production LLMs	2× speed, half memory, same quality

ℹ️ GPT-4 and Claude architectures are proprietary/undisclosed. This teaches the best publicly-confirmed architecture: what LLaMA 3, Mistral and Qwen 2.5 use.

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🚀 Quick Start

# 1. Clone
git clone https://github.com/raiyanyahya/how-to-train-your-gpt.git
cd how-to-train-your-gpt

# 2. Create environment
python -m venv gpt_env
source gpt_env/bin/activate          # Mac/Linux
# gpt_env\Scripts\activate           # Windows

# 3. Install dependencies (CPU version. For GPU see below)
pip install torch tiktoken datasets numpy matplotlib --index-url https://download.pytorch.org/whl/cpu

# Or use the requirements file
pip install -r requirements.txt

# 4. Verify GPU (optional but recommended)
python -c "import torch; print(f'CUDA: {torch.cuda.is_available()}')"

# 5. Start reading!
open chapters/00_overview.md

Run the training script:

python main.py

This uses the tiny config (d_model=256, 4 layers) by default. Training takes a few minutes on CPU. For the GPT-2 scale config (151M params, 768 dims, 12 layers), edit the config in main.py and uncomment the larger configuration.

💻 The default config uses a tiny model (d_model=256, 4 layers, 17M params) that runs in minutes on CPU. For the full GPT-2 scale (151M params, 768 dims, 12 layers), edit the config in main.py and uncomment the larger configuration. You'll need a GPU for that one.

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📓 Jupyter Notebooks

Alongside the textbook, each chapter has a companion notebook you can run live. These strip away the explanations and give you pure, clean code that executes from top to bottom. If the textbook teaches you why, the notebooks let you see it happen.

We're going to run this whole project on a very small dataset so you can watch training happen in minutes rather than weeks. Every notebook is self-contained. Open it, run all cells and you'll see the model learn in real time.

# Install everything you need
pip install jupyter tiktoken torch numpy datasets matplotlib --index-url https://download.pytorch.org/whl/cpu

# Start with chapter 2 (tokenization)
jupyter notebook notebooks/02_tokenization.ipynb

Notebooks live in the notebooks/ directory, one per chapter. Open any of them and hit Cell → Run All.

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📚 Topic Explainers

Each concept in this guide has a dedicated deep dive inside explanations and examples WIP/. These are written in the simplest possible language. No jargon. No formulas before analogies. Every explainer covers what, where, why, when and how with a code example you can run.

The last two files are narrative walkthroughs. A Token's Journey follows one sentence through the entire model. The Complete Story covers every component across 22 parts. Read these after the chapters to see how everything connects.

Topic	File	What It Covers
RoPE	rope.md	How word order is encoded through rotation
Attention	attention.md	Step by step with a 3-token worked example
BPE Tokenization	bpe_tokenization.md	How text becomes tokens
Embeddings	embeddings.md	How numbers become meaning
RMSNorm	rmsnorm.md	Simpler faster normalization
SwiGLU	swiglu.md	The gated activation that beat ReLU
Causal Masking	causal_masking.md	No peeking at the future
Residual Connections	residual_connections.md	The gradient highway
KV Cache	kv_cache.md	Making generation fast
Sampling	sampling.md	Temperature, top-k, top-p
Mixed Precision	mixed_precision.md	Speed without sacrifice
AdamW	adamw.md	The optimizer that trains LLMs
Weight Tying	weight_tying.md	Two jobs one matrix
Gradient Clipping	gradient_clipping.md	Preventing training explosions
Cosine Warmup	cosine_warmup.md	The learning rate schedule
Pre-Norm	pre_norm.md	Where to normalize
Grouped Query Attention	grouped_query_attention.md	MHA vs GQA vs MQA explained
Flash Attention	flash_attention.md	How Flash Attention makes training 4× faster
Loss Curves	how_to_read_loss.md	Diagnose training problems from the loss curve
Mixture of Experts	mixture_of_experts.md	How MoE scales models with sparse routing
Speculative Decoding	speculative_decoding.md	2-3× faster generation with a draft model
Perplexity	perplexity.md	The one number that measures your model
Beam Search	beam_search.md	Generate more accurate text with multiple candidates
Cheatsheet	cheatsheet.md	Every formula and hyperparameter in one place
FAQ	faq.md	Troubleshooting common problems
Encoder vs Decoder	encoder_decoder_architectures.md	GPT vs BERT vs T5 explained
📖 A Token's Journey	a_tokens_journey.md	Follow one sentence through every layer
📖 The Complete Story	the_complete_story.md	The full narrative: 22 parts, 7800 words

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📖 How to Read

Each chapter follows the same 4-step structure:

Step	Format	Purpose
1️⃣ Analogy	Plain English, 5-year-old level	Build intuition before math
2️⃣ Worked Example	Real numbers traced through	See exactly what happens
3️⃣ Annotated Code	Every line: WHAT + WHY	Understand every decision
4️⃣ Diagram	Mermaid flowchart or ASCII	Visualize data flow

💡 Tip: Lost in the code? Jump back to the analogy. Confused by the math? Skip to the worked example.

