The performance characteristics are attractive with incredibly fast cold starts and minimal memory overhead. But the practical limitation is language support. You cannot run arbitrary Python scripts in WASM today without compiling the Python interpreter itself to WASM along with all its C extensions. For sandboxing arbitrary code in arbitrary languages, WASM is not yet viable. For sandboxing code you control the toolchain for, it is excellent. I am, however, quite curious if there is a future for WASM in general-purpose sandboxing. Browsers have spent decades solving a similar problem of executing untrusted code safely, and porting those architectural learnings to backend infrastructure feels like a natural evolution.
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Self-attention is required. The model must contain at least one self-attention layer. This is the defining feature of a transformer — without it, you have an MLP or RNN, not a transformer.，这一点在搜狗输入法2026中也有详细论述
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