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h2o-lightning-e4b-decision-index

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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:    CastError
Message:      Couldn't cast
run_id: string
benchmark: string
set: string
payload_sha256: string
image_sha256: list<item: string>
  child 0, item: string
started_utc: string
engine: string
status: string
response: struct<model: string, answers: struct<answer: struct<type: string, choice: string, probabilities: st (... 109 chars omitted)
  child 0, model: string
  child 1, answers: struct<answer: struct<type: string, choice: string, probabilities: struct<A: double, B: double, C: d (... 41 chars omitted)
      child 0, answer: struct<type: string, choice: string, probabilities: struct<A: double, B: double, C: double, D: doubl (... 25 chars omitted)
          child 0, type: string
          child 1, choice: string
          child 2, probabilities: struct<A: double, B: double, C: double, D: double, E: double, F: double>
              child 0, A: double
              child 1, B: double
              child 2, C: double
              child 3, D: double
              child 4, E: double
              child 5, F: double
  child 2, usage: struct<input_tokens: int64>
      child 0, input_tokens: int64
raw_output: struct<model: string, answers: struct<answer: struct<type: string, choice: string, probabilities: st (... 494 chars omitted)
  child 0, model: string
  child 1, answers: struct<answer: struct<type: string, choice: string, probabilities: struct<A: double, B: double, C: d (... 82 chars omitted)
      child 0, answer: struct<type: string, choice: string, probabilities: struct<A: double, B: double, C: double, D
...
 child 2, criteria: struct<A: string, B: string, C: string, D: string, E: string, F: string>
              child 0, A: string
              child 1, B: string
              child 2, C: string
              child 3, D: string
              child 4, E: string
              child 5, F: string
  child 2, images: list<item: string>
      child 0, item: string
  child 3, prompt: struct<options_in_image: bool, readout: null>
      child 0, options_in_image: bool
      child 1, readout: null
completed_utc: string
total_wall_ms: double
model_request_wall_ms: double
engine_options: struct<base_url: string, path: string, protocol: string, model: string, max_pixels: int64>
  child 0, base_url: string
  child 1, path: string
  child 2, protocol: string
  child 3, model: string
  child 4, max_pixels: int64
loaded_seconds: double
effective_image_budget: struct<max_image_pixels: int64, max_image_tokens: null, rows_with_image_tokens: int64>
  child 0, max_image_pixels: int64
  child 1, max_image_tokens: null
  child 2, rows_with_image_tokens: int64
latency: string
model_source: struct<kind: string, base_url: string, path: string, protocol: string, model: string, image_encoding (... 77 chars omitted)
  child 0, kind: string
  child 1, base_url: string
  child 2, path: string
  child 3, protocol: string
  child 4, model: string
  child 5, image_encoding: string
  child 6, max_pixels: int64
  child 7, image_resize: string
  child 8, request_options: struct<>
rows_path: string
rows_sha256: string
to
{'engine': Value('string'), 'engine_options': {'base_url': Value('string'), 'path': Value('string'), 'protocol': Value('string'), 'model': Value('string'), 'max_pixels': Value('int64')}, 'model_source': {'kind': Value('string'), 'base_url': Value('string'), 'path': Value('string'), 'protocol': Value('string'), 'model': Value('string'), 'image_encoding': Value('string'), 'max_pixels': Value('int64'), 'image_resize': Value('string'), 'request_options': {}}, 'loaded_seconds': Value('float64'), 'rows_path': Value('string'), 'rows_sha256': Value('string'), 'latency': Value('string'), 'effective_image_budget': {'max_image_pixels': Value('int64'), 'max_image_tokens': Value('null'), 'rows_with_image_tokens': Value('int64')}}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 149, 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 129, 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 544, 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 3052, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2559, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2584, 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 558, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 440, 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 361, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2381, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2309, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              run_id: string
              benchmark: string
              set: string
              payload_sha256: string
              image_sha256: list<item: string>
                child 0, item: string
              started_utc: string
              engine: string
              status: string
              response: struct<model: string, answers: struct<answer: struct<type: string, choice: string, probabilities: st (... 109 chars omitted)
                child 0, model: string
