Module 8Transformer Deep Dive
Training Recipes That Work
Learn warmup, clipping, precision, and data choices that stabilize transformer training.
Why this module matters
Most failed transformer training runs are recipe failures, not idea failures.
Prerequisites
- ▸ Optimizer basics
Learning objectives
- ▸ Choose learning rates and warmup
- ▸ Use clipping and mixed precision carefully
- ▸ Match model size to hardware budget
Core concepts
Warmup
Gradient clipping
Sequence length vs batch tradeoff
Hands-on practice
- ▸ Compare runs with and without warmup on a small decoder
Expected output
A short training-stability benchmark note.
Study checklist
- ✅ Choose learning rates and warmup
- ✅ Use clipping and mixed precision carefully
- ✅ Match model size to hardware budget
Common mistakes
- ⚠️ No warmup on fragile runs
- ⚠️ Treating OOM as random bad luck
- ⚠️ Ignoring sequence-length cost
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 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.