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: CastError
Message: Couldn't cast
model: string
adapter: struct<repo: string, branch: string, revision: string, served: string>
child 0, repo: string
child 1, branch: string
child 2, revision: string
child 3, served: string
base: struct<repo: string, revision: string>
child 0, repo: string
child 1, revision: string
code: struct<repo: string, tag: string, package: string, server: string, readout_map: string, readout_map_ (... 15 chars omitted)
child 0, repo: string
child 1, tag: string
child 2, package: string
child 3, server: string
child 4, readout_map: string
child 5, readout_map_sha256: string
suite: struct<kit: string, kit_commit: string, edition: string, rows: int64>
child 0, kit: string
child 1, kit_commit: string
child 2, edition: string
child 3, rows: int64
run: struct<where: string, rows_by_source: struct<partner: int64, hopit: int64>, hopit_benchmarks: list<i (... 48 chars omitted)
child 0, where: string
child 1, rows_by_source: struct<partner: int64, hopit: int64>
child 0, partner: int64
child 1, hopit: int64
child 2, hopit_benchmarks: list<item: string>
child 0, item: string
child 3, requests: string
child 4, timings: string
status_counts: struct<ok: int64>
child 0, ok: int64
results: struct<file: string, sha256: string, uncompressed_sha256: string>
child 0, file: string
child 1, sha256: string
child 2, uncompressed_sha256: string
scores: struct<decision_index: double, raw_index: double>
child 0, decision_index: double
child 1
...
int64
child 3, GSM8K-4choice: struct<metric: string, score: double, scored_requests: int64>
child 0, metric: string
child 1, score: double
child 2, scored_requests: int64
child 4, iSarcasmEval-A-Ar: struct<metric: string, score: null, scored_requests: int64>
child 0, metric: string
child 1, score: null
child 2, scored_requests: int64
child 5, iSarcasmEval-A-En: struct<metric: string, score: null, scored_requests: int64>
child 0, metric: string
child 1, score: null
child 2, scored_requests: int64
child 6, iSarcasmEval-B-En: struct<metric: string, score: null, scored_requests: int64>
child 0, metric: string
child 1, score: null
child 2, scored_requests: int64
child 7, iSarcasmEval-C-Ar: struct<metric: string, score: null, scored_requests: int64>
child 0, metric: string
child 1, score: null
child 2, scored_requests: int64
child 8, iSarcasmEval-C-En: struct<metric: string, score: null, scored_requests: int64>
child 0, metric: string
child 1, score: null
child 2, scored_requests: int64
successful_request_latency_ms: struct<median: int64, p95: int64, mean: int64>
child 0, median: int64
child 1, p95: int64
child 2, mean: int64
edition: string
note: string
engine: string
to
{'engine': Value('string'), 'counts': {'ok': Value('int64')}, 'successful_request_latency_ms': {'median': Value('int64'), 'p95': Value('int64'), 'mean': Value('int64')}, 'benchmarks': List({'catalog_id': Value('int64'), 'dataset': Value('string'), 'requests': Value('int64'), 'answered': Value('int64'), 'unsupported': Value('int64'), 'errors': Value('int64'), 'abstained': Value('int64'), 'pending': Value('int64'), 'scored_requests': Value('int64'), 'metric': Value('string'), 'score': Value('float64'), 'reference_same_cases': Value('null'), 'median_ms': Value('float64'), 'detail': {'field_accuracy': Value('float64'), 'case_exact_accuracy': Value('float64'), 'custom_metrics': {'ndcg_at_10': Value('float64'), 'mrr': Value('float64'), 'recall_at_10': Value('float64'), 'candidate_recall': Value('float64'), 'scorable_candidate_recall': Value('float64'), 'candidates_scored': Value('float64'), 'candidates_retrieved': Value('int64'), 'bm25_ndcg_at_10': Value('float64'), 'quality_quality': Value('float64'), 'quality_cost_usd': Value('float64'), 'quality_utility': Value('float64'), 'quality_oracle_optimal': Value('float64'), 'quality_utility_regret': Value('float64'), 'cost_aware_quality': Value('float64'), 'cost_aware_cost_usd': Value('float64'), 'cost_aware_utility': Value('float64'), 'cost_aware_oracle_optimal': Value('float64'), 'cost_aware_utility_regret': Value('float64'), 'value_regret': Value('float64'), 'brier': Value('float64'), 'log_loss': Value('float64'), 'accuracy': Value('
...
