Datasets:

openvulcan
/

vulcan-decision-shell-v1-data

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
complete: bool
status: string
source_model_sha256: string
files: struct<training-records.jsonl.gz: struct<bytes: int64, sha256: string>, source-occurrences.jsonl.gz: (... 38 chars omitted)
  child 0, training-records.jsonl.gz: struct<bytes: int64, sha256: string>
      child 0, bytes: int64
      child 1, sha256: string
  child 1, source-occurrences.jsonl.gz: struct<bytes: int64, sha256: string>
      child 0, bytes: int64
      child 1, sha256: string
counts: struct<unique_public_records: int64, prompt_roundtrip_exact: int64, eligible_source_rows: int64, pri (... 118 chars omitted)
  child 0, unique_public_records: int64
  child 1, prompt_roundtrip_exact: int64
  child 2, eligible_source_rows: int64
  child 3, privacy_pattern_excluded: int64
  child 4, privacy:email_address: int64
  child 5, public_source_occurrences: int64
  child 6, holdout_excluded: int64
caches: list<item: struct<path: string, sha256: string, references: list<item: struct<version: string, role: (... 178 chars omitted)
  child 0, item: struct<path: string, sha256: string, references: list<item: struct<version: string, role: string>>,  (... 166 chars omitted)
      child 0, path: string
      child 1, sha256: string
      child 2, references: list<item: struct<version: string, role: string>>
          child 0, item: struct<version: string, role: string>
              child 0, version: string
              child 1, role: string
      child 3, counts: struct<eligible_source_rows: int64, privacy_pattern_exclu
...
ldout_excluded: int64
protocols: list<item: struct<version: string, path: string, sha256: string, parent: string, public_path: string (... 2 chars omitted)
  child 0, item: struct<version: string, path: string, sha256: string, parent: string, public_path: string>
      child 0, version: string
      child 1, path: string
      child 2, sha256: string
      child 3, parent: string
      child 4, public_path: string
source_role: string
private_evaluation_databases_opened: int64
heldout_records_published: int64
training_updates: int64
commands_executed: int64
exact_bit_retraining_claimed: bool
epoch: double
source_splits: struct<train: int64, <absent>: int64, fit: int64>
  child 0, train: int64
  child 1, <absent>: int64
  child 2, fit: int64
exact_token_roundtrips_not_repeated_already_verified: int64
private_databases_opened: bool
same_prompt_multiple_historical_decision_labels: int64
conflicting_prompt_hashes: list<item: null>
  child 0, item: null
source_appearances: int64
privacy_pattern_findings: int64
legacy_164m_199m_not_reencoded_or_added_to_Qwen_unique_count: bool
distinct_prompt_and_supervision_pairs: int64
distinct_Qwen_prompt_texts: int64
exception_exact_fixture: string
all_candidates_actually_consumed_by_optimizer_claimed: bool
bindings: list<item: struct<path: string, bytes: int64, sha256: string>>
  child 0, item: struct<path: string, bytes: int64, sha256: string>
      child 0, path: string
      child 1, bytes: int64
      child 2, sha256: string
seconds: double
to
{'complete': Value('bool'), 'counts': {'early_pool': {'records': Value('int64'), 'already_present_same_prompt_and_supervision': Value('int64'), 'decision_label_2': Value('int64'), 'decision_label_0': Value('int64'), 'decision_label_1': Value('int64')}, 'ancestral_additions': {'records': Value('int64'), 'already_present_same_prompt_and_supervision': Value('int64'), 'decision_label_1': Value('int64'), 'decision_label_0': Value('int64'), 'decision_label_2': Value('int64'), 'auxiliary_records': Value('int64')}, 'scp_fit': {'records': Value('int64'), 'already_present_same_prompt_and_supervision': Value('int64'), 'decision_label_2': Value('int64'), 'decision_label_0': Value('int64')}}, 'distinct_Qwen_prompt_texts': Value('int64'), 'distinct_prompt_and_supervision_pairs': Value('int64'), 'same_prompt_multiple_historical_decision_labels': Value('int64'), 'conflicting_prompt_hashes': List(Value('null')), 'source_appearances': Value('int64'), 'source_splits': {'train': Value('int64'), '<absent>': Value('int64'), 'fit': Value('int64')}, 'privacy_pattern_findings': Value('int64'), 'exception_exact_fixture': Value('string'), 'bindings': List({'path': Value('string'), 'bytes': Value('int64'), 'sha256': Value('string')}), 'legacy_164m_199m_not_reencoded_or_added_to_Qwen_unique_count': Value('bool'), 'exact_token_roundtrips_not_repeated_already_verified': Value('int64'), 'private_databases_opened': Value('bool'), 'all_candidates_actually_consumed_by_optimizer_claimed': Value('bool'), 'seconds': Value('float64'), 'training_updates': 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
              complete: bool
              status: string
              source_model_sha256: string
              files: struct<training-records.jsonl.gz: struct<bytes: int64, sha256: string>, source-occurrences.jsonl.gz: (... 38 chars omitted)
                child 0, training-records.jsonl.gz: struct<bytes: int64, sha256: string>
                    child 0, bytes: int64
                    child 1, sha256: string
