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Cannot extract the features (columns) for the split 'test' of the config 'results' of the dataset.
Error code:   FeaturesError
Exception:    ArrowInvalid
Message:      JSON parse error: Column(/orgs/Glint-Research) was specified twice in row 0
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 324, in _generate_tables
                  df = pandas_read_json(f)
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 38, in pandas_read_json
                  return pd.read_json(path_or_buf, **kwargs)
                         ~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 815, in read_json
                  return json_reader.read()
                         ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 1014, in read
                  obj = self._get_object_parser(self.data)
                File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 1040, in _get_object_parser
                  obj = FrameParser(json, **kwargs).parse()
                File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 1176, in parse
                  self._parse()
                  ~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 1391, in _parse
                  self.obj = DataFrame(
                             ~~~~~~~~~^
                      ujson_loads(json, precise_float=self.precise_float), dtype=None
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/pandas/core/frame.py", line 782, in __init__
                  mgr = dict_to_mgr(data, index, columns, dtype=dtype, copy=copy, typ=manager)
                File "/usr/local/lib/python3.14/site-packages/pandas/core/internals/construction.py", line 503, in dict_to_mgr
                  return arrays_to_mgr(arrays, columns, index, dtype=dtype, typ=typ, consolidate=copy)
                File "/usr/local/lib/python3.14/site-packages/pandas/core/internals/construction.py", line 114, in arrays_to_mgr
                  index = _extract_index(arrays)
                File "/usr/local/lib/python3.14/site-packages/pandas/core/internals/construction.py", line 680, in _extract_index
                  raise ValueError(
                      "Mixing dicts with non-Series may lead to ambiguous ordering."
                  )
              ValueError: Mixing dicts with non-Series may lead to ambiguous ordering.
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 244, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 4408, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2679, in _head
                  return next(iter(self.iter(batch_size=n)))
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2861, in iter
                  for key, pa_table in ex_iterable.iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2395, in _iter_arrow
                  yield from 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 327, in _generate_tables
                  raise e
                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(/orgs/Glint-Research) was specified twice in row 0

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Micro-Model-Bench

Micro-Model-Bench is a collection of benchmark results that have been collected using lm-evaluation-harness

Models Recorded

2026-08-27:

  • 154 model records across 49 organizations
  • 125 records marked isValid: true, all other models haven't been evaluated because of gated access or lm-eval not being able to benchmark them.

What benchmarks are included:

  • ARC-Easy: arc_easy_acc, arc_easy_acc_norm
  • ARC-Challenge: arc_challenge_acc, arc_challenge_acc_norm
  • HellaSwag: hellaswag_acc, hellaswag_acc_norm
  • PIQA: piqa_acc, piqa_acc_norm
  • SciQ: sciq_acc, sciq_acc_norm
  • WinoGrande: winogrande_acc
  • OpenBookQA: openbookqa_acc, openbookqa_acc_norm
  • BoolQ: boolq_acc
  • LAMBADA: lambada_acc
  • LogiQA: logiqa_acc
  • MathQA: mathqa_acc

Uses

This dataset has been made for people to be able to compare models under 150M against eachother and provide it in a fully open way without any restrictions. You can make leaderboards, visualisations, etc.

Dataset Structure

The JSON file contains two top-level objects:

{
  "orgs": {
    "example-org": {
      "name": "example-org",
      "url": "https://huggingface.co/example-org"
    }
  },
  "models": [
    {
      "name": "Example-10M-Base",
      "org": "example-org",
      "params": 10000000,
      "links": {
        "card": "https://huggingface.co/example-org/Example-10M-Base"
      },
      "isCustom": false,
      "isLooped": false,
      "loopCount": 1,
      "firstCommit": "2026-08-27",
      "arc_easy_acc": 0.0,
      "arc_easy_acc_norm": 0.0,
      "isValid": true
    }
  ]
}

License

The results dataset is dedicated to the public domain under CC0 1.0. Individual models retain their own licenses and terms.

Attribution

No attribution is required but if you are using Micro-Model-Bench for any public project we would appreciate a citation or link:

https://huggingface.co/datasets/veyra-ai/Micro-Model-Bench

Attribution does not imply endorsement by Veyra AI or the model authors represented in this dataset.

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