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The dataset generation failed because of a cast error
Error code:   DatasetGenerationCastError
Exception:    DatasetGenerationCastError
Message:      An error occurred while generating the dataset

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This happened while the csv dataset builder was generating data using

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'hf://datasets/kv-compression/lingbot-va-attn@a51f9363a05b5e3223dd4fe8500f2488bb35e0c4/artifacts/attention/va-vv-attention-c10-s24/grab-the-medium-sized-white-mug-rotate-it-place-it-on-the-table-and-hook-it-onto-the-smooth-dark-gray-rack/step_024/vv/l28h09.csv', 'hf://datasets/kv-compression/lingbot-va-attn@a51f9363a05b5e3223dd4fe8500f2488bb35e0c4/artifacts/attention/va-vv-attention-c10-s24/grab-the-medium-sized-white-mug-rotate-it-place-it-on-the-table-and-hook-it-onto-the-smooth-dark-gray-rack/step_024/vv/l28h10.csv', 'hf://datasets/kv-compression/lingbot-va-attn@a51f9363a05b5e3223dd4fe8500f2488bb35e0c4/artifacts/attention/va-vv-attention-c10-s24/grab-the-medium-sized-white-mug-rotate-it-place-it-on-the-table-and-hook-it-onto-the-smooth-dark-gray-rack/step_024/vv/l28h11.csv', 'hf://datasets/kv-compression/lingbot-va-attn@a51f9363a05b5e3223dd4fe8500f2488bb35e0c4/artifacts/attention/va-vv-attention-c10-s24/grab-the-medium-sized-white-mug-rotate-it-place-it-on-the-table-and-hook-it-onto-the-smooth-dark-gray-rack/step_024/vv/l28h12.csv', 'hf://datasets/kv-compression/lingbot-va-attn@a51f9363a05b5e3223dd4fe8500f2488bb35e0c4/artifacts/attention/va-vv-attention-c10-s24/grab-the-medium-sized-white-mug-rotate-it-place-it-on-the-table-and-hook-it-onto-the-smooth-dark-gray-rack/step_024/vv/l28h13.csv', 'hf://datasets/kv-compression/lingbot-va-attn@a51f9363a05b5e3223dd4fe8500f2488bb35e0c4/artifacts/attention/va-vv-attention-c10-s24/grab-the-medium-sized-white-mug-rotate-it-place-it-on-the-table-and-hook-it-onto-the-smooth-dark-gray-rack/step_024/vv/l28h14.csv', 'hf://datasets/kv-compression/lingbot-va-attn@a51f9363a05b5e3223dd4fe8500f2488bb35e0c4/artifacts/attention/va-vv-attention-c10-s24/grab-the-medium-sized-white-mug-rotate-it-place-it-on-the-table-and-hook-it-onto-the-smooth-dark-gray-rack/step_024/vv/l28h15.csv', 'hf://datasets/kv-compression/lingbot-va-attn@a51f9363a05b5e3223dd4fe8500f2488bb35e0c4/artifacts/attention/va-vv-attention-c10-s24/grab-the-medium-sized-white-mug-rotate-it-place-it-on-the-table-and-hook-it-onto-the-smooth-dark-gray-rack/step_024/vv/l28h16.csv', 'hf://datasets/kv-compression/lingbot-va-attn@a51f9363a05b5e3223dd4fe8500f2488bb35e0c4/artifacts/attention/va-vv-attention-c10-s24/grab-the-medium-sized-white-mug-rotate-it-place-it-on-the-table-and-hook-it-onto-the-smooth-dark-gray-rack/step_024/vv/l28h17.csv', 'hf://datasets/kv-compression/lingbot-va-attn@a51f9363a05b5e3223dd4fe8500f2488bb35e0c4/artifacts/attention/va-vv-attention-c10-s24/grab-the-medium-sized-white-mug-rotate-it-place-it-on-the-table-and-hook-it-onto-the-smooth-dark-gray-rack/step_024/vv/l28h18.csv', 'hf://datasets/kv-compression/lingbot-va-attn@a51f9363a05b5e3223dd4fe8500f2488bb35e0c4/artifacts/attention/va-vv-attention-c10-s24/grab-the-medium-sized-white-mug-rotate-it-place-it-on-the-table-and-hook-it-onto-the-smooth-dark-gray-rack/step_024/vv/l28h19.csv', 'hf://datasets/kv-compression/lingbot-va-attn@a51f9363a05b5e3223dd4fe8500f2488bb35e0c4/artifacts/attention/va-vv-attention-c10-s24/grab-the-medium-sized-white-mug-rotate-it-place-it-on-the-table-and-hook-it-onto-the-smooth-dark-gray-rack/step_024/vv/l28h20.csv', 