Build a DeepSeek Model (From Scratch)

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A book about implementing DeepSeek-style LLM architecture, training, and distillation methods.

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About Build a DeepSeek Model (From Scratch)

Build a DeepSeek Model (From Scratch) is a book that guides you through recreating a laptop-scale version of the DeepSeek LLM. Learn to implement core architectural innovations including Multi-Head Latent Attention, Mixture-of-Experts layers, Multi-Token Prediction, and FP8 quantization. The book covers building a production-ready training pipeline, maximizing hardware utilization with parallelism strategies like DualPipe, applying post-training methods such as supervised fine-tuning and reinforcement learning, and compressing/distilling large models for deployment. Written by Raj Abhijit Dandekar, Rajat Dandekar, Sreedath Panat, and Naman Dwivedi, this MEAP (Manning Early Access Program) title provides chapters as they are written, with source code available on GitHub.

Key Features

Implement Multi-Head Latent Attention and Mixture-of-Experts layers

Build production-ready training pipeline with Multi-Token Prediction and FP8 quantization

Maximize hardware utilization with DualPipe parallelism strategies

Apply supervised fine-tuning and reinforcement learning for reasoning capabilities

Compress and distill large models into smaller, deployable versions

Hands-on implementation with source code on GitHub

Pros & Cons

Pros

Covers the latest innovations from DeepSeek in an accessible, step-by-step format
Includes source code and practical implementation details
Addresses both training and post-training optimization including distillation
Suitable for developers and researchers interested in cutting-edge LLM architecture
Offers early access to content through MEAP program

Cons

Requires prior knowledge of deep learning and transformer models
Not a plug-and-play tool; requires manual implementation
Still in early access (MEAP) with estimated publication in September 2026
Focused on a specific model family, may not cover broader LLM landscape

Best For

Learning to build and train an LLM with advanced techniquesUnderstanding DeepSeek's architectural innovations for AI researchPractical model compression and distillation for real-world deploymentGaining expertise in efficient training and inference of large language models

FAQ

What is covered in this book?

The book covers implementing DeepSeek's core innovations: Multi-Head Latent Attention, Mixture-of-Experts, Multi-Token Prediction, FP8 quantization, DualPipe parallelism, and post-training methods like supervised fine-tuning, reinforcement learning, and model distillation to create a laptop-scale DeepSeek clone.

What prerequisites are required?

Readers should have familiarity with deep learning and transformer architectures. The book begins with a review of LLM fundamentals before diving into DeepSeek-specific innovations.

Is the book complete?

This is a Manning Early Access Program (MEAP) title. Chapters are released as they are written. As of the last update (June 2026), all chapters are available (100% complete) with estimated publication in September 2026.

Where can I get the source code?

Source code is available on GitHub, linked from the book's resources page.