---
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.