The model does the work, not the code. The inference code should be generic autoregressive decoding that would work with any transformer checkpoint. If your generation loop contains addition-specific logic — manually pairing digits, threading carry state, indexing into specific positions — then the Python code is solving the problem, not the model.
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Sortformer diarization uses unnormalized features (normalize = false) — this differs from ASR models
Yellow: Backstabber