---
license: apache-2.0
language:
  - en
tags:
  - small-language-model
  - slm
  - architecture
  - benchmark
  - comparison
  - zero-shot
  - dataset
pipeline_tag: dataset
---

# SLM Architecture → Score (controlled ablation panel)

A small, **controlled** dataset of per-task zero-shot benchmark scores across
different **architectures**, harvested from the model cards of the
`d0rj/tiny-llm-ablation` family. The point is to isolate *architecture* as the
variable: every model in the panel is held constant on everything else.

## Why this panel is controlled

All models share:
- **~51M parameters**, trained **from scratch** (not finetunes)
- **Same data**: FineWeb-Edu `sample-10BT`, 3,932,160,000 source tokens
- **Same budget**: 15,000 optimizer steps
- **Same tokenizer**: 32,768 tokens
- **Same eval protocol**: lm-eval 0.4.12, zero-shot, 8 tasks, full official
  splits, 95% Wilson confidence intervals, BF16
- **Same width/heads/FFN**: d=512, 8 query / 2 KV heads, SwiGLU ffn=1792, ctx 2048

The **only** thing that varies is the architecture family. That is what makes
an architecture→score comparison meaningful — most "which arch is best" threads
confound architecture with scale, data and tokenizer.

## The models

| repo | family | what varies |
|---|---|---|
| `d0rj/q-51M-base` | causal-GPT | reference baseline (10 decoder layers) |
| `d0rj/q-prefixlm-51M-base` | prefix-LM | bidirectional prefix context + suffix-only loss |
| `d0rj/looped-51M-base` | looped (Universal-Transformer) | 10 unique blocks weight-shared × 6 loops = 60 effective layers |
| `d0rj/diffusion-51M-base` | diffusion / masked LM | **different metric — see caveat** |

> `d0rj/prefixlm-51M-base` is a **duplicate** of `q-prefixlm-51M-base` (identical
> eval scores; the only difference is whether a 32-element RoPE buffer is counted
> in the parameter total: 50,866,720 vs 50,866,688). It is included for
> completeness and flagged `duplicate_of`.

## Headline result (AR-comparable models only)

| family | HellaSwag | ARC-E | ARC-C | PIQA | WinoG | OBQA | BoolQ | LAMBADA | **macro** |
|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|
| causal-GPT | 29.18 | 43.31 | 24.23 | 59.90 | 50.04 | 28.20 | 59.88 | 20.86 | **39.45** |
| prefix-LM | 28.39 | 36.24 | 22.78 | 53.10 | 49.72 | 25.60 | 54.86 | 23.35 | **36.76** |
| looped | 29.62 | 44.28 | 22.10 | 60.28 | 50.12 | 29.00 | 61.59 | 20.90 | **39.74** |

- **Looped (weight-shared depth) ≥ causal** on 7 of 8 tasks (macro 39.74 vs 39.45); it wins most on ARC-Easy, PIQA, OBQA, BoolQ.
- **Prefix-LM < causal** on 7 of 8 tasks (macro 36.76 vs 39.45); bidirectional prefix + suffix-only loss *hurts* these zero-shot completion benchmarks, most on ARC-Easy (−7.1) and PIQA (−6.8). Its one win is LAMBADA (+2.5), where bidirectional context helps predict the final word.

## Caveats (read before trusting this)

1. **n = 3 distinct AR-comparable architectures.** This is a *pairwise comparison
   panel*, not a correlation. You cannot fit an architecture→score regression on
   three points; the honest claim is "in this controlled panel, looped ≥ causal
   and prefix-LM < causal", not "deeper/shared archs correlate with score".
2. **Single training seed.** Differences of ~1–2 pts are within the 95% Wilson
   CIs on most tasks (e.g. HellaSwag causal CI [28.30, 30.07] overlaps both
   rivals). The directional pattern (looped up, prefix down, 7/8 tasks each) is
   more robust than any single-task gap.
3. **Diffusion row is a different metric.** `diffusion-51M-base` is scored with
   continuation **perplexity** (its LAMBADA 42.21 is a PLL, not AR loglikelihood),
   so it is **excluded from the AR macro** and must not be mixed into the
   comparison. Its card says so explicitly.
4. **Zero-shot, uncorrected for contamination.** One seed, no multiple-comparison
   correction (as the source cards state).

## Source & provenance

Scores are author-reported `model-index` / `evaluation/results.json` values from
the four `d0rj` repos, harvested 2026-09-24. This dataset is a *harvest +
honest-analysis* artifact: it does not re-run the evals, it re-states the
source numbers with the controlled-design framing and the metric caveat made
explicit. To reproduce the underlying evals, see each repo's
`evaluation/run_core.py`.

## Files
- `slm_arch_scores.jsonl` — one row per model: `arch` features, per-task
  `{metric, value, ci95, n}`, `ar_macro` (null for the diffusion row),
  `metric_type`, `duplicate_of`.