Compactbot/slm-arch-scores

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.