'hf://datasets/kv-compression/lingbot-va-attn@a51f9363a05b5e3223dd4fe8500f2488bb35e0c4/artifacts/attention/va-vv-attention-c10-s24/grab-the-medium-sized-white-mug-rotate-it-place-it-on-the-table-and-hook-it-onto-the-smooth-dark-gray-rack/step_024/vv/l28h21.csv', 'hf://datasets/kv-compression/lingbot-va-attn@a51f9363a05b5e3223dd4fe8500f2488bb35e0c4/artifacts/attention/va-vv-attention-c10-s24/grab-the-medium-sized-white-mug-rotate-it-place-it-on-the-table-and-hook-it-onto-the-smooth-dark-gray-rack/step_024/vv/l28h22.csv', 'hf://datasets/kv-compression/lingbot-va-attn@a51f9363a05b5e3223dd4fe8500f2488bb35e0c4/artifacts/attention/va-vv-attention-c10-s24/grab-the-medium-sized-white-mug-rotate-it-place-it-on-the-table-and-hook-it-onto-the-smooth-dark-gray-rack/step_024/vv/l28h23.csv', 'hf://datasets/kv-compression/lingbot-va-attn@a51f9363a05b5e3223dd4fe8500f2488bb35e0c4/artifacts/attention/va-vv-attention-c10-s24/grab-the-medium-sized-white-mug-rotate-it-place-it-on-the-table-and-hook-it-onto-the-smooth-dark-gray-rack/step_024/vv/l29h00.csv', 'hf://datasets/kv-compression/lingbot-va-attn@a51f9363a05b5e3223dd4fe8500f2488bb35e0c4/artifacts/attention/va-vv-attention-c10-s24/grab-the-medium-sized-white-mug-rotate-it-place-it-on-the-table-and-hook-it-onto-the-smooth-dark-gray-rack/step_024/vv/l29h01.csv', 'hf://datasets/kv-compression/lingbot-va-attn@a51f9363a05b5e3223dd4fe8500f2488bb35e0c4/artifacts/attention/va-vv-attention-c10-s24/grab-the-medium-sized-white-mug-rotate-it-place-it-on-the-table-and-hook-it-onto-the-smooth-dark-gray-rack/step_024/vv/l29h02.csv', 'hf://datasets/kv-compression/lingbot-va-attn@a51f9363a05b5e3223dd4fe8500f2488bb35e0c4/artifacts/attention/va-vv-attention-c10-s24/grab-the-medium-sized-white-mug-rotate-it-place-it-on-the-table-and-hook-it-onto-the-smooth-dark-gray-rack/step_024/vv/l29h03.csv', 'hf://datasets/kv-compression/lingbot-va-attn@a51f9363a05b5e3223dd4fe8500f2488bb35e0c4/artifacts/attention/va-vv-attention-c10-s24/grab-the-medium-sized-white-mug-rotate-it-place-it-on-the-table-and-hook-it-onto-the-smooth-dark-gray-rack/step_024/vv/l29h04.csv', 'hf://datasets/kv-compression/lingbot-va-attn@a51f9363a05b5e3223dd4fe8500f2488bb35e0c4/artifacts/attention/va-vv-attention-c10-s24/grab-the-medium-sized-white-mug-rotate-it-place-it-on-the-table-and-hook-it-onto-the-smooth-dark-gray-rack/step_024/vv/l29h05.csv', 'hf://datasets/kv-compression/lingbot-va-attn@a51f9363a05b5e3223dd4fe8500f2488bb35e0c4/artifacts/attention/va-vv-attention-c10-s24/grab-the-medium-sized-white-mug-rotate-it-place-it-on-the-table-and-hook-it-onto-the-smooth-dark-gray-rack/step_024/vv/l29h06.csv', 'hf://datasets/kv-compression/lingbot-va-attn@a51f9363a05b5e3223dd4fe8500f2488bb35e0c4/artifacts/attention/va-vv-attention-c10-s24/grab-the-medium-sized-white-mug-rotate-it-place-it-on-the-table-and-hook-it-onto-the-smooth-dark-gray-rack/step_024/vv/l29h07.csv', 'hf://datasets/kv-compression/lingbot-va-attn@a51f9363a05b5e3223dd4fe8500f2488bb35e0c4/artifacts/attention/va-vv-attention-c10-s24/grab-the-medium-sized-white-mug-rotate-it-place-it-on-the-table-and-hook-it-onto-the-smooth-dark-gray-rack/step_024/vv/l29h08.csv', 'hf://datasets/kv-compression/lingbot-va-attn@a51f9363a05b5e3223dd4fe8500f2488bb35e0c4/artifacts/attention/va-vv-attention-c10-s24/grab-the-medium-sized-white-mug-rotate-it-place-it-on-the-table-and-hook-it-onto-the-smooth-dark-gray-rack/step_024/vv/l29h09.csv', 