Transformer Layer Architecture Visualizer
Interactive pipeline of a complete Transformer block: Embeddings, RoPE, RMSNorm, Multi-Head Attention, Residual Add, and SwiGLU FFN.
Architecture Breakdown: Transformer Pipeline & Self-Attention
Words enter as raw steel. Station 1 shapes them into parts (Embedding). Station 2 connects matching parts (Attention). Station 3 installs knowledge (Feed-Forward). At the exit, a finished word rolls off!
Early layers learn basic letters and spelling. Middle layers learn sentence grammar and facts. The deepest layers learn multi-step logic and reasoning. Stacking blocks creates wisdom!
Click any colored block in the pipeline diagram below to inspect what happens at that specific station in the Transformer architecture.
4. Multi-Head Self-Attention
What happens at this stage
Tokens compute Query-Key dot products and dynamically aggregate information across the sequence.
Why it matters in LLM design
This is where context is blended across tokens (e.g. resolving pronouns and syntax).
Quick Reference & Instructions
Simple steps, pro tips, and execution details
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Instant Live Analysis
Calculations and formatting happen automatically with zero delay as you type.
Copy or Use Output
Copy results or apply the clean output directly to your projects.
How It Works
Animates data tensor flow through every sublayer of a transformer decoder block, explaining residual skip connections and normalization.
Frequently Asked Questions
Common questions about calculations, assumptions, and edge cases.
Yes, Transformer Layer Architecture Visualizer is 100% free with unlimited calculations and zero paywalls or subscriptions.
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Interactive Scaled Dot-Product Attention matrix. Select any token to view real-time attention weights across the entire sentence.
Query-Key-Value (QKV) Numerical Playground
Interactive step-by-step matrix laboratory: inspect Q × Kᵀ dot products, division by √d_k, Softmax normalization, and weighted V sums.
LLM Generation Simulator
Watch an LLM generate text step by step: Tokenize → Forward Pass → Softmax → Sampling → Append → Repeat.