Transformer Configuration
Transformer Components
Token Embedding
Convert tokens to dense vector representations (512d)
Positional Encoding
Add sinusoidal position information to embeddings
Multi-Head Attention
Attend to different parts of the sequence (8 heads)
Feed Forward Network
Process attended information through dense layers
Layer Normalization
Normalize activations for stable training
Residual Connection
Add skip connections to prevent vanishing gradients
Processing Status
Current Layer1 / 6
Current StepToken Embedding
0% Complete
Architecture Overview
Token Processing Flow (Layer 1)
Hello
512d vector
world
512d vector
Data flows through 6 operations per layer
Positional Encoding Matrix
Sinusoidal patterns encode position information
Attention Heatmap (Head 1)
Darker colors = stronger attention weights
Feed Forward Network Architecture
Two-layer MLP: 512 → 2048 → 512 with ReLU activation
Model Statistics
2
Input Tokens
8
Attention Heads
512
Hidden Dimensions
7.9M
Parameters (est.)
Current Processing State: Ready to process input through transformer layers.