'hf://datasets/kv-compression/lingbot-va-attn@a51f9363a05b5e3223dd4fe8500f2488bb35e0c4/artifacts/attention/va-vv-attention-c10-s24/grab-the-medium-sized-white-mug-rotate-it-place-it-on-the-table-and-hook-it-onto-the-smooth-dark-gray-rack/step_024/vv/l29h10.csv', 'hf://datasets/kv-compression/lingbot-va-attn@a51f9363a05b5e3223dd4fe8500f2488bb35e0c4/artifacts/attention/va-vv-attention-c10-s24/grab-the-medium-sized-white-mug-rotate-it-place-it-on-the-table-and-hook-it-onto-the-smooth-dark-gray-rack/step_024/vv/l29h11.csv', 'hf://datasets/kv-compression/lingbot-va-attn@a51f9363a05b5e3223dd4fe8500f2488bb35e0c4/artifacts/attention/va-vv-attention-c10-s24/grab-the-medium-sized-white-mug-rotate-it-place-it-on-the-table-and-hook-it-onto-the-smooth-dark-gray-rack/step_024/vv/l29h12.csv', 'hf://datasets/kv-compression/lingbot-va-attn@a51f9363a05b5e3223dd4fe8500f2488bb35e0c4/artifacts/attention/va-vv-attention-c10-s24/grab-the-medium-sized-white-mug-rotate-it-place-it-on-the-table-and-hook-it-onto-the-smooth-dark-gray-rack/step_024/vv/l29h13.csv', 'hf://datasets/kv-compression/lingbot-va-attn@a51f9363a05b5e3223dd4fe8500f2488bb35e0c4/artifacts/attention/va-vv-attention-c10-s24/grab-the-medium-sized-white-mug-rotate-it-place-it-on-the-table-and-hook-it-onto-the-smooth-dark-gray-rack/step_024/vv/l29h14.csv', 'hf://datasets/kv-compression/lingbot-va-attn@a51f9363a05b5e3223dd4fe8500f2488bb35e0c4/artifacts/attention/va-vv-attention-c10-s24/grab-the-medium-sized-white-mug-rotate-it-place-it-on-the-table-and-hook-it-onto-the-smooth-dark-gray-rack/step_024/vv/l29h15.csv', 'hf://datasets/kv-compression/lingbot-va-attn@a51f9363a05b5e3223dd4fe8500f2488bb35e0c4/artifacts/attention/va-vv-attention-c10-s24/grab-the-medium-sized-white-mug-rotate-it-place-it-on-the-table-and-hook-it-onto-the-smooth-dark-gray-rack/step_024/vv/l29h16.csv', 'hf://datasets/kv-compression/lingbot-va-attn@a51f9363a05b5e3223dd4fe8500f2488bb35e0c4/artifacts/attention/va-vv-attention-c10-s24/grab-the-medium-sized-white-mug-rotate-it-place-it-on-the-table-and-hook-it-onto-the-smooth-dark-gray-rack/step_024/vv/l29h17.csv', 'hf://datasets/kv-compression/lingbot-va-attn@a51f9363a05b5e3223dd4fe8500f2488bb35e0c4/artifacts/attention/va-vv-attention-c10-s24/grab-the-medium-sized-white-mug-rotate-it-place-it-on-the-table-and-hook-it-onto-the-smooth-dark-gray-rack/step_024/vv/l29h18.csv', 'hf://datasets/kv-compression/lingbot-va-attn@a51f9363a05b5e3223dd4fe8500f2488bb35e0c4/artifacts/attention/va-vv-attention-c10-s24/grab-the-medium-sized-white-mug-rotate-it-place-it-on-the-table-and-hook-it-onto-the-smooth-dark-gray-rack/step_024/vv/l29h19.csv', 'hf://datasets/kv-compression/lingbot-va-attn@a51f9363a05b5e3223dd4fe8500f2488bb35e0c4/artifacts/attention/va-vv-attention-c10-s24/grab-the-medium-sized-white-mug-rotate-it-place-it-on-the-table-and-hook-it-onto-the-smooth-dark-gray-rack/step_024/vv/l29h20.csv', 'hf://datasets/kv-compression/lingbot-va-attn@a51f9363a05b5e3223dd4fe8500f2488bb35e0c4/artifacts/attention/va-vv-attention-c10-s24/grab-the-medium-sized-white-mug-rotate-it-place-it-on-the-table-and-hook-it-onto-the-smooth-dark-gray-rack/step_024/vv/l29h21.csv', 'hf://datasets/kv-compression/lingbot-va-attn@a51f9363a05b5e3223dd4fe8500f2488bb35e0c4/artifacts/attention/va-vv-attention-c10-s24/grab-the-medium-sized-white-mug-rotate-it-place-it-on-the-table-and-hook-it-onto-the-smooth-dark-gray-rack/step_024/vv/l29h22.csv', 'hf://datasets/kv-compression/lingbot-va-attn@a51f9363a05b5e3223dd4fe8500f2488bb35e0c4/artifacts/attention/va-vv-attention-c10-s24/grab-the-medium-sized-white-mug-rotate-it-place-it-on-the-table-and-hook-it-onto-the-smooth-dark-gray-rack/step_024/vv/l29h23.csv', 