tablinum

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chadstthomas  updated a collection 2 days ago
LiteBeam
chadstthomas  updated a collection 2 days ago
LiteBeam
chadstthomas  updated a model 2 days ago
tablinum/lfm2.5-2.6b-litebeam
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Organization Card
Tablinum mark
Tablinum
the record room added to a house that already existed

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.

354M
smallest production coworker
1.000
eval.v1 — stock bases sit at ≈0.24
0.980
m365 premise bench · sidecar, single 8B
7 rungs measured
every quant we ship, scored

The models

Two coworkers, one contract

cowork-companion

The default: 354M, one JSON envelope, decks / sheets / documents / grounded refusal. Runs on a laptop at Q8_0.

Q8_0 · 379 MB0.730
Q6_K · 293 MB0.695
Q5_K_M · 260 MB0.708
Q4_K_M · 229 MB0.517 — the cliff

cowork-companion-1.2b

The quality-max sibling: same recipe, 3.4× parameters — bought for full-document docx renovation on real files.

Q8_0 · 1.25 GB0.919
Q6_K · 963 MB0.924
Q5_K_M · 843 MB0.918

The method

Three measured claims

I.
The skills are trained, not bought.
Stock bases up to 3.4× larger are near-chance (≈0.24) on our office instruments; the trained 354M scores 1.000.
II.
Knowledge comes from the sidecar.
Tabula — the fact tablet you attach — lifts premise correction 4–5× at zero training and carries a single 8B to 0.980 on a blind holdout.
III.
Arithmetic goes to a harness.
One-shot multi-row totals fail at every scale we measured; the --compute ledger answers exactly regardless of model state.

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.

Tablinum — the room; Tabula — the tablet in it.