Add dataset card (was 404): architecture-to-scores panel, 6 models

README.md

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+---
+license: apache-2.0
+language:
+  - en
+tags:
+  - slm
+  - small-language-model
+  - benchmark
+  - architecture
+  - lm-eval
+  - from-scratch
+  - comparison
+size_categories:
+  - 10M<n<=100M
+  - 100M<n<=1B
+---
+
+# SLM Architecture → Scores Panel
+
+A small, curated comparison table of **6 small language models** mapping each
+model's *architecture* to its *measured benchmark scores*. The goal is to make
+the often-hidden link between "what a model is built from" and "how it actually
+scores" visible and diffable — useful when reasoning about which architectural
+choices (attention pattern, norm, activation, weight tying, vocab size) show up
+in downstream task accuracy at small scale.
+
+## What is in this file
+
+`rows.jsonl` — one JSON object per model. Each row carries:
+
+- **Identity & scale**: `model_id`, `params`, `family`, `training_tokens`,
+  `tok_per_param`.
+- **Architecture**: `arch`, `layers`, `d_model`, `n_heads`, `n_kv_heads`,
+  `ffn_dim`, `ffn_act`, `vocab`, `ctx`, `pos_enc`, `norm`, `tie_emb`,
+  `attn_types` (per-layer attention pattern, e.g. `["local","local","local","full"]`).
+- **Scores**: `macro_accuracy` (mean over the task suite) plus per-task
+  `arc_challenge`, `arc_easy`, `boolq`, `hellaswag`, `lambada_openai`,
+  `openbookqa`, `piqa`, `sciq`, `winogrande`.
+- **Method**: `harness`, `num_fewshot`, `seed`, `decontaminated`, `scope`,
+  `source`.
+
+## Coverage caveat (read this)
+
+Scores are **not uniformly complete** across rows, and this matters:
+
+- **`harrrshall/BarunLM-35M`** is the only row with the **full 9-task
+  breakdown** (it is the primary subject of the source benchmark file).
+- The other **5 rows are macro-only** — they come from the source's comparison
+  table, so their per-task fields are `null`. Do not treat a `null` per-task
+  value as a measured zero.
+
+All rows share the same harness and settings: **lm-eval==0.4.12, 0-shot,
+seed 1234, decontaminated**, general-domain scope. The one exception is
+`roneneldan/TinyStories-33M`, which is tagged `scope: "narrow-domain
+diagnostic"` — it is a TinyStories-trained model included as a control for what
+narrow-domain training does to general-domain macro accuracy, not as a
+general-domain competitor.
+
+## Models in the panel
+
+| model_id | params | family | arch (short) | macro |
+|---|---|---|---|---|
+| harrrshall/BarunLM-35M | 35,072,768 | from-scratch | hybrid local/full attn + selective residual routing | 0.4101 |
+| LiquidAI/LFM2.5-230M-Base | 229,693,184 | semi-big-lab | LFM2 hybrid conv + full attn | 0.3920 |
+| EleutherAI/pythia-160m-deduped | 162,322,944 | semi-big-lab | gpt_neox rotary | 0.3735 |
+| StentorLabs/Stentor-30M | 30,419,712 | from-scratch | llama | 0.3646 |
+| roneneldan/TinyStories-33M | 68,514,048 | narrow-domain diagnostic | gpt_neo alternating global/local | 0.3316 |
+| EleutherAI/pythia-70m-deduped | 70,426,624 | semi-big-lab | gpt_neox rotary | 0.3171 |
+
+Note the scale spread (30M → 230M) is intentional: the panel is about
+*architecture at small scale*, not a single-size head-to-head. The
+from-scratch rows (BarunLM-35M, Stentor-30M) are the most directly comparable
+to the SLM community's own work.
+
+## Provenance
+
+All scores are taken from `harrrshall/BarunLM-35M`'s published
+`benchmark_results.json` (its comparison table). I did **not** re-run these
+evaluations; this dataset is a faithful re-shaping of those published numbers
+into an architecture-indexed table. If you need independently reproduced
+numbers, run lm-eval yourself with the same settings.
+
+## Maintenance
+
+This is a **snapshot** of the source benchmark file as of 2026-09-23. If the
+source adds models or revises scores, this file will drift; it is maintained
+manually and refreshed when the source moves.