minimind

Developed to make LLM training accessible and understandable, MiniMind provides a complete implementation covering tokenizer training, dataset preparation, model architecture, pre-training, and supervised fine-tuning (SFT), all within a clean and well-documented Python codebase.

The project includes multiple model scales ranging from 26M to 218M parameters, allowing learners to experiment with different capacity trade-offs and observe how scale affects language understanding and generation quality.

MiniMind also implements techniques from recent research including grouped query attention (GQA), mixture of experts (MoE), and direct preference optimization (DPO), exposing practitioners to production-level training methods within an approachable experimental framework.

MiniMind was originally developed in Chinese and has attracted significant international attention for its comprehensive scope and practical focus. The repository includes pre-trained weights, training scripts, evaluation utilities, and a deployment server for inference testing.

It is particularly useful for ML engineers who understand deep learning fundamentals but want hands-on experience with the specific engineering challenges of LLM training data pipeline design, tokenization choices, distributed training patterns, and evaluation methodology.