MiniMind drops 26M GPT training stack

MiniMind is less a model race entry than a teaching scaffold, and that’s the point. It packages the full life cycle of a tiny LLM into something readable enough for newcomers and sturdy enough for tinkering.

• –The 2-hour, single-3090 claim is compelling because it lowers the intimidation barrier; it makes "train your own LLM" feel reachable.
• –The repo is broad in scope: tokenizer, pretraining, SFT, LoRA, DPO, PPO/GRPO/SPO, distillation, and YaRN-style long-context work are all in play.
• –Compatibility with `transformers`, `vllm`, `llama.cpp`, `ollama`, and OpenAI-style APIs makes it useful beyond the tutorial phase.
• –Native PyTorch implementations are the selling point for developers who want to understand the mechanics instead of hiding behind abstractions.
• –Third-party explainers and walkthroughs around the project suggest it has already become a reference point for the tiny-LLM crowd.