'hf://datasets/kv-compression/lingbot-va-attn@a51f9363a05b5e3223dd4fe8500f2488bb35e0c4/derived/density/top-p-0.9/trajectory-6steps/step_000.csv', 'hf://datasets/kv-compression/lingbot-va-attn@a51f9363a05b5e3223dd4fe8500f2488bb35e0c4/derived/density/top-p-0.9/trajectory-6steps/step_004.csv', 'hf://datasets/kv-compression/lingbot-va-attn@a51f9363a05b5e3223dd4fe8500f2488bb35e0c4/derived/density/top-p-0.9/trajectory-6steps/step_009.csv', 'hf://datasets/kv-compression/lingbot-va-attn@a51f9363a05b5e3223dd4fe8500f2488bb35e0c4/derived/density/top-p-0.9/trajectory-6steps/step_014.csv', 'hf://datasets/kv-compression/lingbot-va-attn@a51f9363a05b5e3223dd4fe8500f2488bb35e0c4/derived/density/top-p-0.9/trajectory-6steps/step_019.csv', 'hf://datasets/kv-compression/lingbot-va-attn@a51f9363a05b5e3223dd4fe8500f2488bb35e0c4/derived/density/top-p-0.9/trajectory-6steps/step_024.csv', 'hf://datasets/kv-compression/lingbot-va-attn@a51f9363a05b5e3223dd4fe8500f2488bb35e0c4/metadata/experiments.csv']

Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1848, in _prepare_split_single
                  writer.write_table(table)
                  ~~~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 765, in write_table
                  self._write_table(pa_table, writer_batch_size=writer_batch_size)
                  ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 773, in _write_table
                  pa_table = table_cast(pa_table, self._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
              0.00426948676: double
              0.00441499567: double
              0.00402253773: double
              0.00619925791: double
              0.00530038215: double
              0.00379803916: double
              0.00463973777: double
              0.00315956469: double
              0.00457050186: double
              0.00494950032: double
              0.00413152901: double
              0.00422735699: double
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              0.0049180598: double
              0.00433367724: double
              0.00372165628: double
              0.00351778511: double
              0.00257870439: double
              0.00397544168: double
              0.00493197655: double
              0.00344126276: double
              0.00371673144: double
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              0.00448663207: double
              0.00369546795: double
              0.00469905278: double
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              0.0037248868: double
              0.00459609646: double
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              0.00455467822: double
              0.00436470425: double
              0.00415172055: double
              0.00494068721: double
              0.00443126587: double
              0.0046739052: double
              0.00415783795: double
              0.00422599539: double
              0.00597921759: double
              0.00641177455: double
              0.00413765758: double
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              0.00409919769: double
              0.00401787972: double
              0.0038661221: double
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              0.00514732767: double
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              0.00451361388: double
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              0.0024806
              ...
