Complete Transformer Flow
Georgia Tech's Transformer Explainer
This is the absolute gold standard in interactive education. It visualizes the entire neural pathway of a Transformer in real-time, down to individual float values, Softmax charts, Key/Query/Value math, and the Feed-Forward neural expansion. Highly recommended to use alongside these lessons!
🚀 Open Transformer Explainer →Chapter 04 · Transformer: From Token Vectors to Contextual Understanding
Follow the 7 visual stages: Input Text → Tokenization → Token IDs → Embedding Matrix Lookup (1,536D) → Positional Encoding & Multi-Head Self-Attention (Syntax, Entity Relations) → FFN Non-Linear Projection → Contextual Output Tensor.
What is Complete Transformer Flow? Why does it exist? Where does it appear in Transformer?
Draw or describe a visual model of Complete Transformer Flow. What does it look like conceptually?
Work through a concrete manual example of Complete Transformer Flow with real numbers or pseudocode.
Implement Complete Transformer Flow from scratch in Python. Keep it minimal — just enough to see it work.
Try Complete Transformer Flow on a real example. Vary the inputs and observe what changes.
How is Complete Transformer Flow different from related concepts in Transformer? What are the trade-offs?
Explain Complete Transformer Flow in plain language using an analogy. Could a non-expert follow it?
Build a small project or demo that uses Complete Transformer Flow as a core component.
🔨 M-05 transformer.py 🔁 residual pattern → execution checkpointing → return to
tiny_llm/transformer.py
📋 full plan →