The Complete Guide to Transformer Architecture
Pure JavaScript • No Dependencies • Learn by Building
This diagram shows the complete data flow through a Transformer model.
SUBSTRATE is a complete learning system for understanding Large Language Models. You build every component from scratch - no magic frameworks hiding the details.
What you'll build:
- Tokenizer: text to numbers
- Embeddings: numbers to vectors
- Attention: the breakthrough mechanism
- Transformer blocks: core units
- Full LLM: complete GPT model
- Training: learning algorithm
- Inference: text generation
- Visualizations: understanding the model
substrate/
├── 01_tokenizer/
├── 02_embeddings/
├── 03_attention/
├── 04_transformer_block/
├── 05_full_model/
├── 06_inference/
├── 07_visualizations/
├── notebooks/
└── README.md
- Clone the repository
git clone https://github.com/codewithfourtix/substrate.git
cd substrate- Start with Module 1
Open 01_tokenizer/README.md- Run the complete simulation
Open notebooks/01_full_simulation.md| Module | Topic | Duration | Difficulty |
|---|---|---|---|
| 01 | Tokenization | 15 min | Beginner |
| 02 | Embeddings | 20 min | Beginner |
| 03 | Attention | 45 min | Intermediate |
| 04 | Transformer Block | 30 min | Intermediate |
| 05 | Full Model | 25 min | Intermediate |
| 06 | Inference | 20 min | Beginner |
| 07 | Visualizations | 15 min | Beginner |
Convert text into numbers that models can process.
"hello" → [7, 2, 3, 3, 4]
Key concepts:
- Vocabulary: Dictionary of all tokens
- Token IDs: Unique numbers for each token
- Padding: Make sequences same length
Convert token IDs into dense vectors that capture meaning.
Token 5 → [0.2, -0.1, 0.5, 0.3, ...]
Plus positional encoding: Tell the model word position using sine/cosine.
The breakthrough mechanism. Tokens look at other tokens and learn relationships.
Attention(Q, K, V) = softmax(Q * K^T / sqrt(d_k)) * V
Multi-head attention: 8+ heads learning different patterns simultaneously.
Causal masking: During generation, can only look at previous tokens.
Stack of: Attention → LayerNorm → FeedForward → LayerNorm
Repeated N times (2 for small models, 96 for GPT-3).
Model predicts next token. Loss decreases as it learns.
Generate text one token at a time using:
- Greedy decoding: fastest
- Beam search: better quality
- Temperature sampling: creative
The architecture in SUBSTRATE is identical to:
- GPT-2 (1.5B parameters)
- GPT-3 (175B parameters)
- BERT
- ChatGPT
- LLaMA
The only differences: size, training data, hardware.
- Read 01_tokenizer/README.md
- Study the code
- Run notebook 01_full_simulation.md
- Build your own model
- 01_full_simulation.md - Complete walkthrough
- 02_attention_deep_dive.md - Understand attention deeply
- 03_train_on_tiny_shakespeare.md - Train on real data
How long does this take? 3 hours for complete understanding.
Do I need GPU? No, runs on CPU.
Can I train on my data? Absolutely, just replace the training data.
Is this production-ready? No, it's for learning. Real systems use PyTorch/TensorFlow.
What's the hardest part? Attention mechanism. But once you understand it everything clicks.
- How tokenization works
- How embeddings capture meaning
- How attention lets tokens communicate
- How transformers process text
- How to train an LLM
- How generation works
- How to debug LLM issues