              : double
              Unnamed: 2337: double
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              Unnamed: 2388: double
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              Unnamed: 2396: double
              Unnamed: 2397: double
              Unnamed: 2398: double
              Unnamed: 2399: double
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 303518
              to
              {'Unnamed: 0': Value('float64'), 'Unnamed: 1': Value('float64'), 'Unnamed: 2': Value('float64'), 'Unnamed: 3': Value('float64'), 'Unnamed: 4': Value('float64'), 'Unnamed: 5': Value('float64'), 'Unnamed: 6': Value('float64'), 'Unnamed: 7': Value('float64'), 'Unnamed: 8': Value('float64'), 'Unnamed: 9': Value('float64'), 'Unnamed: 10': Value('float64'), 'Unnamed: 11': Value('float64'), 'Unnamed: 12': Value('float64'), 'Unnamed: 13': Value('float64'), 'Unnamed: 14': Value('float64'), 'Unnamed: 15': Value('float64'), 'Unnamed: 16': Value('float64'), 'Unnamed: 17': Value('float64'), 'Unnamed: 18': Value('float64'), 'Unnamed: 19': Value('float64'), 'Unnamed: 20': Value('float64'), 'Unnamed: 21': Value('float64'), 'Unnamed: 22': Value('float64'), 'Unnamed: 23': Value('float64'), 'Unnamed: 24': Value('float64'), 'Unnamed: 25': Value('float64'), 'Unnamed: 26': Value('float64'), 'Unnamed: 27': Value('float64'), 'Unnamed: 28': Value('float64'), 'Unnamed: 29': Value('float64'), 'Unnamed: 30': Value('float64'), 'Unnamed: 31': Value('float64'), 'Unnamed: 32': Value('float64'), 'Unnamed: 33': Value('float64'), 'Unnamed: 34': Value('float64'), 'Unnamed: 35': Value('float64'), 'Unnamed: 36': Value('float64'), 'Unnamed: 37': Value('float64'), 'Unnamed: 38': Value('float64'), 'Unnamed: 39': Value('float64'), 'Unnamed: 40': Value('float64'), 'Unnamed: 41': Value('float64'), 'Unnamed: 42': Value('float64'), 'Unnamed: 43': Value('float64'), 'Unnamed: 44': Value('float64'), 'Unnamed: 45': Value('fl
              ...