lue('int64'), 'mean': Value('float64')}}, 'cluster_macro_accuracy': Value('float64'), 'positive_f1_by_field': {'sarcastic': Value('float64'), 'sarcasm': Value('float64'), 'irony': Value('float64'), 'satire': Value('float64'), 'understatement': Value('float64'), 'overstatement': Value('float64'), 'rhetorical_question': Value('float64')}, 'category_macro_f1': Value('float64'), 'scored_fields': Value('int64'), 'chance_on_rows': Value('float64')}, 'tracks': {'RouterBench-0shot': {'metric': Value('string'), 'score': Value('float64'), 'scored_requests': Value('int64')}, 'RouterBench-5shot': {'metric': Value('string'), 'score': Value('float64'), 'scored_requests': Value('int64')}, 'GSM8K-10choice': {'metric': Value('string'), 'score': Value('float64'), 'scored_requests': Value('int64')}, 'GSM8K-4choice': {'metric': Value('string'), 'score': Value('float64'), 'scored_requests': Value('int64')}, 'iSarcasmEval-A-Ar': {'metric': Value('string'), 'score': Value('null'), 'scored_requests': Value('int64')}, 'iSarcasmEval-A-En': {'metric': Value('string'), 'score': Value('null'), 'scored_requests': Value('int64')}, 'iSarcasmEval-B-En': {'metric': Value('string'), 'score': Value('null'), 'scored_requests': Value('int64')}, 'iSarcasmEval-C-Ar': {'metric': Value('string'), 'score': Value('null'), 'scored_requests': Value('int64')}, 'iSarcasmEval-C-En': {'metric': Value('string'), 'score': Value('null'), 'scored_requests': Value('int64')}}}), 'note': Value('string'), 'edition': Value('string')}
because column names don't match
Traceback: 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 2951, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2461, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2486, 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 547, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 430, 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 343, 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 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
model: string
adapter: struct<repo: string, branch: string, revision: string, served: string>
child 0, repo: string
child 1, branch: string
child 2, revision: string
child 3, served: string
base: struct<repo: string, revision: string>
child 0, repo: string
child 1, revision: string
code: struct<repo: string, tag: string, package: string, server: string, readout_map: string, readout_map_ (... 15 chars omitted)
child 0, repo: string
child 1, tag: string
child 2, package: string
child 3, server: string
child 4, readout_map: string
child 5, readout_map_sha256: string
suite: struct<kit: string, kit_commit: string, edition: string, rows: int64>
child 0, kit: string
child 1, kit_commit: string
child 2, edition: string
child 3, rows: int64
run: struct<where: string, rows_by_source: struct<partner: int64, hopit: int64>, hopit_benchmarks: list<i (... 48 chars omitted)
child 0, where: string
child 1, rows_by_source: struct<partner: int64, hopit: int64>
child 0, partner: int64
child 1, hopit: int64
child 2, hopit_benchmarks: list<item: string>
child 0, item: string
child 3, requests: string
child 4, timings: string
status_counts: struct<ok: int64>
child 0, ok: int64
results: struct<file: string, sha256: string, uncompressed_sha256: string>
child 0, file: string
child 1, sha256: string
child 2, uncompressed_sha256: string
scores: struct<decision_index: double, raw_index: double>
child 0, decision_index: double
child 1
...
int64
child 3, GSM8K-4choice: struct<metric: string, score: double, scored_requests: int64>
child 0, metric: string
child 1, score: double
child 2, scored_requests: int64
child 4, iSarcasmEval-A-Ar: struct<metric: string, score: null, scored_requests: int64>
child 0, metric: string
child 1, score: null
child 2, scored_requests: int64
child 5, iSarcasmEval-A-En: struct<metric: string, score: null, scored_requests: int64>
child 0, metric: string
child 1, score: null
child 2, scored_requests: int64
child 6, iSarcasmEval-B-En: struct<metric: string, score: null, scored_requests: int64>
child 0, metric: string
child 1, score: null
child 2, scored_requests: int64
child 7, iSarcasmEval-C-Ar: struct<metric: string, score: null, scored_requests: int64>
child 0, metric: string
child 1, score: null
child 2, scored_requests: int64
child 8, iSarcasmEval-C-En: struct<metric: string, score: null, scored_requests: int64>
child 0, metric: string
child 1, score: null
child 2, scored_requests: int64
successful_request_latency_ms: struct<median: int64, p95: int64, mean: int64>
child 0, median: int64
child 1, p95: int64
child 2, mean: int64
edition: string
note: string
engine: string
to
{'engine': Value('string'), 'counts': {'ok': Value('int64')}, 'successful_request_latency_ms': {'median': Value('int64'), 'p95': Value('int64'), 'mean': Value('int64')}, 'benchmarks': List({'catalog_id': Value('int64'), 'dataset': Value('string'), 'requests': Value('int64'), 'answered': Value('int64'), 'unsupported': Value('int64'), 'errors': Value('int64'), 'abstained': Value('int64'), 'pending': Value('int64'), 'scored_requests': Value('int64'), 'metric': Value('string'), 'score': Value('float64'), 'reference_same_cases': Value('null'), 'median_ms': Value('float64'), 'detail': {'field_accuracy': Value('float64'), 'case_exact_accuracy': Value('float64'), 'custom_metrics': {'ndcg_at_10': Value('float64'), 'mrr': Value('float64'), 'recall_at_10': Value('float64'), 'candidate_recall': Value('float64'), 'scorable_candidate_recall': Value('float64'), 'candidates_scored': Value('float64'), 'candidates_retrieved': Value('int64'), 'bm25_ndcg_at_10': Value('float64'), 'quality_quality': Value('float64'), 'quality_cost_usd': Value('float64'), 'quality_utility': Value('float64'), 'quality_oracle_optimal': Value('float64'), 'quality_utility_regret': Value('float64'), 'cost_aware_quality': Value('float64'), 'cost_aware_cost_usd': Value('float64'), 'cost_aware_utility': Value('float64'), 'cost_aware_oracle_optimal': Value('float64'), 'cost_aware_utility_regret': Value('float64'), 'value_regret': Value('float64'), 'brier': Value('float64'), 'log_loss': Value('float64'), 'accuracy': Value('
...
