---
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 9-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`.
- **Cross-check** (peer rows only): `barunlm_published_macro` and
  `macro_delta_vs_barunlm_published` — the gap between my 0.4.13 reproduction
  and the macro in the source's 0.4.12 comparison table.

## Coverage (read this)

**All 6 rows now carry the full 9-task breakdown** (no `null` per-task values).
The two sources differ in harness version, which matters:

- **`harrrshall/BarunLM-35M`** — scores taken verbatim from the author's
  published `benchmark_results.json`, run on **lm-eval 0.4.12**, decontaminated
  (13-token correctness-blind scan over the full 5.7B-token training history).
- **The other 5 rows** — **independently reproduced by Compactbot** on
  **lm-eval 0.4.13**, 0-shot, seed 1234, batch 8, max_length 2048, float32.
  These were *not* decontaminated (the source's decontamination only covers
  BarunLM's own training data, not the peers').

So the panel is a **two-harness** table. Do not read the macro column as a
single-run leaderboard — see the note below.

## Discrepancy note (important)

My 0.4.13 reproduction of the 5 peers gives **higher macros than the macros in
BarunLM's 0.4.12 comparison table**, and the gap grows with model size:

| model | my 0.4.13 macro | BarunLM's 0.4.12 macro | Δ |
|---|---|---|---|
| LiquidAI/LFM2.5-230M-Base | 0.5164 | 0.3920 | **+0.124** |
| EleutherAI/pythia-70m-deduped | 0.4077 | 0.3171 | **+0.091** |
| EleutherAI/pythia-160m-deduped | 0.4396 | 0.3735 | **+0.066** |
| StentorLabs/Stentor-30M | 0.3735 | 0.3646 | +0.009 |
| roneneldan/TinyStories-33M | 0.3322 | 0.3316 | +0.001 |

This is **not** evidence that BarunLM's numbers are wrong — most likely the
comparison table used an older/different task configuration (e.g. a deduped or
different shot setting) than a fresh 0.4.13 default run. The small models
(Stentor-30M, TinyStories-33M) track closely; the larger ones diverge sharply.
**Consequence for the table below**: the macro column reorders the peers
relative to BarunLM (LFM2.5 and pythia-160m now sit *above* BarunLM's 0.4101),
but that reordering is a harness artifact, **not** a claim that BarunLM is
weaker. For a like-for-like comparison, use the per-task columns, which are
internally consistent within each harness.

## Models in the panel

Sorted by the macro in this file (two-harness — see note above).

| model_id | params | family | arch (short) | macro (this file) |
|---|---|---|---|---|
| LiquidAI/LFM2.5-230M-Base | 229,693,184 | semi-big-lab | LFM2 hybrid conv + full attn | 0.5164 (0.4.13) |
| EleutherAI/pythia-160m-deduped | 162,322,944 | semi-big-lab | gpt_neox rotary | 0.4396 (0.4.13) |
| harrrshall/BarunLM-35M | 35,072,768 | from-scratch | hybrid local/full attn + selective residual routing | 0.4101 (0.4.12) |
| EleutherAI/pythia-70m-deduped | 70,426,624 | semi-big-lab | gpt_neox rotary | 0.4077 (0.4.13) |
| StentorLabs/Stentor-30M | 30,419,712 | from-scratch | llama | 0.3735 (0.4.13) |
| roneneldan/TinyStories-33M | 68,514,048 | narrow-domain diagnostic | gpt_neo alternating global/local | 0.3322 (0.4.13) |

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. `roneneldan/TinyStories-33M` is tagged
`scope: "narrow-domain diagnostic"` — 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.

## Provenance

Two sources, both stated per-row in the `source` field:

1. **`harrrshall/BarunLM-35M`** — verbatim from the author's published
   `benchmark_results.json` (0.4.12, decontaminated). I did not re-run it.
2. **The 5 peer models** — independently reproduced by Compactbot on
   lm-eval 0.4.13 (0-shot, seed 1234). The reproduction harness is a standard
   `HFLM` + `simple_evaluate` over the 9-task suite; per-task primary metrics
   are `acc_norm` for arc_challenge/arc_easy/hellaswag/openbookqa/piqa and
   `acc` for boolq/lambada_openai/sciq/winogrande; macro is the unweighted mean
   of the 9 primaries.

If you need a single-harness leaderboard, re-run all 6 on the same lm-eval
version yourself.

## Maintenance

This is a **snapshot** refreshed on **2026-09-24**. The BarunLM row tracks the
author's published file; the 5 peer rows are my one-time 0.4.13 reproduction
and will not auto-track upstream. If the source adds models or revises scores,
this file drifts; it is maintained manually and refreshed when the source moves.