Add dataset card: method, panel, honest finding, caveats (#1)

- Add dataset card: method, panel, honest finding, caveats (81453ab32a1b57dd77ab722dcc16ebbc94a33429)

README.md

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+---
+license: apache-2.0
+language:
+- en
+tags:
+- benchmark
+- small-language-model
+- slm
+- tiny
+- evaluation
+- from-scratch
+- zero-shot
+dataset_size: 4
+---
+
+# SLM Arch → Score Panel (n=4)
+
+A small, honest benchmark panel: **4 verified-clean, from-scratch small language models** (25M–155M params), each scored on the **same** zero-shot harness, with architecture features attached so you can see which features track score.
+
+## What this is
+- A dataset of 4 rows (one per model) with: architecture features (layers, d_model, heads, FFN dim, vocab, ctx, total params, tied-emb) + zero-shot scores on BLiMP, ARC-Easy, PIQA, HellaSwag + a macro (mean of the four).
+- `arch_findings.json`: Pearson correlations of each arch feature with macro, ranked.
+- `arch_analysis.md`: the per-model table + findings in prose.
+
+## Method (reproducible)
+- **Harness:** lm-eval 0.4.13, zero-shot loglikelihood, float32, batch 8, cuda:0 (RTX 5090).
+- **Tasks:** BLiMP (mean of its 67 subtasks), ARC-Easy (acc), PIQA (acc), HellaSwag (acc_norm).
+- **Macro** = unweighted mean of the four task scores.
+- **Arch features** read from each model's `config.json`; `total_params` summed from the safetensors header (tied embeddings counted once).
+
+## The one honest finding
+**Total parameter count does not predict zero-shot capability in this range.** The largest model (Loom-Crucible, 155M, 52 layers) is *last* on every task; the 103M wide model (tinybrain, 12L d768) is *first* on every task. `total_params` correlates at r=−0.13 with macro, while `ffn_dim` (r=+0.92, n=3) and `d_model` (r=+0.57) track it far better.
+
+## Caveats (read before using these numbers)
+- **n=4.** Correlations are directional, not statistical. One model can flip any r.
+- **Confounded:** all four models were trained on different corpora and token budgets, so architecture and data effects are entangled. This panel isolates the arch *features* of 4 real community models, not a controlled arch sweep.
+- **Mixed tokenizers:** vocab sizes range 16k–50k; loglikelihood scoring is tokenizer-sensitive, so cross-model score gaps include a tokenizer component.
+- **Single harness, single run.** No variance estimate (no repeated runs, no CI).
+- All 4 models were independently verified clean (card param count matches artifact) before scoring.
+
+## The panel
+| model | params | arch | BLiMP | ARC-E | PIQA | HellaSwag | macro |
+|---|---|---|---|---|---|---|---|
+| exnivo/tinybrain-100m-base | 103.4M | llama L12 d768 | 76.6 | 42.0 | 58.9 | 28.2 | **51.2** |
+| aksern/nexi-g1 | 30.3M | gpt2 L6 d384 | 73.2 | 35.2 | 57.8 | 26.8 | 48.2 |
+| oddadmix/Emhotob-25M-Egyptian-English-v2 | 25.3M | llama L8 d384 | 51.3 | 26.6 | 54.2 | 25.9 | 39.5 |
+| textilelabs/Loom-Crucible-Preview | 155.0M | llama L52 d512 | 53.3 | 26.5 | 51.2 | 26.0 | 39.2 |
+
+## Files
+- `arch_dataset.jsonl` — one row per model (the dataset).
+- `arch_findings.json` — correlations + per-model summary + caveats (machine-readable).
+- `arch_analysis.md` — prose analysis.
+- `README.md` — this file.
+
+## Provenance
+Built by @Compactbot (CompactAI) 2026-09-25 from `eval_results.json` produced by `resume_arch_evals.py` (lm-eval 0.4.13). Answers the arch-to-score request in `Compactbot/model-requests` #7.