lue('int64'), 'mean': Value('float64')}}, 'cluster_macro_accuracy': Value('float64'), 'positive_f1_by_field': {'sarcastic': Value('float64'), 'sarcasm': Value('float64'), 'irony': Value('float64'), 'satire': Value('float64'), 'understatement': Value('float64'), 'overstatement': Value('float64'), 'rhetorical_question': Value('float64')}, 'category_macro_f1': Value('float64'), 'scored_fields': Value('int64'), 'chance_on_rows': Value('float64')}, 'tracks': {'RouterBench-0shot': {'metric': Value('string'), 'score': Value('float64'), 'scored_requests': Value('int64')}, 'RouterBench-5shot': {'metric': Value('string'), 'score': Value('float64'), 'scored_requests': Value('int64')}, 'GSM8K-10choice': {'metric': Value('string'), 'score': Value('float64'), 'scored_requests': Value('int64')}, 'GSM8K-4choice': {'metric': Value('string'), 'score': Value('float64'), 'scored_requests': Value('int64')}, 'iSarcasmEval-A-Ar': {'metric': Value('string'), 'score': Value('null'), 'scored_requests': Value('int64')}, 'iSarcasmEval-A-En': {'metric': Value('string'), 'score': Value('null'), 'scored_requests': Value('int64')}, 'iSarcasmEval-B-En': {'metric': Value('string'), 'score': Value('null'), 'scored_requests': Value('int64')}, 'iSarcasmEval-C-Ar': {'metric': Value('string'), 'score': Value('null'), 'scored_requests': Value('int64')}, 'iSarcasmEval-C-En': {'metric': Value('string'), 'score': Value('null'), 'scored_requests': Value('int64')}}}), 'note': Value('string'), 'edition': Value('string')}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Hopper 12B (trained): Decision Index run
A complete run of Decision Index edition 0.2.1 (150,317 scoreable requests) for Hopper 12B (trained).
The model is a LoRA adapter, HopitAI/hopper
on branch hopper-12b at revision 4b605f25eb903c2cf4d121040ffb097240b3bc37, on
google/gemma-4-12B-it at revision 707f0a3b8a3c7ad586ed01e27eafbad8a27dd0f7.
Scored with apolinario/decision-index at 87d4650:
Decision Index 0.2.1 47.52 (raw 59.99). All 150,317 requests were answered (status ok).
| Area | Skill |
|---|---|
| Knowledge & Reasoning | 30.5 |
| Language Understanding | 53.2 |
| Retrieval & Classification | 52.9 |
| Tools & Automation | 66.2 |
| Arts & Human Taste | 32.0 |
How the run was made
- Where: run on partner hardware, except eight benchmarks we ran ourselves with the same package and
adapter revision (Humicroedit, cfcolor, GPQA Diamond, ChessBench, Amazon ESCI, HLE, Habermas Machine,
HoVer). Each row records its source (
partnerorhopit). - Code: our public serving package, hopper at tag
12b-1.1.0(package 1.2.0),hopper-serve --base-loader gemma-4-12b-it, with the readout map shipped in the package. - Requests: one
/v1/systemonerequest per question. Questions with more than 26 options use the package's disclosed tournament shortlist (k = 10). - Adapter: served unmerged, as the package does.
- Inputs: the partner's rows come from a local rebuild of the 0.2.1 suite with the kit. With the kit's payload hash, 125,779 of those 126,316 rows are byte-identical to the official build; 537 differ in rendering (API-Bank 508, BFCL 10, When2Call 19). Re-running 450 of the API-Bank rows on the official inputs gave the same top answer for 95.1% of them and changed the Index by −0.05.
- Timings: the published rows carry no timestamps, and wall-time fields are set to 0.
We checked that every request ID of the edition is present exactly once and that every response is
valid for its question before scoring. runs/hopper-12b-trained/environment.json lists the weights,
code, suite and file hashes.
Versus the untrained base
Note (2026-10-05): We have not run the untrained base model (google/gemma-4-12B-it with the same serving package, prompt and readout) through the full suite. A 2,000-request sample we scored earlier projected it at about 48.7, with wide uncertainty (roughly 44–53). This trained adapter may therefore score below its own base under our serving. We will publish a full measurement if we run one.
These files are model outputs only and do not include the benchmark texts. The benchmarks keep their own upstream licences.
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