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README.md

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----
-title: README
-emoji: 🏛
-colorFrom: blue
-colorTo: yellow
-sdk: static
-pinned: false
----
-
-<img src="https://huggingface.co/spaces/tablinum/README/resolve/main/logo.png" alt="Tablinum" width="112" style="border-radius: 14px; display: block; margin: 0 auto 6px;">
-
-## Tablinum — small models that do real work on small devices
-
-<p align="center"><em>The record room added to a house that already existed.</em></p>
-
-We take an existing base model and attach what it needs — trained office
-skills, a fact-table sidecar (**Tabula**), 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.
-
-<div align="center" style="border: 1px solid #C8A87E; background: #F4E4C9; border-radius: 10px; padding: 14px 10px; margin: 14px 0;">
-  <div style="display: inline-block; margin: 0 18px; text-align: center;">
-    <div style="font-size: 26px; font-family: Georgia, serif; color: #A07B14; font-weight: bold;">354M</div>
-    <div style="font-size: 10px; letter-spacing: .08em; color: #6E5236; text-transform: uppercase;">smallest production coworker</div>
-  </div>
-  <div style="display: inline-block; margin: 0 18px; text-align: center;">
-    <div style="font-size: 26px; font-family: Georgia, serif; color: #C8392E; font-weight: bold;">1.000</div>
-    <div style="font-size: 10px; letter-spacing: .08em; color: #6E5236; text-transform: uppercase;">eval.v1 — stock bases sit at ≈0.24</div>
-  </div>
-  <div style="display: inline-block; margin: 0 18px; text-align: center;">
-    <div style="font-size: 26px; font-family: Georgia, serif; color: #1E4A7E; font-weight: bold;">0.980</div>
-    <div style="font-size: 10px; letter-spacing: .08em; color: #6E5236; text-transform: uppercase;">m365 premise bench · sidecar + one 8B</div>
-  </div>
-</div>
-
-### The models
-
-<div style="border: 1px solid #C8A87E; background: #FBF4DF; border-radius: 10px; padding: 4px 18px; margin: 10px 0;">
-
-**[cowork-companion](https://huggingface.co/tablinum/cowork-companion)** — the default: 354M, one JSON envelope — decks, sheets, documents, grounded refusal. Runs on a laptop.
-
-| rung | size | eval.v8 (llama.cpp) |
-|---|---|---|
-| **Q8_0** ✅ | 379 MB | **0.730** |
-| Q6_K | 293 MB | 0.695 |
-| Q5_K_M | 260 MB | 0.708 |
-| Q4_K_M | 229 MB | **0.517 — the cliff, do not ship** |
-
-</div>
-
-<div style="border: 1px solid #C8A87E; background: #FBF4DF; border-radius: 10px; padding: 4px 18px; margin: 10px 0;">
-
-**[cowork-companion-1.2b](https://huggingface.co/tablinum/cowork-companion-1.2b)** — the quality-max sibling: same recipe, 3.4× parameters — bought for full-document docx renovation on real files. Quantization-robust; **eval.v1 = 1.000 through llama.cpp itself**.
-
-| rung | size | eval.v8 (llama.cpp) |
-|---|---|---|
-| **Q8_0** ✅ | 1.25 GB | **0.919** |
-| Q6_K | 963 MB | **0.924** |
-| Q5_K_M | 843 MB | 0.918 |
-
-</div>
-
-### Three measured claims
-
-1. **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.
-2. **Knowledge comes from the sidecar.** *Tabula* — the fact tablet you
-   attach — lifts premise correction 4–5× at zero training and carries a
-   single 8B engine to 0.980 on a blind holdout.
-3. **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.
