Project Title

minimind — Train a 26M-parameter GPT from Scratch in Just 2 Hours

Overview

minimind is an open-source project that enables developers to train a 26M-parameter GPT model from scratch in just 2 hours, at a minimal cost. It offers a lightweight alternative to large language models, making it accessible for personal GPUs. The project provides a comprehensive guide to training, including data cleaning, pre-training, fine-tuning, and model distillation, all implemented in PyTorch without third-party library dependencies.

Key Features

Train a 26M-parameter GPT model from scratch in 2 hours
Lightweight model, 1/7000th the size of GPT-3
Open-source code for the entire training process, including data cleaning and model distillation
No dependency on third-party libraries for core algorithms
Supports training on single GPU setups

Use Cases

Researchers and developers looking to understand and experiment with large language models
Educational purposes for teaching the fundamentals of language model training
Rapid prototyping of language models for specific applications without the need for massive computational resources

Advantages

Extremely cost-effective and time-efficient training process
High accessibility for individuals with limited resources
Full transparency and control over the training process due to the absence of third-party abstractions
Potential for customization and further development by users

Limitations / Considerations

The "2-hour" claim is based on using specific hardware (NVIDIA 3090), and results may vary on different setups
While designed for personal GPUs, training such models still requires a significant amount of computational power
The project is in active development, and some features might be in the experimental phase

Hugging Face Transformers: A widely-used library for state-of-the-art NLP models, offering a high level of abstraction and ease of use. minimind differs by providing a more hands-on approach to training from scratch.
EleutherAI's GPT-Neo: An open-source implementation of a smaller version of GPT, which shares the goal of making large language models accessible but does not focus on the ultra-low cost aspect that minimind emphasizes.
LLMs like GPT-3: These are much larger models that require significant computational resources to train, making them less accessible to individuals compared to minimind.

Basic Information

GitHub: https://github.com/jingyaogong/minimind
Stars: 25,617
License: Unknown
Last Commit: 2025-09-05

📊 Project Information

Project Name: minimind
GitHub URL: https://github.com/jingyaogong/minimind
Programming Language: Python
⭐ Stars: 25,617
🍴 Forks: 3,035
📅 Created: 2024-07-27
🔄 Last Updated: 2025-09-05

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minimind

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