                child 1, source-occurrences.jsonl.gz: struct<bytes: int64, sha256: string>
                    child 0, bytes: int64
                    child 1, sha256: string
              counts: struct<unique_public_records: int64, prompt_roundtrip_exact: int64, eligible_source_rows: int64, pri (... 118 chars omitted)
                child 0, unique_public_records: int64
                child 1, prompt_roundtrip_exact: int64
                child 2, eligible_source_rows: int64
                child 3, privacy_pattern_excluded: int64
                child 4, privacy:email_address: int64
                child 5, public_source_occurrences: int64
                child 6, holdout_excluded: int64
              caches: list<item: struct<path: string, sha256: string, references: list<item: struct<version: string, role: (... 178 chars omitted)
                child 0, item: struct<path: string, sha256: string, references: list<item: struct<version: string, role: string>>,  (... 166 chars omitted)
                    child 0, path: string
                    child 1, sha256: string
                    child 2, references: list<item: struct<version: string, role: string>>
                        child 0, item: struct<version: string, role: string>
                            child 0, version: string
                            child 1, role: string
                    child 3, counts: struct<eligible_source_rows: int64, privacy_pattern_exclu
              ...
              ldout_excluded: int64
              protocols: list<item: struct<version: string, path: string, sha256: string, parent: string, public_path: string (... 2 chars omitted)
                child 0, item: struct<version: string, path: string, sha256: string, parent: string, public_path: string>
                    child 0, version: string
                    child 1, path: string
                    child 2, sha256: string
                    child 3, parent: string
                    child 4, public_path: string
              source_role: string
              private_evaluation_databases_opened: int64
              heldout_records_published: int64
              training_updates: int64
              commands_executed: int64
              exact_bit_retraining_claimed: bool
              epoch: double
              source_splits: struct<train: int64, <absent>: int64, fit: int64>
                child 0, train: int64
                child 1, <absent>: int64
                child 2, fit: int64
              exact_token_roundtrips_not_repeated_already_verified: int64
              private_databases_opened: bool
              same_prompt_multiple_historical_decision_labels: int64
              conflicting_prompt_hashes: list<item: null>
                child 0, item: null
              source_appearances: int64
              privacy_pattern_findings: int64
              legacy_164m_199m_not_reencoded_or_added_to_Qwen_unique_count: bool
              distinct_prompt_and_supervision_pairs: int64
              distinct_Qwen_prompt_texts: int64
              exception_exact_fixture: string
              all_candidates_actually_consumed_by_optimizer_claimed: bool
              bindings: list<item: struct<path: string, bytes: int64, sha256: string>>
                child 0, item: struct<path: string, bytes: int64, sha256: string>
                    child 0, path: string
                    child 1, bytes: int64
                    child 2, sha256: string
              seconds: double
              to
              {'complete': Value('bool'), 'counts': {'early_pool': {'records': Value('int64'), 'already_present_same_prompt_and_supervision': Value('int64'), 'decision_label_2': Value('int64'), 'decision_label_0': Value('int64'), 'decision_label_1': Value('int64')}, 'ancestral_additions': {'records': Value('int64'), 'already_present_same_prompt_and_supervision': Value('int64'), 'decision_label_1': Value('int64'), 'decision_label_0': Value('int64'), 'decision_label_2': Value('int64'), 'auxiliary_records': Value('int64')}, 'scp_fit': {'records': Value('int64'), 'already_present_same_prompt_and_supervision': Value('int64'), 'decision_label_2': Value('int64'), 'decision_label_0': Value('int64')}}, 'distinct_Qwen_prompt_texts': Value('int64'), 'distinct_prompt_and_supervision_pairs': Value('int64'), 'same_prompt_multiple_historical_decision_labels': Value('int64'), 'conflicting_prompt_hashes': List(Value('null')), 'source_appearances': Value('int64'), 'source_splits': {'train': Value('int64'), '<absent>': Value('int64'), 'fit': Value('int64')}, 'privacy_pattern_findings': Value('int64'), 'exception_exact_fixture': Value('string'), 'bindings': List({'path': Value('string'), 'bytes': Value('int64'), 'sha256': Value('string')}), 'legacy_164m_199m_not_reencoded_or_added_to_Qwen_unique_count': Value('bool'), 'exact_token_roundtrips_not_repeated_already_verified': Value('int64'), 'private_databases_opened': Value('bool'), 'all_candidates_actually_consumed_by_optimizer_claimed': Value('bool'), 'seconds': Value('float64'), 'training_updates': Value('int64')}
              because column names don't match

