how-to-train-your-gpt README

The project's README from github.com/raiyanyahya/how-to-train-your-gpt. As one markdown file: README.md.

🧠 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.

12 chapters 7,500+ lines 28 topic explainers 100% commented Python basics only LLaMA 3 style Learning only Open In Colab

📖 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.

🤔 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.

👥 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.

🗺️ 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.

🏗️ 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

🏛️ 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.

🚀 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.

📓 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.

📚 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

📖 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.

✨ 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

🎯 Skills You'll Gain

🔮 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

📁 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

"Any sufficiently explained technology is indistinguishable from magic. Until you build it yourself."

⭐ Star this repo if you found it useful | 🐛 Issues & PRs welcome | 📖 Happy learning!

how-to-train-your-gpt (raiyanyahya/how-to-train-your-gpt)

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How-to-train-your-gpt
What is this?A 12-chapter, 7,500+ line interactive textbook that teaches you how to build, train and run a modern GPT-style language model completely from scratch, with every line of code commented and explained in plain English.
Is this a code library or a tutorial I read through?It is an educational resource, not an installable library: chapters of annotated prose plus code, 28 standalone topic explainers, and a companion Jupyter notebook for each chapter so you can run the code as you read.
How do I start working through it?Start at Chapter 0 and read sequentially; run `main.py` to train the default tiny config (dmodel=256, 4 layers), or open the matching notebook in the notebooks/ directory and hit Cell -> Run All.
Do I need an ML background or just Python?Only basic Python (functions, classes, pip install) is required; no calculus, linear algebra or prior PyTorch experience is needed since the guide teaches those as it goes. A GPU is only needed if you switch to the full 151M-parameter GPT-2-scale config.
Does it teach the exact architecture behind GPT-4 or Claude?No, those architectures are undisclosed. It teaches the best publicly-confirmed modern decoder-only architecture instead, the style used by LLaMA 3, Mistral and Qwen 2.5 (RoPE, RMSNorm, SwiGLU, pre-norm).
How is the whole project organized beyond the 12 chapters?Besides the chapters there are 28 topic explainer files (RoPE, KV cache, AdamW, MoE, etc.) and two narrative walkthroughs, 'A Token's Journey' and 'The Complete Story', that trace one sentence through the entire model.
What model do I actually end up training?By default you train a tiny ~17M-parameter model (dmodel=256, 4 layers) that finishes in minutes on CPU; editing main.py switches to the full GPT-2-scale config of 151M parameters, 768 dims and 12 layers, which needs a GPU.
What skills am I expected to gain by the end?You should be able to explain BPE tokenization, compute attention scores manually for a 3-token sentence, debug a training loop, choose sampling parameters, and read modern ML papers recognizing every component described.
Which modern techniques does the implementation actually use?RoPE positional encoding, RMSNorm, SwiGLU activations, pre-norm residual blocks, AdamW with cosine warmup, BPE tokenization, weight tying and mixed-precision training, the same set used by LLaMA 3, Mistral and Gemma.
What are similar projects?LLMs-from-scratch, llama3-from-scratch, OpenArch, nn-zero-to-hero, llm-architecture-from-scratch, minGPT, train-llm-from-scratch, lectures
Which topics is it in?tokenizer, flash-attention, from-scratch, educational, speculative-decoding, mixture-of-experts, attention-mechanism, language-model, llama, natural-language-processing, transformers, gpt
Creator
raiyanyahya
Size
0.3 MB
Stars
4K
Updated
CommitAug 30
Age
5mo
Commits · 90 Days