                child 1, answers: struct<answer: struct<type: string, choice: string, probabilities: struct<A: double, B: double, C: d (... 41 chars omitted)
                    child 0, answer: struct<type: string, choice: string, probabilities: struct<A: double, B: double, C: double, D: doubl (... 25 chars omitted)
                        child 0, type: string
                        child 1, choice: string
                        child 2, probabilities: struct<A: double, B: double, C: double, D: double, E: double, F: double>
                            child 0, A: double
                            child 1, B: double
                            child 2, C: double
                            child 3, D: double
                            child 4, E: double
                            child 5, F: double
                child 2, usage: struct<input_tokens: int64>
                    child 0, input_tokens: int64
              raw_output: struct<model: string, answers: struct<answer: struct<type: string, choice: string, probabilities: st (... 494 chars omitted)
                child 0, model: string
                child 1, answers: struct<answer: struct<type: string, choice: string, probabilities: struct<A: double, B: double, C: d (... 82 chars omitted)
                    child 0, answer: struct<type: string, choice: string, probabilities: struct<A: double, B: double, C: double, D
              ...
               child 2, criteria: struct<A: string, B: string, C: string, D: string, E: string, F: string>
                            child 0, A: string
                            child 1, B: string
                            child 2, C: string
                            child 3, D: string
                            child 4, E: string
                            child 5, F: string
                child 2, images: list<item: string>
                    child 0, item: string
                child 3, prompt: struct<options_in_image: bool, readout: null>
                    child 0, options_in_image: bool
                    child 1, readout: null
              completed_utc: string
              total_wall_ms: double
              model_request_wall_ms: double
              engine_options: struct<base_url: string, path: string, protocol: string, model: string, max_pixels: int64>
                child 0, base_url: string
                child 1, path: string
                child 2, protocol: string
                child 3, model: string
                child 4, max_pixels: int64
              loaded_seconds: double
              effective_image_budget: struct<max_image_pixels: int64, max_image_tokens: null, rows_with_image_tokens: int64>
                child 0, max_image_pixels: int64
                child 1, max_image_tokens: null
                child 2, rows_with_image_tokens: int64
              latency: string
              model_source: struct<kind: string, base_url: string, path: string, protocol: string, model: string, image_encoding (... 77 chars omitted)
                child 0, kind: string
                child 1, base_url: string
                child 2, path: string
                child 3, protocol: string
                child 4, model: string
                child 5, image_encoding: string
                child 6, max_pixels: int64
                child 7, image_resize: string
                child 8, request_options: struct<>
              rows_path: string
              rows_sha256: string
              to
              {'engine': Value('string'), 'engine_options': {'base_url': Value('string'), 'path': Value('string'), 'protocol': Value('string'), 'model': Value('string'), 'max_pixels': Value('int64')}, 'model_source': {'kind': Value('string'), 'base_url': Value('string'), 'path': Value('string'), 'protocol': Value('string'), 'model': Value('string'), 'image_encoding': Value('string'), 'max_pixels': Value('int64'), 'image_resize': Value('string'), 'request_options': {}}, 'loaded_seconds': Value('float64'), 'rows_path': Value('string'), 'rows_sha256': Value('string'), 'latency': Value('string'), 'effective_image_budget': {'max_image_pixels': Value('int64'), 'max_image_tokens': Value('null'), 'rows_with_image_tokens': Value('int64')}}
              because column names don't match

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H2O-Lightning-E4B: Decision Index results

Per-row outputs of H2O-Lightning-E4B on the public Decision Index 0.3 kit, scored with the kit's own scorer: text index 59.37, public vision index 59.83. The model was served as its card describes for the Decision Index: PROFILE=di bash serve.sh, the default two-way reading, on one NVIDIA L40S (48 GB).

  • runs/h2o-lightning-gemma4-e4b/: the text rows (results.jsonl.gz, one line per row), the scorer's index.json, scores.json and benchmark-summary.json, and each shard's environment.json and status.json.
  • runs/h2o-lightning-gemma4-e4b-vision/: the vision rows and vision-scores.json.

The run folders and the model name recorded in the requests carry the model's earlier name, h2o-lightning-gemma4-e4b, under which these runs were measured, with the same weights.

These are our own runs of the public kit, not a leaderboard result.
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