DeepSeek-V3 Model Architecture: Educational Deep Dive

DeepSeek-V3 Model Architecture: Educational Deep Dive

Overview

This notebook provides an educational exploration of the DeepSeek-V3 transformer architecture breaking down each component with visualizations and examples using randomly generated data to understand how the model works under the hood.

1. Model Architecture Overview - High-level structure
2. Attention Mechanisms - Multi-head and grouped-query attention
3. Feed-Forward Networks - MLP layers and activations
4. Layer Normalization - RMSNorm implementation
5. Positional Encodings - RoPE (Rotary Position Embedding)
6. Mixture of Experts (MoE) - Expert routing and selection
7. Model Scaling - Different model sizes (16B, 236B, 671B)
8. Inference Pipeline - How text generation works

This notebook is for:

• 📖 Educational demonstrations of DeepSeek-V3 Model Architecture.

📂 GitHub Repository: DeepSeek-V3-Model-Architecture