Datasets:

LiquidAI
/

d1-decision-index

License:

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Split (1)

train

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, p (... 14 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, 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
edition: string
counts: struct<ok: int64>
  child 0, ok: int64
note: string
successful_request_latency_ms: struct<median: double, p95: double, mean: double>
  child 0, median: double
  child 1, p95: double
  child 2, mean: double
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
...
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 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
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 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
              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, p (... 14 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, 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
              edition: string
              counts: struct<ok: int64>
                child 0, ok: int64
              note: string
              successful_request_latency_ms: struct<median: double, p95: double, mean: double>
                child 0, median: double
                child 1, p95: double
                child 2, mean: double
              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
              ...
              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 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
              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

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d1 models on the Decision Index 0.3

Complete runs of the public 0.3 suite for two Liquid AI decision models, made with the Decision Index kit at 62d2f51 (branch v0.3) and scored by it.

model	served from	public index (0.3)	answered	run
d1-3B	`LiquidAI/d1-3B` at `da1fe36`	48.99	100%	`runs/d1-3B`
d1-omni-600M	`LiquidAI/d1-omni-600M` at `414f8d6`	14.64	73.5%	`runs/d1-omni-600M`

Each run directory holds the kit's own files: results.jsonl.gz (compact), scores.json, index.json, benchmark-summary.json, environment.json and status.json.

How each model was run

	engine	settings	hardware
d1-3B	`d1_engine:D1` (`code/d1_engine.py`)	`dtype=bfloat16`, eager	1 x AMD Instinct MI325X, torch 2.13 (ROCm 7.1), transformers 5.19.0
d1-omni-600M	`d1_engine:D1` (`code/d1_engine.py`)	`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(state, questions), the call documented on the model card. Nothing is shortened: a request the model cannot read whole is recorded as unsupported.

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 (state, instructions or an option text) is unsupported: 37,141 of 140,178 scored requests. Its default batching needs about 52 GB of GPU memory on the longest requests.
d1-3B reads every request whole. --option compile=true (CUDA graphs, NVIDIA only) changes 0.1% of top answers and is no faster on the varied shapes of this suite.

Reproduce

With the suite built as the kit's README describes:

git clone -b v0.3 https://github.com/apolinario/decision-index && cd decision-index && git checkout 62d2f51
pip install -e . "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/d1-3B --option revision=da1fe36a861f24690f27f622dca1d8688503d113 \
    --option dtype=bfloat16 --out runs/d1-3B
python -m decision_index pipeline --edition 0.3 --engine d1_engine:D1 --compact \
    --option model=LiquidAI/d1-omni-600M --option revision=414f8d6438174f5b2133a9c21a478fc42625e308 \
    --option dtype=float16 --out runs/d1-omni-600M

Images

Both models read images. D1(...)(state, questions, images=[...]) takes PIL images, file paths, raw bytes or data: URLs. With images, d1-omni-600M keeps 896 text tokens, and the engine applies the same rule as above.

Downloads last month: -

Total file size:

68.4 MB