minimind

What the project is

minimind is a Python project hosted on GitHub. 🧠 Train a 64M-parameter LLM from scratch in just 2h! 此开源项目旨在完全从 0 开始，仅用 3 块钱成本与 2 小时训练时间，即可训练出规模约为 64M 的超小语言模型 MiniMind。 MiniMind 系列极其轻量，主线最小版本体积约为 GPT-3 的 $\frac{1}{2700}$，力求让普通个人 GPU 也能快速完成训练与复现。 项目同时开源了大模型的极简结构与完整训练链路，覆盖 MoE、数据清洗、预训练（Pretrain）、监督微调（SFT）、LoRA、RLHF（DPO）、RLAIF（PPO / GRPO / CISPO）、Tool Use、Agentic RL、自适应思考与模型蒸馏等全过程代码。 MiniMind 同时拓展了视觉模态模型 MiniMind-V、多模态 Omni 模型 MiniMind-O、扩散语言模型（MiniMind-dLM）、线性模型（MiniMind-Linear），详见 Discussion。 项目所有核心算法代码均从 0 使用 PyTorch 原生实现，不依赖第三方库提供的高层抽象接口。 这不仅是一个大语言模型全阶段开源复现项目，也是一套面向 LLM 入门与实践的教程。 希望此项目能为更多人提供一个可复现、可理解、可扩展的起点，一起感受创造的乐趣，并推动更广泛 AI 社区的进步。 The repository is maintained by jingyaogong and tracks activity through its public issue tracker and commit history. Readers who want a working example rather than a bare library will find the documentation and the linked resources useful for getting a first build running.

What it offers

The minimind repository documents several concrete capabilities that shape how people use it. 此开源项目旨在完全从 0 开始，仅用 3 块钱成本与 2 小时训练时间，即可训练出规模约为 64M 的超小语言模型 MiniMind。 MiniMind 系列极其轻量，主线最小版本体积约为 GPT-3 的 $\frac{1}{2700}$，力求让普通个人 GPU 也能快速完成训练与复现。 项目同时开源了大模型的极简结构与完整训练链路，覆盖 MoE、数据清洗、预训练（Pretrain）、监督微调（SFT）、LoRA、RLHF（DPO）、RLAIF（PPO / GRPO / CISPO）、Tool Use、Agentic RL、自适应思考与模型蒸馏等全过程代码。 MiniMind 同时拓展了视觉模态模型 MiniMind-V、多模态 Omni 模型 MiniMind-O、扩散语言模型（MiniMind-dLM）、线性模型（MiniMind-Linear），详见 Discussion。 项目所有核心算法代码均从 0 使用 PyTorch 原生实现，不依赖第三方库提供的高层抽象接口。 These points are taken from the project README, so they reflect what the maintainers actually ship rather than marketing claims. Checking the file list and the example directories gives a fuller picture of how each piece fits together.

How to set it up

Getting minimind running starts with cloning the source from https://github.com/jingyaogong/minimind. The README lists the commands needed to fetch the code and build or launch it. Representative steps from the documentation include: git clone --depth 1 https://github.com/jingyaogong/minimind git clone https://huggingface.co/jingyaogong/minimind-3 python eval_llm.py --load_from ./minimind-3 python eval_llm.py --load_from ./model --weight full_sft. After the code is on disk, the project instructions walk through configuration and any dependencies. Following the order shown in the README avoids the common setup mistakes that come from mixing steps.

License and project status

minimind is written primarily in Python and is released under the Apache-2.0 license, which sets the terms for reuse and redistribution. The project reports around 54956 stars on GitHub, a signal of how many developers have bookmarked it. The source was last updated on 2026-08-24. The canonical location is https://github.com/jingyaogong/minimind, where the license file, the changelog and the open issues give the most current state of the work. Anyone planning to depend on it should read the license text directly before shipping it inside another product.

Editorial conclusion

The minimind source lives at https://github.com/jingyaogong/minimind under the Apache-2.0 license and is mainly written in Python. The README and the linked examples remain the place to confirm the current behaviour before adopting the project.