Use real rule names and add checkpoint provenance stamping - #34
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…venance stamping model/transformer.py now uses real rule names from vocab.json's rule_tokens when provided, raising loudly on a count mismatch instead of silently mislabeling. Regression test included (3/3 passing). checkpoint_provenance.py stamps every saved checkpoint with git commit hash, config hash, and checkpoint hash, auto-called from train.py after saving -- the exact provenance gap that made the original 43.3%/66.7% numbers and the config/epoch mismatch impossible to trace. Verified: train.py imports cleanly and correctly resolves to the real model/transformer.py implementation (not the LSTM fallback stub in solver_model.py). Does not affect model weights, loss, or the currently-running training job on another machine.
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Merge pull request QuantumLogicsLabs#34 from Momin0000/main
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…venance stamping
model/transformer.py now uses real rule names from vocab.json's rule_tokens when provided, raising loudly on a count mismatch instead of silently mislabeling. Regression test included (3/3 passing). checkpoint_provenance.py stamps every saved checkpoint with git commit hash, config hash, and checkpoint hash, auto-called from train.py after saving -- the exact provenance gap that made the original 43.3%/66.7% numbers and the config/epoch mismatch impossible to trace. Verified: train.py imports cleanly and correctly resolves to the real model/transformer.py implementation (not the LSTM fallback stub in solver_model.py). Does not affect model weights, loss, or the currently-running training job on another machine.