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
engine: string
engine_options: struct<model: string, revision: string, dtype: string>
child 0, model: string
child 1, revision: string
child 2, dtype: string
model_source: struct<kind: string, repo: string, revision: string, device: string, dtype: string, compile: bool, m (... 33 chars omitted)
child 0, kind: string
child 1, repo: string
child 2, revision: string
child 3, device: string
child 4, dtype: string
child 5, compile: bool
child 6, max_pixels: int64
child 7, policy: string
torch: string
transformers: string
device: string
hip: string
cuda: null
gpu: string
loaded_seconds: double
frozen_corpus_sha256: string
rows_path: list<item: string>
child 0, item: string
runner_sha256: string
latency: string
successful_request_latency_ms: struct<median: double, p95: double, mean: double>
child 0, median: double
child 1, p95: double
child 2, mean: double
counts: struct<ok: int64>
child 0, ok: int64
benchmarks: list<item: struct<catalog_id: int64, dataset: string, requests: int64, answered: int64, unsupported: (... 6325 chars omitted)
child 0, item: struct<catalog_id: int64, dataset: string, requests: int64, answered: int64, unsupported: int64, err (... 6313 chars omitted)
child 0, catalog_id: int64
child 1, dataset: string
child 2, requests: int64
child 3, answered: int64
child 4, unsupported: int64
child 5, errors: int64
child 6, abstained: int64
child 7, pending: int64
child 8, scored_request
...
ric: string, score: null, scored_requests: int64>
child 0, metric: string
child 1, score: null
child 2, scored_requests: int64
child 3, 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 4, 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 5, 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 6, iSarcasmEval-C-En: struct<metric: string, score: null, scored_requests: int64>
child 0, metric: string
child 1, score: null
child 2, scored_requests: int64
child 7, GSM8K-10choice: struct<metric: string, score: double, scored_requests: int64>
child 0, metric: string
child 1, score: double
child 2, scored_requests: int64
child 8, GSM8K-4choice: struct<metric: string, score: double, scored_requests: int64>
child 0, metric: string
child 1, score: double
child 2, scored_requests: int64
edition: string
note: string
to
{'engine': Value('string'), 'counts': {'ok': Value('int64')}, 'successful_request_latency_ms': {'median': Value('float64'), 'p95': Value('float64'), 'mean': Value('float64')}, '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'), 'normalized_rank_regret': Value('float64')}, 'positive_micro_f1': Valu
...
Value('int64'), 'mean': Value('int64')}}, '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')}, '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')}, 'GSM8K-10choice': {'metric': Value('string'), 'score': Value('float64'), 'scored_requests': Value('int64')}, 'GSM8K-4choice': {'metric': Value('string'), 'score': Value('float64'), '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 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
engine: string
engine_options: struct<model: string, revision: string, dtype: string>
child 0, model: string
child 1, revision: string
child 2, dtype: string
model_source: struct<kind: string, repo: string, revision: string, device: string, dtype: string, compile: bool, m (... 33 chars omitted)
child 0, kind: string
child 1, repo: string
child 2, revision: string
child 3, device: string
child 4, dtype: string
child 5, compile: bool
child 6, max_pixels: int64
child 7, policy: string
torch: string
transformers: string
device: string
hip: string
cuda: null
gpu: string
loaded_seconds: double
frozen_corpus_sha256: string
rows_path: list<item: string>
child 0, item: string
runner_sha256: string
latency: string
successful_request_latency_ms: struct<median: double, p95: double, mean: double>
child 0, median: double
child 1, p95: double
child 2, mean: double
counts: struct<ok: int64>
child 0, ok: int64
benchmarks: list<item: struct<catalog_id: int64, dataset: string, requests: int64, answered: int64, unsupported: (... 6325 chars omitted)
child 0, item: struct<catalog_id: int64, dataset: string, requests: int64, answered: int64, unsupported: int64, err (... 6313 chars omitted)
child 0, catalog_id: int64
child 1, dataset: string
child 2, requests: int64
child 3, answered: int64
child 4, unsupported: int64
child 5, errors: int64
child 6, abstained: int64
child 7, pending: int64
child 8, scored_request
...
ric: string, score: null, scored_requests: int64>
child 0, metric: string
child 1, score: null
child 2, scored_requests: int64
child 3, 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 4, 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 5, 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 6, iSarcasmEval-C-En: struct<metric: string, score: null, scored_requests: int64>
child 0, metric: string
child 1, score: null
child 2, scored_requests: int64
child 7, GSM8K-10choice: struct<metric: string, score: double, scored_requests: int64>
child 0, metric: string
child 1, score: double
child 2, scored_requests: int64
child 8, GSM8K-4choice: struct<metric: string, score: double, scored_requests: int64>
child 0, metric: string
child 1, score: double
child 2, scored_requests: int64
edition: string
note: string
to
{'engine': Value('string'), 'counts': {'ok': Value('int64')}, 'successful_request_latency_ms': {'median': Value('float64'), 'p95': Value('float64'), 'mean': Value('float64')}, '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'), 'normalized_rank_regret': Value('float64')}, 'positive_micro_f1': Valu
...
