TRANSFORMER ARCHITECTURE // ATTENTION MECHANISM PARALLEL SELF-ATTENTION & RESIDUAL FEATURE CONDUITS CURRENT FORMULATION Attention(Q,K,V) = softmax(QKᵀ/√dₖ)V PE(pos, 2i) = sin(pos/10000²ⁱ/ᵈ) RAW INPUT TEXT STREAM "Attention Is All You Need — Transformer Architecture Deep Dive" QUERY (Q) W_q · X KEY (K) W_k · X VALUE (V) W_v · X ATTENTION WEIGHT MATRIX [A] Softmax(Q · Kᵀ / √dₖ) TOKEN RELATIONAL ENERGY FLOW Dynamic Head #1 / #8 Multi-Head Self-Attention Linear Projections & Scaled Dot-Product Add & LayerNorm LayerNorm(x + Sub(x)) Feed-Forward Network (FFN) FFN(x) = max(0, xW₁ + b₁)W₂ + b₂ Add & LayerNorm Dense 512 → 2048 → 512 ▲ RESIDUAL HIGHWAY: PRESERVES LOW-LEVEL GRADIENT DYNAMICS ENCODER STACK (Nx=6) Feed Forward Add & Norm Multi-Head Attention Add & Norm Input Embedding + PE K, V DECODER STACK (Nx=6) Feed Forward Cross Multi-Head Attention Add & Norm Masked Multi-Head Attention Output Embedding + PE Linear & Softmax Out LATENT D_MODEL (512-D) High-dimensional Semantic Tensor SEMANTIC SPACE PHASE 1/4 Tokenization & Positional Encoding: Input text slices into discrete embedding vectors with injected sinusoidal harmonics.