MiniMind-in-Depth

Why we included this project

MiniMind-in-Depth reads the source of a small working language model line by line and explains what each piece does. The author builds on jingyaogong/minimind and walks through everything from training a tokenizer from scratch and RMSNorm to rotary position embeddings and mixture-of-experts routing, with the input and output shape of every tensor marked so you can trace how data moves through the network. Later chapters cover pretraining, supervised fine-tuning, DPO alignment, LoRA, and distillation, which helps if you want to see how each technique actually plugs into a real training loop rather than reading about it in the abstract. The writing sits somewhere between a guided textbook and well-commented code, so it suits developers who prefer to learn by reading an annotated codebase. It is a study companion, not software to deploy.