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vulcan-decision-shell-v1-data

Training and validation banks of the vulcan-decision-shell-v1 command decision model.

English | 简体中文

This repository: vulcan-decision-shell-v1-data · Training and validation banks

The public training banks (575,693 Qwen-format prompts plus the legacy 164M/199M pools, the 26-stage parameter records and source provenance, plus v1-mini's last training stage) and the public validation banks (106,324 questions), with third-party source licenses. No training scripts and no model weights.

The vulcan-decision-shell-v1 release

The model is published in several formats; this dataset is shared by all of them, and every repository carries the same docs/ folder.

Repository	Format	Size	Status
vulcan-decision-shell-v1	PyTorch · ONNX	11.9 GB	v1 0.75B · every format, one folder each
vulcan-decision-shell-v1-flash	PyTorch	2.9 GB	v1-flash 0.36B · native PyTorch
vulcan-decision-shell-v1-mini	PyTorch	3.1 GB	v1-mini 0.2B · native PyTorch
vulcan-decision-shell-v1-runtime	ONNX	5.1 GB	ONNX packages of all three · used by VulcanCode and dsh
vulcan-decision-shell-v1-data (this repository)	Dataset	866 MB	Training and validation banks

Download

Install the command-line tool once:

pip install -U "huggingface_hub[cli]"

# Everything · 866 MB
hf download openvulcan/vulcan-decision-shell-v1-data --repo-type dataset --local-dir vulcan-decision-shell-v1-data

Path	Contents
`training-data/early-0.8b-sources/`	The early Qwen-format training pool, courses, facts, the legacy 164M/199M pools and source provenance
`training-data/ancestral-additions/`	Records added along the selected lineage, with per-stage training history
`training-data/scp-fit/`	The 2,080 SCP source/target/identity-role examples trained in v8055
`training-data/mini-v65/`	v1-mini's last training stage: 31,565 records already tokenized for ModernBERT, the 31-update schedule, the recipe and a line map with each record's hash (its earlier pools are the legacy 164M/199M pools above)
`training-data/INDEX.json`	Index of the v1 training banks
`validation/observed-regression.jsonl.gz`	51,922 questions that do not overlap the public training prompts but were observed during development
`validation/training-replay.jsonl.gz`	54,402 questions that overlap public training prompts or belong to training replay
`validation/manifest.json`	Public-subset matrices and file hashes
`licenses/`	Licenses of third-party sources and the attribution list

The Qwen-format sources hold 575,693 prompts and 576,397 prompt and supervision pairs. The legacy 164M/199M pools (731,373 old-tokenizer inputs from 110 sources) are listed separately and cannot simply be added.
The 106,324 public validation questions are not a blind test: both files were seen during development, and their reference labels were produced with AI help, not certified by humans.
483,680 real Codex/Claude command bodies are private and not included; 204 reference prompts were withheld for privacy. Retained questions were not edited.
No training scripts and no model weights. Details: docs/TRAINING-DATA.en.md, docs/VALIDATION.en.md, docs/TRAINING-HISTORY.en.md.

License

Our own material is released under the Apache License 2.0. Third-party sources keep their own licenses (atomic-red-team, nl2bash, SecLists, tldr-pages and others listed in licenses/public-import-attribution.json).

Downloads last month: 181

Total file size:

866 MB

vulcan-decision-shell-v1-data

The vulcan-decision-shell-v1 release

Download

Contents

License