Beyond update dates, freshness signals include referencing recent events, citing current statistics and data, mentioning the current year in context where relevant, and updating examples to reflect current tools and practices. These signals reassure both AI models and human readers that the information hasn't become outdated even if the core topic is relatively stable.
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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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