Build a DeepSeek Model (From Scratch)

Overview

A project-based guide to building a scaled-down version of the DeepSeek LLM, from architecture fundamentals to training. It walks through implementing techniques such as mixture of experts, latent attention, and multi-token prediction on a laptop.

Best for

Best for
Developers and ML engineers who want to understand and replicate the core architectural innovations of DeepSeek on their own machine.

Use cases

• Recreating DeepSeek-style transformer architectures for personal study or experimentation
• Learning to implement mixture-of-experts and latent attention from scratch
• Training a small but functional LLM on local hardware using open-source techniques

Notes

A project-based guide to building a scaled-down version of the DeepSeek LLM, from architecture fundamentals to training. It walks through implementing techniques such as mixture of experts, latent attention, and multi-token prediction on a laptop.

Use cases

• Recreating DeepSeek-style transformer architectures for personal study or experimentation
• Learning to implement mixture-of-experts and latent attention from scratch
• Training a small but functional LLM on local hardware using open-source techniques

Pros

• Focuses on the actual techniques that made DeepSeek cost-efficient, not just theory
• Practical enough to run on a laptop, lowering the barrier to hands-on learning
• Teaches several state-of-the-art strategies in one coherent project

Cons

• Limited to a scaled-down version, so production-scale deployment is not covered
• Requires intermediate knowledge of Python and deep learning fundamentals
• Documentation may lag as DeepSeek’s methods evolve rapidly

Indexed from awesome-generative-ai and enriched against its public facts.