1 Introduction to DeepSeek

Large language models now write, reason, and code with striking fluency—and this book invites you to build one yourself. Using DeepSeek as a guiding case, it blends theory and code so you can reconstruct a state-of-the-art open-source LLM from the ground up. Framed as a hands-on, accessible journey inspired by a popular instructional series, the chapter motivates DeepSeek as a landmark in the democratization of AI and sets the expectation that by building each piece, you will gain durable, first-principles understanding rather than surface familiarity.

The chapter explains why DeepSeek is pivotal: it showed that an open model can match the best proprietary systems while being trained at a fraction of the cost. Technically, DeepSeek modernizes the Transformer by replacing standard attention with Multi-Head Latent Attention (MLA) to cut memory and latency for long contexts, and swapping the feed-forward block with a Mixture-of-Experts (MoE) to expand capacity efficiently. Training advances include Multi-Token Prediction (MTP) for faster learning and inference and FP8 quantization for compute efficiency, complemented by an overlapping pipeline (e.g., DualPipe) to keep hardware saturated. Post-training elevates the base DeepSeek-v3 to DeepSeek-R1 through a sequence of reinforcement learning, rejection sampling–based self-labeling, blended supervision, and a final RL phase, culminating in benchmark-competitive reasoning. Knowledge distillation then transfers capabilities into compact 1.5B–70B checkpoints, making high performance broadly accessible.

Finally, the chapter outlines the book’s four-stage roadmap and scope. Stage 1 establishes the Key-Value cache foundations that make MLA intuitive; Stage 2 implements MLA and MoE; Stage 3 adds MTP, FP8, and efficient scheduling; Stage 4 covers supervised fine-tuning, RL, and distillation. You will learn to design, implement, and reason about these components, but the book does not reproduce proprietary data, exact weights, hyperscale distributed training, or production serving concerns. Prerequisites include intermediate Python, basic deep learning and PyTorch, and familiarity with Transformers; the projects are sized for a laptop or a single 8–12GB GPU (with 24–48GB enabling larger MoE experiments), and reproducible environments are provided. The goal is a cohesive mini-DeepSeek that you understand deeply enough to adapt and extend.

A simple interaction with the DeepSeek chat interface.

The title and abstract of the DeepSeek-R1 research paper.

A detailed view of a standard Transformer block, the foundational architecture used in models like LLaMA and the GPT series. It is composed of a multi-head attention block and a feed-forward network (NN).

A simplified view of the DeepSeek model architecture. It modifies the standard Transformer by replacing the core components with Multi-Head Latent Attention (MLA) and a Mixture-of-Experts (MoE) layer. This design also utilizes RMS Norm (Root Mean Square Normalization) and a specialized Decoupled RoPE (Rotary Position Embedding).

An illustration of the DualPipe training pipeline on a single device. By overlapping the forward pass (the initial blocks), backward pass (the hatched blocks), and combined computations, this scheduling strategy minimizes GPU idle time and maximizes hardware utilization during large-scale training.

The multi-step post-training pipeline used to create DeepSeek-R1 from the DeepSeek-V3 base model. This process involves a combination of reinforcement learning (Pure RL), data generation (Rejection sampling), and fine-tuning to instill advanced reasoning capabilities.

Benchmark performance of DeepSeek-R1 against other leading models (as of January 2025).

The concept of knowledge distillation. A large, powerful "teacher" model (like DeepSeek-R1) is used to generate training data to teach a much smaller, more efficient "student" model, transferring its capabilities without the high computational cost.

The four-stage roadmap for building a mini-DeepSeek model in this book. We will progress from foundational concepts (Stage 1) and core architecture (Stage 2) to advanced training (Stage 3) and post-training techniques (Stage 4), implementing each key innovation along the way.

• Large Language Models (LLMs) have become a dominant force in technology, but the knowledge to build them has often been confined to a few large labs.
• DeepSeek marked a pivotal moment by releasing open-source models with performance that rivaled the best proprietary systems, demonstrating that cutting-edge AI could be developed and shared openly.
• This book will guide you through a hands-on process of building a mini-DeepSeek model, focusing on its key technical innovations to provide a deep, practical understanding of modern LLM architecture and training.
• The core innovations we will implement are divided into four stages: (1) KV Cache Foundation, (2) Core Architecture (MLA & MoE), (3) Advanced Training Techniques (MTP & FP8), and (4) Post-training (RL & Distillation).
• By building these components yourself, you will gain not just theoretical knowledge but also the practical skills to implement and adapt state-of-the-art AI techniques.

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