              4'), 'Unnamed: 244': Value('float64'), 'Unnamed: 245': Value('float64'), 'Unnamed: 246': Value('float64'), 'Unnamed: 247': Value('float64'), 'Unnamed: 248': Value('float64'), 'Unnamed: 249': Value('float64'), 'Unnamed: 250': Value('float64'), 'Unnamed: 251': Value('float64'), 'Unnamed: 252': Value('float64'), 'Unnamed: 253': Value('float64'), 'Unnamed: 254': Value('float64'), 'Unnamed: 255': Value('float64'), 'Unnamed: 256': Value('float64'), 'Unnamed: 257': Value('float64'), 'Unnamed: 258': Value('float64'), 'Unnamed: 259': Value('float64'), 'Unnamed: 260': Value('float64'), 'Unnamed: 261': Value('float64'), 'Unnamed: 262': Value('float64'), 'Unnamed: 263': Value('float64'), 'Unnamed: 264': Value('float64'), 'Unnamed: 265': Value('float64'), 'Unnamed: 266': Value('float64'), 'Unnamed: 267': Value('float64'), 'Unnamed: 268': Value('float64'), 'Unnamed: 269': Value('float64'), 'Unnamed: 270': Value('float64'), 'Unnamed: 271': Value('float64'), 'Unnamed: 272': Value('float64'), 'Unnamed: 273': Value('float64'), 'Unnamed: 274': Value('float64'), 'Unnamed: 275': Value('float64'), 'Unnamed: 276': Value('float64'), 'Unnamed: 277': Value('float64'), 'Unnamed: 278': Value('float64'), 'Unnamed: 279': Value('float64'), 'Unnamed: 280': Value('float64'), 'Unnamed: 281': Value('float64'), 'Unnamed: 282': Value('float64'), 'Unnamed: 283': Value('float64'), 'Unnamed: 284': Value('float64'), 'Unnamed: 285': Value('float64'), 'Unnamed: 286': Value('float64'), 'Unnamed: 287': Value('float64')}
              because column names don't match
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      builder, max_dataset_size_bytes=max_dataset_size_bytes
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1694, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ~~~~~~~~~~~~~~~~~~~~~~~~~~^
                      gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  ):
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1850, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
                  ...<4 lines>...
                  )
              datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
              
              All the data files must have the same columns, but at some point there are 2352 new columns ({'Unnamed: 1832', 'Unnamed: 1611', 'Unnamed: 1188', 'Unnamed: 632', 'Unnamed: 1565', 'Unnamed: 766', 'Unnamed: 2152', '0.00203101127', '0.00366296363', 'Unnamed: 653', 'Unnamed: 1861', '0.00415172055', 'Unnamed: 987', '0.00430731801', 'Unnamed: 686', 'Unnamed: 1214', 'Unnamed: 320', 'Unnamed: 669', 'Unnamed: 337', '0.00257870439', 'Unnamed: 1870', 'Unnamed: 939', 'Unnamed: 1452', 'Unnamed: 944', 'Unnamed: 787', 'Unnamed: 1434', 'Unnamed: 429', 'Unnamed: 1586', 'Unnamed: 1228', 'Unnamed: 1238', 'Unnamed: 779', 'Unnamed: 514', 'Unnamed: 352', 'Unnamed: 741', 'Unnamed: 1142', 'Unnamed: 1012', 'Unnamed: 2101', 'Unnamed: 648', 'Unnamed: 577', 'Unnamed: 892', 'Unnamed: 1010', 'Unnamed: 1560', 'Unnamed: 1158', 'Unnamed: 2093', 'Unnamed: 1194', 'Unnamed: 737', 'Unnamed: 914', 'Unnamed: 1978', 'Unnamed: 434', 'Unnamed: 1529', 'Unnamed: 701', 'Unnamed: 293', 'Unnamed: 745', 'Unnamed: 1276', 'Unnamed: 1006', '0.00469905278', 'Unnamed: 1631', 'Unnamed: 802', 'Unnamed: 959', 'Unnamed: 574', 'Unnamed: 419', 'Unnamed: 618', 'Unnamed: 1848', 'Unnamed: 1125', 'Unnamed: 687', 'Unnamed: 1077', 'Unnamed: 1855', 'Unnamed: 1169', 'Unnamed: 401', 'Unnamed: 1099', 'Unnamed: 2230', 'Unnamed: 1458', 'Unnamed: 448', 'Unnamed: 938', 'Unnamed: 1575', 'Unnamed: 1939', 'Unnamed: 2037', 'Unnamed: 479', 'Unnamed: 295', 'Unnamed: 946', 'Unnamed: 2185', 'Unnamed: 1375', 'Unnamed: 1601', '0.00289596827', 'Unnamed: 567', 'Unnamed: 1703', 'Unnamed: 2292', '0.0089340331', 'Unnamed: 2081', '0.00276727951', '0.00473
              ...
