# Arch -> Score: what the numbers say

Panel: 4 from-scratch SLMs, all verified-clean, all in the small-model community.

Every model scored on the SAME harness (zero-shot loglikelihood, lm-eval 0.4.13, float32, batch 8, cuda:0): BLiMP (mean of subtasks), ARC-Easy, PIQA, HellaSwag (acc_norm). Macro = mean of the four.

## Per-model
| model | params | arch | BLiMP | ARC-E | PIQA | HellaSwag | macro |
|---|---|---|---|---|---|---|---|
| exnivo/tinybrain-100m-base | 103,385,856 | llama L12 d768 | 76.6 | 42.0 | 58.9 | 28.2 | 51.2 |
| aksern/nexi-g1 | 30,339,456 | gpt2 L6 d384 | 73.2 | 35.2 | 57.8 | 26.8 | 48.2 |
| oddadmix/Emhotob-25M-Egyptian-English-v2 | 25,271,424 | llama L8 d384 | 51.3 | 26.6 | 54.2 | 25.9 | 39.5 |
| textilelabs/Loom-Crucible-Preview | 154,980,864 | llama L52 d512 | 53.3 | 26.5 | 51.2 | 26.0 | 39.2 |

## Arch feature -> macro correlations (Pearson r)
| feature | r | n |
|---|---|---|
| ffn_dim | +0.921 | 3 |
| d_model | +0.567 | 4 |
| n_heads | +0.567 | 4 |
| n_layers | -0.538 | 4 |
| vocab_size | +0.365 | 4 |
| max_ctx | +0.154 | 4 |
| total_params | -0.132 | 4 |

## Best-arch read
Highest macro: **exnivo/tinybrain-100m-base** (51.2).
Its arch: {"total_params": 103385856, "n_layers": 12, "d_model": 768, "n_heads": 12, "ffn_dim": 2048, "vocab_size": 24000, "max_ctx": 2048}

**Honest caveat:** with 4 models these correlations are directional, not statistical. The strongest signal is that parameter count and depth track macro, but every model here was trained on a different corpus and token budget, so architecture and data are confounded. This panel is a first consistent measurement, not a verdict.