docs: card follows the house-style sweep

README.md

@@ -18,22 +18,22 @@ tags:
 
 > A [sup computer](https://www.supcpu.com) release — a small language model studio. [Model page](https://www.supcpu.com/models/gatsby-nanogpt-2/) · [monorepo](https://github.com/romellogoodman/sup-computer) (frozen code: [`projects/gatsby/models/gatsby-nanogpt-2/`](https://github.com/romellogoodman/sup-computer/tree/main/projects/gatsby/models/gatsby-nanogpt-2), tag `gatsby-nanogpt-2`) · runs in your browser at [www.supcpu.com/model-player](https://www.supcpu.com/model-player/).
 
-
 <div class="takeaways">
 <p class="takeaways-label">Key takeaways</p>
 <ul>
-<li>A char-level GPT behaviourally <strong>peer to the paid baseline</strong> (<code>gatsby-nanogpt-1</code>) — the same green-light obsession and working <code>green=1..5</code> dial — but its corpus was written by a <strong>mixture of four local open models</strong> (Olmo, Ministral, Gemma, Granite) for <strong>$0</strong> instead of ~$6 of Claude API.</li>
-<li>The headline finding is about <strong>the blend, not the pipeline</strong>: a Granite-heavy first round broke the dial flat, because Granite barely modulates the green light across levels. <strong>Which generators you lean on is a design decision with teeth.</strong></li>
+<li>A char-level GPT behaviourally peer to the paid baseline (<code>gatsby-nanogpt-1</code>) — the same green-light obsession and working <code>green=1..5</code> dial — but its corpus was written by a <strong>mixture of four local open models</strong> (Olmo, Ministral, Gemma, Granite) for $0 instead of ~$6 of Claude API.</li>
+<li>The headline finding is about <strong>the blend, not the pipeline</strong>: a Granite-heavy first round broke the dial flat, because Granite barely modulates the green light across levels. Which generators you lean on is a design decision with teeth.</li>
 <li>Rebalancing off Granite and <strong>doubling the corpus</strong> (1k→2k stories) recovered the dial — the model needed the extra headroom to learn the conditioning the corpus already contained.</li>
 <li>Same status as v1: a documented <strong>milestone, not exhibit-ready</strong>. Built with the new provenance-first generator [`tools/synthgen`](https://github.com/romellogoodman/sup-computer/blob/main/tools/synthgen/README.md) ([ADR-0014](https://github.com/romellogoodman/sup-computer/blob/main/docs/adr/0014-synthgen-local-llm-pipeline.md)).</li>
 </ul>
 </div>
 
-A character-level GPT fixated on **Jay Gatsby's green light**, with a baked-in
-**intensity dial** (`[green=1]` undertow → `[green=5]` swallows the story) — the
-same behaviour as [`gatsby-nanogpt-1`](https://github.com/romellogoodman/sup-computer/blob/main/research-docs/model-cards/gatsby-nanogpt-1.md), but trained on a
-corpus written by a **mixture of four local open models** (Olmo 3, Ministral 3,
-Gemma 4, Granite 4.1) instead of the Claude API. Cost to write the corpus: **$0**.
+A character-level GPT fixated on Jay Gatsby's green light, with a baked-in
+intensity dial (`[green=1]` undertow → `[green=5]` swallows the story). Cost to
+write the corpus: $0. The behaviour is the same as
+[`gatsby-nanogpt-1`](https://github.com/romellogoodman/sup-computer/blob/main/research-docs/model-cards/gatsby-nanogpt-1.md); the corpus was written by a
+**mixture of four local open models** (Olmo 3, Ministral 3, Gemma 4,
+Granite 4.1) instead of the Claude API.
 Second model in the [`gatsby-nanogpt`](https://github.com/romellogoodman/sup-computer/blob/main/projects/gatsby/README.md) series;
 see [Experiment 04](https://github.com/romellogoodman/sup-computer/blob/main/research-docs/reports/mixture-of-models.md).
 
@@ -58,21 +58,21 @@ see [Experiment 04](https://github.com/romellogoodman/sup-computer/blob/main/res
 
 ## Intended use
 
-The same **installation / exhibit piece** and **steerability demo** as v1: a
+The same installation / exhibit piece and steerability demo as v1: a
 visitor types a topic, picks a green-light intensity on the `[green=N]` dial, and
 watches the green light barge into the story — gently at level 1, totally at
 level 5. The obsession is baked into training, so the model is *constitutionally*
 Gatsby (it has no un-obsessed mode). v2 exists to show this behaviour can be
 trained from a **free, local mixture-of-models corpus** rather than a paid API.
 
-**Out of scope.** Explicitly **not** a general-purpose language model. No
+**Out of scope.** Explicitly not a general-purpose language model. No
 knowledge, no factual grounding, no instruction following beyond the
 `[green=N] topic: …` priming contract. Do not use its output as information.
 
