This repository is a specialized library of distributed orthonormal optimizers for large-scale language model training, implementing the Dion family of algorithms (Dion, Dion2, Dion3, NorMuon, Muon) alongside traditional optimizers (AdamW, Lion). It provides a complete training pipeline including a GPT model, data loaders for FineWeb datasets, and configurable parallelism strategies (FSDP2, TP, DDP). The core innovation lies in communication-efficient orthogonal gradient updates via submatrix selection, Newton-Schulz iterations, and polar decomposition, all wrapped in an async megabatch framework that overlaps collectives with computation. The codebase is well-structured with modular optimizer implementations, extensive tests (including distributed process spawning), and CUDA graph capture support. It is primarily an application library for ML practitioners, but retains a research character through its multiple algorithm variants and academic references. The high engineering quality and active maintenance make it suitable for both production use and further research extensions.