[GitHub] jingyaogong/minimind

This is an open-source project for educational and research purposes, aiming to demonstrate how to train a small language model from scratch on consumer-grade hardware. Its core is to provide a complete, reproducible pipeline for training a language model with 64 million parameters. The key technical aspects and implementations of the project are as follows: The model's parameter scale is set at 64 million to match personal computing resources. The project offers a full set of Python code and detailed guidelines, ranging from data preprocessing to model training. The training process can be completed in approximately 2 hours on a single NVIDIA RTX 3090 graphics card. The project's code structure is clear, typically including core modules such as data loading, model definition (based on the Transformer architecture), and the training loop. The project's impact is mainly reflected in: it significantly lowers the barrier to understanding and practicing the principles of training large models, enabling researchers, students, and developers to experience and verify the entire process—from data preparation to model convergence—firsthand in a local environment with minimal time and computational costs. It provides a lightweight and efficient practical platform for the teaching and experimentation of large model training.