Build DeepSeek‑V3 from scratch: explore MLA, MoE, RoPE, and MTP innovations with hands‑on training and implementation insights.

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A hands-on tutorial series introducing DeepSeek-V3's architecture by building it from scratch in PyTorch. Covers the four core innovations: Multihead Latent Attention (MLA) for KV cache compression, Mixture of Experts (MoE) for efficient scaling, Multi-Token Prediction (MTP) for richer training signals, and Rotary Positional Embeddings (RoPE) for better positional encoding. This first lesson implements the model configuration dataclass and the RoPE/RMSNorm modules, with a small educational model trained on TinyStories as the target.

DeepSeek-V3 Model: Theory, Config, and Rotary Positional Embeddings

Implementing DeepSeek-V3 Model Configuration and RoPE