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✨ What Makes This Different

Aspect	😴 Typical Tutorial	🔥 This Guide
Explanation depth	"Attention helps the model focus"	8-step worked example with real numbers + variance math + causal mask visualization
Code comments	Few or none	Every single line: WHAT + WHY
Modern techniques	GPT-2 style (2019)	LLaMA 3 style (2024): RoPE, RMSNorm, SwiGLU
Training	Uses HuggingFace Trainer	Full custom loop: AdamW, cosine warmup, mixed precision, grad accumulation
Inference	model.generate()	Temperature, top-k, top-p, beam search, KV cache explained
Target audience	ML engineers	Python developers with zero ML experience
Diagrams	None	Mermaid flowcharts + ASCII matrices + worked examples

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🎯 Skills You'll Gain

• ✅ Explain how GPT-4 tokenizes text using BPE
• ✅ Understand why RoPE, RMSNorm and SwiGLU replaced older techniques
• ✅ Compute attention scores manually for a 3-token sentence
• ✅ Debug a Transformer training loop (loss spikes, flat lines, overfitting)
• ✅ Choose sampling parameters (temperature, top_k, top_p) for different use cases
• ✅ Understand why KV caching is critical for production inference
• ✅ Read modern ML papers with confidence (you'll recognize every component)

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🔮 Next Steps After Finishing

Experiment	What to Change	What You'll Learn
Bigger model	num_layers 12 → 24	How depth improves reasoning
More data	Add BookCorpus, C4, The Pile	Impact of data quality and diversity
Flash Attention	Install flash-attn, swap attention	2-5× faster training, longer context
Grouped Query Attention	Set num_kv_heads < num_heads	How Mistral achieves efficient inference
LoRA fine-tuning	Add low-rank adapter layers	Customize models without full retraining
RLHF / DPO	Add reward model training	How ChatGPT learns to follow instructions
KV Cache	Implement persistent key-value storage	500× faster text generation
Mixture of Experts	Route tokens through different FFN experts	How GPT-4 scales to trillions of params

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📁 File Structure

📦 how-to-train-your-gpt/
├── 📄 README.md              ← You are here
├── 🐍 main.py                ← Runnable training script (clone & run)
├── 📋 requirements.txt       ← One command install
├── 📂 chapters/
│   ├── 🏠 00_overview.md     ← What is a GPT? Why build one?
│   ├── 🔧 01_setup.md        ← Install tools, GPU vs CPU, venv basics
│   ├── 🔪 02_tokenization.md ← BPE walkthrough, EOS tokens, emoji handling
│   ├── 🧊 03_embeddings.md   ← How numbers become meaning, king − man + woman
│   ├── 📍 04_positional_encoding.md ← RoPE math, numerical example, theta
│   ├── 🧠 05_attention.md    ← ⭐ THE CORE (713 lines). Q,K,V, scaling, causal mask
│   ├── 🧱 06_transformer_block.md ← RMSNorm, SwiGLU, residuals, pre-norm vs post
│   ├── 🏗️ 07_gpt_model.md    ← Complete 151M model, weight tying, logits explained
│   ├── 🏋️ 08_training.md     ← Cross-entropy, backprop, AdamW, cosine warmup
│   ├── 🎤 09_inference.md    ← KV cache, temperature, top-k/p, beam search
│   ├── 📜 10_full_script.md  ← About main.py
│   └── 📊 11_glossary.md     ← Architecture provenance, parameter breakdown
├── 📓 notebooks/             ← Jupyter notebooks (one per chapter)
│   ├── 🎨 attention_visualized.ipynb ← Watch attention weights in action
│   └── ☁️ colab_train.ipynb  ← One-click cloud training on Colab
├── 🎯 fine-tuning/           ← Fine-tuning guide: LoRA, QLoRA, data prep
│   ├── 📄 README.md
│   ├── 01_what_is_finetuning.md
│   ├── 02_lora_explained.md
│   ├── 03_qlora_explained.md
│   ├── 04_data_preparation.md
│   ├── 05_full_finetune.md
│   └── 📓 notebooks/lora_finetune.ipynb
├── 📚 explanations and examples WIP/ ← Standalone explainers (28 topics)
└── 📄 CONTRIBUTING.md

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