Module 10Transformer Deep Dive
Capstone: Mini-GPT Build
Assemble tokenizer, embeddings, blocks, training, and sampling into one working model.
Why this module matters
This is where transformer theory turns into a system you can inspect and trust.
Prerequisites
- ▸ All previous transformer modules
Learning objectives
- ▸ Train and sample from a tiny GPT
- ▸ Inspect failure modes
- ▸ Connect architecture, optimization, and generation quality
Core concepts
End-to-end decoder pipeline
Sampling diagnostics
Checkpoint analysis
Hands-on practice
- ▸ Train on a small text corpus and compare checkpoints
Expected output
A working mini-GPT project with training and inference scripts.
Study checklist
- ✅ Train and sample from a tiny GPT
- ✅ Inspect failure modes
- ✅ Connect architecture, optimization, and generation quality
Common mistakes
- ⚠️ Overfitting tiny data and calling it success
- ⚠️ Ignoring sampling parameters
- ⚠️ No qualitative evaluation of outputs
Module rhythm
- 1. Read the summary and why-it-matters section first.
- 2. Work through concepts before rushing into practice.
- 3. Use the checklist to verify real understanding, not just completion.
How to continue
You are now ready to study modern transformer variants and system-level tradeoffs.
Back to course overview →How to use this page well
Treat each module as a compact learning system: understand the intuition, verify the concepts, do one hands-on task, then use the checklist and mistakes section to pressure-test your understanding.