LESSON 4.10 · Assessment · 240 min
Mastery Gate 4: from nanoGPT to small pretraining
Mastery Gate 4: from nanoGPT to small pretraining. Learn data, resources, training, and evaluation as one system and complete: Submit budgets, logs, incident review, and a reproduc
Learning objectives
- Explain what problem “Mastery Gate 4: from nanoGPT to small pretraining” solves without hiding behind terminology.
- Trace the variables and causal links across data, resources, training.
- Complete “Submit budgets, logs, incident review, and a reproducible run” and judge the result with evidence rather than intuition.
Core concepts
data
data is part of the lesson’s causal model. State its inputs, outputs, invariants, and failure mode; then verify it with a hand-check or a minimal experiment before moving to an optimized implementation.
resources
resources is part of the lesson’s causal model. State its inputs, outputs, invariants, and failure mode; then verify it with a hand-check or a minimal experiment.
training
training is part of the lesson’s causal model. State its inputs, outputs, invariants, and failure mode; then verify it with a hand-check or a minimal experiment.
and evaluation as one system
and evaluation as one system is part of the lesson’s causal model. State its inputs, outputs, invariants, and failure mode; then verify it with a hand-check or a minimal experiment.
Build and verify
Submit budgets, logs, incident review, and a reproducible run
- Predict: write the expected output, trend, or failure before running code.
- Build: implement only the minimum components needed to answer the question.
- Verify: compare with a baseline or trusted implementation; save seeds, parameters, and raw outputs.
- Transfer: change one shape, dataset, scale, or workload condition and explain whether the conclusion still holds.