Value('int64'), 'mean': Value('int64')}}, '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')}, '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')}, 'GSM8K-10choice': {'metric': Value('string'), 'score': Value('float64'), 'scored_requests': Value('int64')}, 'GSM8K-4choice': {'metric': Value('string'), 'score': Value('float64'), '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.
d1 models on the Decision Index
Complete runs of Liquid AI's d1 decision models through the Decision Index kit, scored by it: the public 0.3 suite, which the 0.4 board takes as it is, and the public vision suite.
Decision Index (public 0.3 suite)
| model | served from | public index | answered | kit | run |
|---|---|---|---|---|---|
| d1-2B | LiquidAI/d1-2B at c133b68 |
51.10 | 100% | 25c6ec7 |
runs/d1-2B |
| d1-Omni-2B | LiquidAI/d1-Omni-2B at 28dd9f7 |
49.02 | 100% | 25c6ec7 |
runs/d1-Omni-2B |
| d1-3B | LiquidAI/d1-3B at da1fe36 |
48.99 | 100% | 62d2f51 |
runs/d1-3B |
| d1-omni-600M | LiquidAI/d1-omni-600M at 414f8d6 |
14.64 | 73.5% | 62d2f51 |
runs/d1-omni-600M |
Vision (public vision suite)
| model | served from | public vision index | answered | kit | run |
|---|---|---|---|---|---|
| d1-2B | LiquidAI/d1-2B at c133b68 |
64.60 | 100% | 25c6ec7 |
runs/vision-d1-2B |
| d1-Omni-2B | LiquidAI/d1-Omni-2B at 28dd9f7 |
59.13 | 100% | 25c6ec7 |
runs/vision-d1-Omni-2B |
Each run directory holds the kit's own files, unchanged: results.jsonl.gz (compact), the scores (scores.json,
index.json and benchmark-summary.json, or vision-scores.json), environment.json and status.json.
How each model was run
| engine | settings | hardware | |
|---|---|---|---|
| d1-2B, d1-Omni-2B | d1_engine:D1 (code/d1_engine.py) |
dtype=bfloat16, eager, one request at a time |
1 x AMD Instinct MI325X, torch 2.13 (ROCm 7.1), transformers 5.19.0 |
| d1-3B | same | dtype=bfloat16, eager |
same |
| d1-omni-600M | same | dtype=float16, eager |
same |
d1_engine:D1 loads the model from the Hub with its own code (trust_remote_code=True) and answers every request
with model.system_one, the call documented on the model card: pictures go in as media= on d1-2B and d1-Omni-2B,
images= on the earlier models. Nothing is shortened: a request the model cannot read whole is recorded as
unsupported.
- d1-2B and d1-Omni-2B read every request whole, text and pictures. On the vision suite each picture keeps its
native resolution up to 1,638,400 pixels, the kit's cap and the models' own, and a request whose pictures are the
whole state is asked with
state=None, as the cards show. - d1-omni-600M reads at most 16,384 positions, and its encoder keeps a question's instructions and option texts to a fixed token budget. A request that its encoder would cut in any way is unsupported: 37,141 of 140,178 scored requests.
Reproduce
With the suites built as the kit's README describes (suite rebuild, then suite rebuild --vision):
pip install "decision-index[vision] @ git+https://github.com/apolinario/decision-index@25c6ec7" "transformers==5.19.0" torch
cp <this repo>/code/d1_engine.py .
python -m decision_index pipeline --edition 0.3 --engine d1_engine:D1 --compact \
--option model=LiquidAI/d1-2B --option revision=c133b68410b748524c9cb95acafa7b7cf4f9c3d5 \
--option dtype=bfloat16 --out runs/d1-2B
python -m decision_index vision pipeline --dir work/vision --engine d1_engine:D1 --workers 1 --compact \
--option model=LiquidAI/d1-2B --option revision=c133b68410b748524c9cb95acafa7b7cf4f9c3d5 \
--option dtype=bfloat16 --out runs/vision-d1-2B
The same commands with LiquidAI/d1-Omni-2B at 28dd9f76b3d410b198808a806389aabe4957cedc give its runs. d1-3B and
d1-omni-600M were run with the kit at 62d2f51; their commands are in the dataset's history at 9fcaa92.
- Downloads last month
- 437