Categorize transformer block components by their functional role.
Sort each transformer-block item by its main role. Routes context across tokens Transforms features per position Preserves signal flow Stabilizes activations Self-attention mixes value vectors from other token positions. Q, K, and V projections create compatibility scores and value payloads. The feed-forward or MLP layer applies learned nonlinear transformations at each position. Activation functions inside the MLP help represent nonlinear feature combinations. Residual connections add sublayer outputs back to the existing representation stream. Skip paths make it easier for deep stacks to carry earlier information forward. Layer normalization keeps representation scale controlled around sublayers. Normalization reduces training and inference instability…
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