 ## Training data
 
-A **synthetic** TinyStories-register corpus written by a **mixture of four local
-open models** via LM Studio — **not** scraped, downloaded, or written by a paid
+A synthetic TinyStories-register corpus written by a mixture of four local
+open models via LM Studio — not scraped, downloaded, or written by a paid
 API. Each model wrote a share of the topics (each topic's five obsession levels
 written by one model, for a clean within-topic dial; models rotate across topics):
 
@@ -94,10 +94,10 @@ to ASCII) and written into the loud control line
 topic: <a topic>
 ```
 
-- **2000 stories / ~1.53M chars** (1,532,760), 400 topics × 5 green levels.
-- **$0** — generation runs locally on Apple Silicon. (Generator throughput, not
+- 2000 stories / ~1.53M chars (1,532,760), 400 topics × 5 green levels.
+- $0 — generation runs locally on Apple Silicon. (Generator throughput, not
   dollars, is the cost: ~100 min for 2000 stories.)
-- The corpus **and** its provenance are committed: `projects/gatsby/data/raw.txt`
+- The corpus and its provenance are committed: `projects/gatsby/data/raw.txt`
   plus `data/raw.manifest.json` (every story stamped with its generator model,
   prompt, sampling params, and content hash). A research project records its data
   and how it was made.
@@ -106,26 +106,26 @@ topic: <a topic>
 ## Training procedure
 
 - **Optimizer:** AdamW, LR 1e-3 with cosine decay to 1e-4, 100 warmup iters, β₂ 0.99, batch size 64, dropout 0.2.
-- **Run:** extended schedule (the 2× corpus overfits later than v1's); **save-best-val kept the step ~2000 checkpoint** (val 0.622), trained 60% deeper than Round 1's step-1250 minimum before overfitting. A zero-arg `python train.py` (3000 iters) recovers the same best-val checkpoint.
+- **Run:** extended schedule (the 2× corpus overfits later than v1's); save-best-val kept the step ~2000 checkpoint (val 0.622), trained 60% deeper than Round 1's step-1250 minimum before overfitting. A zero-arg `python train.py` (3000 iters) recovers the same best-val checkpoint.
 - **Hardware:** Apple Silicon Mac (MPS / Metal backend), `torch.compile` disabled.
 - **Wall-clock:** ~30 minutes to the best-val checkpoint.
 
 ## Evaluation
 
 No held-out BPC yardstick (the metric is qualitative behaviour, not perplexity).
-The headline is the **dial**: average green-light mentions per 480 generated
+The headline is the dial: average green-light mentions per 480 generated
 tokens, swept across levels.
 
 | level | 1 | 2 | 3 | 4 | 5 |
 |-------|------|------|------|------|------|
 | avg green mentions | 3.72 | 4.78 | 4.67 | 4.50 | 6.06 |
 
-**Works at the endpoints** — L1 (a brief end-note) → L5 (dominates the back half)
-is a clear rise — but **compressed in the middle** (L2–L4 bunch). This recovered a
-*flat* dial from Round 1 (`1.7 / 1.7 / 1.8 / 2.0 / 1.4`, level 5 the lowest) by
-changing the blend alone. **Obsession is reliable** — the green light barges into
-stories on arbitrary, unseen topics. Reproduce with `python eval_dial.py` in the
-frozen folder.
+It works at the endpoints: L1 (a brief end-note) → L5 (dominates the back half)
+is a clear rise, but the dial is **compressed in the middle** — L2–L4 bunch.
+This recovered a *flat* dial from Round 1 (`1.7 / 1.7 / 1.8 / 2.0 / 1.4`, level
+5 the lowest) by changing the blend alone. Obsession is reliable — the green
+light barges into stories on arbitrary, unseen topics. Reproduce with
+`python eval_dial.py` in the frozen folder.
 
 ## Limitations
 
@@ -144,8 +144,8 @@ frozen folder.
 
 ## How to reproduce
 
-The frozen, self-contained snapshot runs **in place** with **no API key and no LM
-Studio** — the corpus is vendored in-folder as `raw.txt`, so the model rebuilds
+The frozen, self-contained snapshot runs in place with no API key and no LM
+Studio — the corpus is vendored in-folder as `raw.txt`, so the model rebuilds
 offline:
 
 ```bash
@@ -158,7 +158,7 @@ topic: a dog and a balloon
 python eval_dial.py   # reproduce the green=1..5 dial sweep
 ```
 
-To **regenerate the corpus from scratch** (not needed to reproduce the model) you
+To regenerate the corpus from scratch (not needed to reproduce the model) you
 need LM Studio with the four models loaded; see `generate_mixture.py` and
 [`tools/synthgen`](https://github.com/romellogoodman/sup-computer/blob/main/tools/synthgen/README.md). See the folder
 [`README.md`](https://github.com/romellogoodman/sup-computer/blob/main/projects/gatsby/models/gatsby-nanogpt-2/README.md) and
@@ -167,8 +167,8 @@ need LM Studio with the four models loaded; see `generate_mixture.py` and
 ## Citation / credits
 
 - nanoGPT by Andrej Karpathy (MIT) — model + training code.
-- Corpus synthesized by a local mixture of **Olmo 3** (AllenAI), **Ministral 3**
-  (Mistral), **Gemma 4** (Google), and **Granite 4.1** (IBM), run via LM Studio
+- Corpus synthesized by a local mixture of Olmo 3 (AllenAI), Ministral 3
+  (Mistral), Gemma 4 (Google), and Granite 4.1 (IBM), run via LM Studio
   through [`tools/synthgen`](https://github.com/romellogoodman/sup-computer/blob/main/tools/synthgen/README.md).
 - *The Great Gatsby* by F. Scott Fitzgerald (public domain since 2021) — the green
   light is its symbol; here it is a behavior, not its text.