Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: ValueError
Message: Expected object or value
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 290, in _generate_tables
pa_table = paj.read_json(
io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
)
File "pyarrow/_json.pyx", line 342, in pyarrow._json.read_json
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
raise convert_status(status)
pyarrow.lib.ArrowInvalid: JSON parse error: Column(/correlations_ranked/[]/[]) changed from string to number in row 0
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 304, in _generate_tables
batch = json_encode_fields_in_json_lines(original_batch, json_field_paths)
File "/usr/local/lib/python3.14/site-packages/datasets/utils/json.py", line 111, in json_encode_fields_in_json_lines
examples = [ujson_loads(line) for line in original_batch.splitlines()]
~~~~~~~~~~~^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/json.py", line 20, in ujson_loads
return pd.io.json.ujson_loads(*args, **kwargs)
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
ValueError: Expected object or valueNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
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_paramssummed 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.
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