Rank-1 linear, factorized embed, sparse gate, param-free norm, low-rank head, cross-layer sharing
2025年，是德国舍弗勒集团进入中国30周年，该集团在中国的首座工厂就位于太仓。太仓供电局为舍弗勒带来了一份特别的礼物——一张供电热力图谱。
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One challenge is having enough training data. Another is that the training data needs to be free of contamination. For a model trained up till 1900, there needs to be no information from after 1900 that leaks into the data. Some metadata might have that kind of leakage. While it’s not possible to have zero leakage - there’s a shadow of the future on past data because what we store is a function of what we care about - it’s possible to have a very low level of leakage, sufficient for this to be interesting.
The first step is to specify which APIs our application needs. This is done using an IDL called WIT. For our example, we need the Console API. We can import it by specifying the name of the interface.
dest[i] = (offset + i) & 0xFF;