「用初中数学讲明白AI」第4章：搭积木——Transformer是怎么拼出来的

TL;DR - An accessible tutorial explains how attention, feed-forward networks, residual connections, and layer normalization combine into Transformer blocks. It connects these stacked blocks to autoregressive next-token generation in GPT-style models.

• Attention gathers contextual information; feed-forward layers transform it through expansion, GELU activation, and compression.
• Residual connections preserve signals and gradients, while layer normalization stabilizes activations in deep networks.
• Dimension-preserving blocks can be stacked repeatedly, with each layer sharing the architecture but learning distinct parameters.
• GPT models generate text token by token by converting the final hidden state into vocabulary probabilities and repeating the forward pass.