Unlock DeepSeek is a project for interpreting, extending, and reproducing the DeepSeek series of work, aimed at a broad community of AI researchers, and dedicated to disseminating DeepSeek's cutting-edge innovative achievements on the path to AGI practice.
In terms of purpose, the project is aimed at AI researchers and learners, providing systematic interpretation and hands-on reproduction of the DeepSeek series of papers, helping to understand DeepSeek's innovative achievements in general large language models, mathematical reasoning, code generation, multimodality, reasoning models (such as DeepSeek-R1), as well as MoE architecture, training infrastructure, and other directions.
The main capabilities include: in-depth paper reading, systematic interpretation of the full series of technical reports from DeepSeek-LLM to DeepSeek-V3.2, DeepSeek-Math, DeepSeek-Coder, DeepSeek-VL, and others; hands-on practice, providing reproduction tutorials and code from scratch, supporting localized reproduction in the Chinese community (such as DeepSeek-R1 and others); technical breakdowns, focusing on key elements such as MoE architecture, model inference and reinforcement learning, data, and Infra; and horizontal comparisons, introducing comparisons with similar work such as Kimi, GLM, and MiniMax, presenting different technical routes on the path to AGI.
In terms of usage, the materials do not provide specific installation commands or running steps, and only state that the project is hosted on GitHub (https://github.com/datawhalechina/unlock-deepseek），包含复现教程与代码，读者可据此进行学习与复现。
In terms of applicable scenarios, the target readers are: beginners with foundational knowledge of large language models and university-level mathematics, learners who wish to deeply understand deep reasoning, and practitioners who hope to apply reasoning models to practical work.
In terms of limitations, the project is currently in an Alpha internal testing version, is not yet complete, and may contain errors. The project is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. The project thanks open-source projects such as DeepSeek-R1, Open-R1, transformers, pytorch, flash-attn, trl, mini-deepseek-r1, TinyZero, modelscope, and vllm for their work, and lists the project leaders as Luo Xiutao (Likelihood Lab) and Jiang Shufan (Datawhale member).
