Notes from
the workbench.
Explainers, methods, and lab notes from DeltaTune Lab. Plain language, careful claims.
Why we started DeltaTune LabAn independent AI research lab with a narrow promise: ask focused questions about efficient AI and publish what we learn.
θ′ = θ + Δθ: why small updates matterA plain-language look at parameter-efficient fine-tuning, low-rank adaptation, and the questions DeltaTune Lab wants to test.
How we intend to report resultsBaselines, costs, failure cases and configurations: the checklist every DeltaTune Lab release should pass before it goes public.