              ', 'Unnamed: 1598', 'Unnamed: 810', 'Unnamed: 372', 'Unnamed: 883', 'Unnamed: 2022', 'Unnamed: 1881', 'Unnamed: 1048', 'Unnamed: 319', 'Unnamed: 1865', 'Unnamed: 1932', '0.00548504991', 'Unnamed: 769', 'Unnamed: 672', 'Unnamed: 441', 'Unnamed: 2268', '0.00454560667', 'Unnamed: 1306', 'Unnamed: 301', '0.00411575008', 'Unnamed: 508', 'Unnamed: 2144', '0.00226795184', 'Unnamed: 2137', 'Unnamed: 1098', 'Unnamed: 383', 'Unnamed: 926', 'Unnamed: 1205', 'Unnamed: 1935', 'Unnamed: 1148', 'Unnamed: 1420', 'Unnamed: 2140', 'Unnamed: 595', 'Unnamed: 408', 'Unnamed: 804', 'Unnamed: 2343', '0.00561258895', 'Unnamed: 554', 'Unnamed: 949', 'Unnamed: 1234', 'Unnamed: 2225', 'Unnamed: 418', 'Unnamed: 1730', 'Unnamed: 377', 'Unnamed: 800', 'Unnamed: 1510', 'Unnamed: 975', '0.00427694712', 'Unnamed: 500', 'Unnamed: 2290', 'Unnamed: 905', 'Unnamed: 1103', 'Unnamed: 525', 'Unnamed: 523', 'Unnamed: 1156', 'Unnamed: 1328', 'Unnamed: 1511', 'Unnamed: 2103', 'Unnamed: 2328', 'Unnamed: 2127', 'Unnamed: 2242', 'Unnamed: 930', 'Unnamed: 1463', '0.00584507314', 'Unnamed: 933', 'Unnamed: 1181', 'Unnamed: 1718', 'Unnamed: 2040', 'Unnamed: 795', 'Unnamed: 623', 'Unnamed: 1346', 'Unnamed: 1919', 'Unnamed: 2009', 'Unnamed: 1026', 'Unnamed: 2307', 'Unnamed: 1991', 'Unnamed: 1264', '0.00311369426', '0.00249186996', 'Unnamed: 1243', 'Unnamed: 1745', 'Unnamed: 1549', 'Unnamed: 518', 'Unnamed: 1144', 'Unnamed: 409', 'Unnamed: 1755', 'Unnamed: 350', 'Unnamed: 2172', 'Unnamed: 499', 'Unnamed: 1482', 'Unnamed: 1670'}) and 240 missing columns ({'Unnamed: 142', 'Unnamed: 155', 'Unnamed: 32', 'Unnamed: 172', 'Unnamed: 133', 'Unnamed: 83', 'Unnamed: 115', 'Unnamed: 11', 'Unnamed: 62', 'Unnamed: 90', 'Unnamed: 203', 'Unnamed: 237', 'Unnamed: 35', 'Unnamed: 38', 'Unnamed: 19', 'Unnamed: 169', 'Unnamed: 97', 'Unnamed: 37', 'Unnamed: 221', 'Unnamed: 181', 'Unnamed: 158', 'Unnamed: 234', 'Unnamed: 3', 'Unnamed: 168', 'Unnamed: 79', 'Unnamed: 16', 'Unnamed: 43', 'Unnamed: 66', 'Unnamed: 231', 'Unnamed: 101', 'Unnamed: 121', 'Unnamed: 51', 'Unnamed: 217', 'Unnamed: 209', 'Unnamed: 31', 'Unnamed: 41', 'Unnamed: 157', 'Unnamed: 152', 'Unnamed: 9', 'Unnamed: 206', 'Unnamed: 25', 'Unnamed: 187', 'Unnamed: 72', 'Unnamed: 111', 'Unnamed: 226', 'Unnamed: 20', 'Unnamed: 225', 'Unnamed: 52', 'Unnamed: 194', 'Unnamed: 150', 'Unnamed: 110', 'Unnamed: 198', 'Unnamed: 233', 'Unnamed: 196', 'Unnamed: 137', 'Unnamed: 179', 'Unnamed: 40', 'Unnamed: 130', 'Unnamed: 39', 'Unnamed: 23', 'Unnamed: 36', 'Unnamed: 173', 'Unnamed: 108', 'Unnamed: 162', 'Unnamed: 54', 'Unnamed: 136', 'Unnamed: 197', 'Unnamed: 114', 'Unnamed: 193', 'Unnamed: 67', 'Unnamed: 127', 'Unnamed: 171', 'Unnamed: 8', 'Unnamed: 109', 'Unnamed: 165', 'Unnamed: 61', 'Unnamed: 148', 'Unnamed: 201', 'Unnamed: 0', 'Unnamed: 87', 'Unnamed: 2', 'Unnamed: 200', 'Unnamed: 48', 'Unnamed: 74', 'Unnamed: 45', 'Unnamed: 57', 'Unnamed: 102', 'Unnamed: 156', 'Unnamed: 204', 'Unnamed: 208', 'Unnamed: 163', 'Unnamed: 227', 'Unnamed: 7', 'Unnamed: 161', 'Unnamed: 128', 'Unnamed: 50', 'Unnamed:
              ...
