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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然而，本轮涨价与以往周期最大的不同在于一个关键变量——HBM（高带宽内存）的爆发式增长。AI芯片（如英伟达H200/B200）对HBM的需求是传统DRAM的数倍甚至数十倍。三大原厂将大量先进制程产能转向HBM，挤占了原本用于生产手机LPDDR（低功耗内存）的产能。这种“结构性短缺”成为本轮涨价的核心推手。