Abstract

The architecture for training language models natively inside the Hanzo Rust inference stack, eliminating the conversion boundary between a Python training stack and a separate serving engine. Shows the QLoRA primitive already exists in the engine's reverse-mode autograd, and formalizes QLoRA parameter/memory accounting plus additive LoRA-soup composition. Unusually candid: an entire section names the stack's own non-functional stubs to replace.