This repository provides a comprehensive educational resource for understanding, extending, and reproducing the DeepSeek series of large language models. It targets AI enthusiasts with a foundational understanding of LLMs and mathematics, aiming to demystify advanced reasoning techniques and infrastructure innovations within the DeepSeek ecosystem.

How It Works

The project breaks down DeepSeek's advancements into three core areas: Mixture-of-Experts (MoE) architecture, reasoning capabilities, and training infrastructure. It focuses on the innovative methodologies behind DeepSeek's approach to Artificial General Intelligence (AGI) rather than just performance metrics. The content includes detailed explanations of concepts like MoE, reasoning algorithms (CoT, ToT, GoT, Monte Carlo Tree Search), and infrastructure optimizations (FlashMLA, DeepEP, DeepGEMM).

Quick Start & Requirements

• Installation: No explicit installation command is provided in the README. The project appears to be documentation and code examples rather than a directly installable library.
• Prerequisites: Requires a foundational understanding of LLMs and mathematics. Specific code examples may have dependencies on libraries like PyTorch, Hugging Face Transformers, and potentially CUDA for GPU acceleration, though these are not explicitly listed as project-wide requirements.
• Resources: Setup time and resource footprint are not specified but would depend on the complexity of the code examples being run.
• Links:
  • English Readme: https://github.com/datawhalechina/unlock-deepseek/blob/main/README.md
  • Chinese Readme: https://github.com/datawhalechina/unlock-deepseek/blob/main/%E4%B8%AD%E6%96%87%20Readme.md

Highlighted Details

• Detailed breakdown of DeepSeek's MoE architecture, reasoning models (DeepSeek-R1, DeepSeek-R1-Zero), and infrastructure optimizations.
• Comparative analysis with contemporary models like Kimi-K1.5.
• Explanations of key reasoning algorithms such as Chain-of-Thought (CoT), Tree-of-Thoughts (ToT), Graph-of-Thoughts (GoT), and Monte Carlo Tree Search.
• Focus on reproducing DeepSeek-R1 with community efforts.

Maintenance & Community

• Core contributors include individuals from Likelihood Lab, East China University of Science and Technology, Shenzhen University, Guangzhou University, and Zhipu.
• Contribution guidelines and commit message conventions are provided.
• The project acknowledges and lists several key open-source projects it builds upon.

Licensing & Compatibility

• Licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0).
• The non-commercial clause restricts usage in commercial products or services.

Limitations & Caveats

The project is primarily an educational and explanatory resource, not a production-ready library. The CC BY-NC-SA 4.0 license strictly prohibits commercial use. Some sections of the table of contents are marked as incomplete or not yet implemented.