LESSON 3.8 · Engineering · 120 min
Read GPT-2: configuration, parameter counts, weight loading
Read GPT-2: configuration, parameter counts, weight loading. Learn model configuration, state_dict, and weight transposition and complete: Load official GPT-2 weights and align log
Learning objectives
- Explain what problem “Read GPT-2: configuration, parameter counts, weight loading” solves without hiding behind terminology.
- Trace the variables and causal links across model configuration, state_dict, and weight transposition.
- Complete “Load official GPT-2 weights and align logits” and judge the result with evidence rather than intuition.
Core concepts
model configuration
model configuration 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.
state_dict
state_dict 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 weight transposition
and weight transposition 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
Load official GPT-2 weights and align logits
- 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.