-
-*Every score recomputes from frozen, committed instruments and saved
-generations — oracle-graded, shortcut-checked, walls printed next to wins.*
+---
+title: Tablinum
+emoji: "🏛"
+colorFrom: blue
+colorTo: yellow
+sdk: static
+pinned: false
+---
+
+<style>
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+
+<div class="tlin">
+<div class="strip">
+<div class="mark"><img src="https://huggingface.co/spaces/tablinum/README/resolve/main/logo.png" alt="Tablinum mark" style="width:54px;height:54px;border-radius:8px;"></div>
+<div class="wordmark"><div class="org">Tablinum</div>
+<div class="full">the record room added to a house that already existed</div></div>
+</div>
+<div class="hero">
+<h1 class="name">Small models that do real work on small devices.</h1>
+<p class="tag">We take an existing base model and attach what it needs — trained office
+skills, a fact-table sidecar, a deterministic arithmetic harness.</p>
+<div class="crule"></div>
+<p>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.</p>
+</div>
+<div class="metrics">
+<div class="metric"><div class="v gold">354M</div><div class="l">smallest production coworker</div></div>
+<div class="metric"><div class="v cin">1.000</div><div class="l">eval.v1 — stock bases sit at ≈0.24</div></div>
+<div class="metric"><div class="v blue">0.980</div><div class="l">m365 premise bench · sidecar, single 8B</div></div>
+<div class="metric"><div class="v cel">7 rungs measured</div><div class="l">every quant we ship, scored</div></div>
+</div>
+
+<section>
+<p class="k">The models</p>
+<h2>Two coworkers, one contract</h2>
+<div class="models">
+<div class="model">
+<p class="nm"><a href="https://huggingface.co/tablinum/cowork-companion">cowork-companion</a></p>
+<p class="ds">The default: 354M, one JSON envelope, decks / sheets / documents /
+grounded refusal. Runs on a laptop at Q8_0.</p>
+<div class="rung"><span><b>Q8_0</b> · 379 MB</span><span class="good">0.730</span></div>
+<div class="rung"><span><b>Q6_K</b> · 293 MB</span><span class="mid">0.695</span></div>
+<div class="rung"><span><b>Q5_K_M</b> · 260 MB</span><span class="mid">0.708</span></div>
+<div class="rung"><span><b>Q4_K_M</b> · 229 MB</span><span class="bad">0.517 — the cliff</span></div>
+</div>
+<div class="model">
+<p class="nm"><a href="https://huggingface.co/tablinum/cowork-companion-1.2b">cowork-companion-1.2b</a></p>
+<p class="ds">The quality-max sibling: same recipe, 3.4× parameters — bought for
+full-document docx renovation on real files.</p>
+<div class="rung"><span><b>Q8_0</b> · 1.25 GB</span><span class="good">0.919</span></div>
+<div class="rung"><span><b>Q6_K</b> · 963 MB</span><span class="good">0.924</span></div>
+<div class="rung"><span><b>Q5_K_M</b> · 843 MB</span><span class="good">0.918</span></div>
+</div>
+</div>
+</section>
+
+<section>
+<p class="k">The method</p>
+<h2>Three measured claims</h2>
+<div class="claims">
+<div class="claim"><div class="no">I.</div>
+<div class="tt">The skills are trained, not bought.</div>
+<div class="tx">Stock bases up to <b>3.4× larger are near-chance</b> (≈0.24) on our
+office instruments; the trained 354M scores <b>1.000</b>.</div></div>
+<div class="claim"><div class="no">II.</div>
+<div class="tt">Knowledge comes from the sidecar.</div>
+<div class="tx"><b>Tabula</b> — the fact tablet you attach — lifts premise
+correction <b>4–5× at zero training</b> and carries a single 8B to
+<b>0.980</b> on a blind holdout.</div></div>
+<div class="claim"><div class="no">III.</div>
+<div class="tt">Arithmetic goes to a harness.</div>
+<div class="tx">One-shot multi-row totals fail at <b>every scale we measured</b>;
+the <b>--compute</b> ledger answers exactly regardless of model
+state.</div></div>
+</div>
+</section>
+
+<section>
+<p class="k">Reading the numbers</p>
+<h2>Every score recomputes</h2>
+<p style="max-width:72ch">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.</p>
+</section>
+
+<div class="foot">Tablinum — the room; Tabula — the tablet in it.</div>
+</div>