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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.
GC thrashing in SSR: Batched chunks (Uint8Array[]) amortize async overhead. Sync pipelines via Stream.pullSync() eliminate promise allocation entirely for CPU-bound workloads.