Datasets:

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arcadia-impact

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scimt-dispatch-sft-4epoch-v1

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Arcadia Impact 8

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
_data_files: list<item: struct<filename: string>>
  child 0, item: struct<filename: string>
      child 0, filename: string
_fingerprint: string
_format_columns: null
_format_kwargs: struct<>
_format_type: null
_output_all_columns: bool
_split: null
state: string
model: string
run_name: string
pointer_file: string
note: string
sampler: string
backend: string
state_path: string
train: struct<data: string, dataset_meta: struct<repo: string, revision: string, source_rows: int64, filter (... 179 chars omitted)
  child 0, data: string
  child 1, dataset_meta: struct<repo: string, revision: string, source_rows: int64, filtered_rows: int64, seed: int64, finger (... 61 chars omitted)
      child 0, repo: string
      child 1, revision: string
      child 2, source_rows: int64
      child 3, filtered_rows: int64
      child 4, seed: int64
      child 5, fingerprint: string
      child 6, filter: string
      child 7, materialized_at: timestamp[s]
  child 2, stage: string
  child 3, seed: int64
  child 4, load_checkpoint_path: string
  child 5, grpo: null
  child 6, lora: null
kind: null
sampler_path: string
spec: null
experiment: string
meta: struct<experiment: string, spec: null, kind: null, note: string, train: struct<data: string, dataset (... 292 chars omitted)
  child 0, experiment: string
  child 1, spec: null
  child 2, kind: null
  child 3, note: string
  child 4, train: struct<data: string, dataset_meta: struct<repo: string, revision: string, source_rows: int64, filter (... 179 chars omitted)
      child 0, data: string
      child 1, dataset_meta: struct<repo: string, revision: string, source_rows: int64, filtered_rows: int64, seed: int64, finger (... 61 chars omitted)
          child 0, repo: string
          child 1, revision: string
          child 2, source_rows: int64
          child 3, filtered_rows: int64
          child 4, seed: int64
          child 5, fingerprint: string
          child 6, filter: string
          child 7, materialized_at: timestamp[s]
      child 2, stage: string
      child 3, seed: int64
      child 4, load_checkpoint_path: string
      child 5, grpo: null
      child 6, lora: null
  child 5, run_name: string
  child 6, pointer_file: string
to
{'experiment': Value('string'), 'spec': Value('null'), 'kind': Value('null'), 'note': Value('string'), 'train': {'data': Value('string'), 'dataset_meta': {'repo': Value('string'), 'revision': Value('string'), 'source_rows': Value('int64'), 'filtered_rows': Value('int64'), 'seed': Value('int64'), 'fingerprint': Value('string'), 'filter': Value('string'), 'materialized_at': Value('timestamp[s]')}, 'stage': Value('string'), 'seed': Value('int64'), 'load_checkpoint_path': Value('string'), 'grpo': Value('null'), 'lora': Value('null')}, 'run_name': Value('string'), 'pointer_file': Value('string'), 'backend': Value('string'), 'sampler': Value('string'), 'state': Value('string'), 'model': Value('string'), 'meta': {'experiment': Value('string'), 'spec': Value('null'), 'kind': Value('null'), 'note': Value('string'), 'train': {'data': Value('string'), 'dataset_meta': {'repo': Value('string'), 'revision': Value('string'), 'source_rows': Value('int64'), 'filtered_rows': Value('int64'), 'seed': Value('int64'), 'fingerprint': Value('string'), 'filter': Value('string'), 'materialized_at': Value('timestamp[s]')}, 'stage': Value('string'), 'seed': Value('int64'), 'load_checkpoint_path': Value('string'), 'grpo': Value('null'), 'lora': Value('null')}, 'run_name': Value('string'), 'pointer_file': Value('string')}, 'sampler_path': Value('string'), 'state_path': 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 489, 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 2818, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, 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 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, 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 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              _data_files: list<item: struct<filename: string>>
                child 0, item: struct<filename: string>
                    child 0, filename: string
              _fingerprint: string
              _format_columns: null
              _format_kwargs: struct<>
              _format_type: null
              _output_all_columns: bool
              _split: null
              state: string
              model: string
              run_name: string
              pointer_file: string
              note: string
              sampler: string
              backend: string
              state_path: string
              train: struct<data: string, dataset_meta: struct<repo: string, revision: string, source_rows: int64, filter (... 179 chars omitted)
                child 0, data: string
                child 1, dataset_meta: struct<repo: string, revision: string, source_rows: int64, filtered_rows: int64, seed: int64, finger (... 61 chars omitted)
                    child 0, repo: string
                    child 1, revision: string
                    child 2, source_rows: int64
                    child 3, filtered_rows: int64
                    child 4, seed: int64
                    child 5, fingerprint: string
                    child 6, filter: string
                    child 7, materialized_at: timestamp[s]
                child 2, stage: string
                child 3, seed: int64
                child 4, load_checkpoint_path: string
                child 5, grpo: null
                child 6, lora: null
              kind: null
              sampler_path: string
              spec: null
              experiment: string
              meta: struct<experiment: string, spec: null, kind: null, note: string, train: struct<data: string, dataset (... 292 chars omitted)
                child 0, experiment: string
                child 1, spec: null
                child 2, kind: null
                child 3, note: string
                child 4, train: struct<data: string, dataset_meta: struct<repo: string, revision: string, source_rows: int64, filter (... 179 chars omitted)
                    child 0, data: string
                    child 1, dataset_meta: struct<repo: string, revision: string, source_rows: int64, filtered_rows: int64, seed: int64, finger (... 61 chars omitted)
                        child 0, repo: string
                        child 1, revision: string
                        child 2, source_rows: int64
                        child 3, filtered_rows: int64
                        child 4, seed: int64
                        child 5, fingerprint: string
                        child 6, filter: string
                        child 7, materialized_at: timestamp[s]
                    child 2, stage: string
                    child 3, seed: int64
                    child 4, load_checkpoint_path: string
                    child 5, grpo: null
                    child 6, lora: null
                child 5, run_name: string
                child 6, pointer_file: string
              to
              {'experiment': Value('string'), 'spec': Value('null'), 'kind': Value('null'), 'note': Value('string'), 'train': {'data': Value('string'), 'dataset_meta': {'repo': Value('string'), 'revision': Value('string'), 'source_rows': Value('int64'), 'filtered_rows': Value('int64'), 'seed': Value('int64'), 'fingerprint': Value('string'), 'filter': Value('string'), 'materialized_at': Value('timestamp[s]')}, 'stage': Value('string'), 'seed': Value('int64'), 'load_checkpoint_path': Value('string'), 'grpo': Value('null'), 'lora': Value('null')}, 'run_name': Value('string'), 'pointer_file': Value('string'), 'backend': Value('string'), 'sampler': Value('string'), 'state': Value('string'), 'model': Value('string'), 'meta': {'experiment': Value('string'), 'spec': Value('null'), 'kind': Value('null'), 'note': Value('string'), 'train': {'data': Value('string'), 'dataset_meta': {'repo': Value('string'), 'revision': Value('string'), 'source_rows': Value('int64'), 'filtered_rows': Value('int64'), 'seed': Value('int64'), 'fingerprint': Value('string'), 'filter': Value('string'), 'materialized_at': Value('timestamp[s]')}, 'stage': Value('string'), 'seed': Value('int64'), 'load_checkpoint_path': Value('string'), 'grpo': Value('null'), 'lora': Value('null')}, 'run_name': Value('string'), 'pointer_file': Value('string')}, 'sampler_path': Value('string'), 'state_path': Value('string')}
              because column names don't match