Small models that do real work on small devices.
We take an existing base model and attach what it needs — trained office skills, a fact-table sidecar, a deterministic arithmetic harness.
Nothing here is a general assistant. Every model is a measured instrument, published at every quantization rung we ship, with each rung's score on the same frozen instrument. When a rung degrades, the card says so.
The Dose-Window Hypothesis — the paper behind these claims: order, calibration, and externalised knowledge in small language models.
The models
Two coworkers, one contract
The default: 354M, one JSON envelope, decks / sheets / documents / grounded refusal. Runs on a laptop at Q8_0.
The quality-max sibling: same recipe, 3.4× parameters — bought for full-document docx renovation on real files.
The method
Three measured claims
Reading the numbers
Every score recomputes
Each figure on every card comes from a frozen, committed instrument and saved generations — oracle-graded, shortcut-checked, with the failure walls printed next to the wins. The quant ladders are measured through llama.cpp itself, because deployment truth beats spec sheets.
The set ships complete: the 3,258-row training table, both models at every measured rung, and the open-source cowork CLI (constrained decode, exact --compute arithmetic, artifact rendering, the Tabula sidecar). Instrument hashes are published; instrument contents stay frozen and unpublished.