              med: 29', 'Unnamed: 188', 'Unnamed: 1', 'Unnamed: 224', 'Unnamed: 30', 'Unnamed: 86', 'Unnamed: 69', 'Unnamed: 118', 'Unnamed: 60', 'Unnamed: 44', 'Unnamed: 214', 'Unnamed: 199', 'Unnamed: 236', 'Unnamed: 17', 'Unnamed: 154', 'Unnamed: 190', 'Unnamed: 103', 'Unnamed: 229', 'Unnamed: 106', 'Unnamed: 68', 'Unnamed: 186', 'Unnamed: 56', 'Unnamed: 145', 'Unnamed: 14', 'Unnamed: 75', 'Unnamed: 85', 'Unnamed: 207', 'Unnamed: 80', 'Unnamed: 191', 'Unnamed: 230', 'Unnamed: 232', 'Unnamed: 21', 'Unnamed: 182', 'Unnamed: 91', 'Unnamed: 210', 'Unnamed: 5', 'Unnamed: 119', 'Unnamed: 129', 'Unnamed: 27', 'Unnamed: 65', 'Unnamed: 144', 'Unnamed: 185', 'Unnamed: 131', 'Unnamed: 184', 'Unnamed: 147', 'Unnamed: 223', 'Unnamed: 100', 'Unnamed: 138', 'Unnamed: 77', 'Unnamed: 99', 'Unnamed: 46', 'Unnamed: 239', 'Unnamed: 98', 'Unnamed: 4', 'Unnamed: 28', 'Unnamed: 176', 'Unnamed: 47', 'Unnamed: 192', 'Unnamed: 153', 'Unnamed: 219', 'Unnamed: 164', 'Unnamed: 55', 'Unnamed: 220', 'Unnamed: 53', 'Unnamed: 96', 'Unnamed: 151', 'Unnamed: 113', 'Unnamed: 141', 'Unnamed: 195', 'Unnamed: 95', 'Unnamed: 105', 'Unnamed: 58', 'Unnamed: 26', 'Unnamed: 134', 'Unnamed: 117', 'Unnamed: 70', 'Unnamed: 135', 'Unnamed: 159', 'Unnamed: 64', 'Unnamed: 183', 'Unnamed: 140', 'Unnamed: 63', 'Unnamed: 12', 'Unnamed: 73', 'Unnamed: 139', 'Unnamed: 81', 'Unnamed: 49', 'Unnamed: 10', 'Unnamed: 212', 'Unnamed: 18', 'Unnamed: 238', 'Unnamed: 124', 'Unnamed: 76', 'Unnamed: 149', 'Unnamed: 222', 'Unnamed: 125', 'Unnamed: 71'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/kv-compression/lingbot-va-attn/artifacts/attention/va-vv-attention-c10-s24-benchmark3/pick-up-the-hammer-and-strike-the-block/step_024/vv/l00h00.csv (at revision a51f9363a05b5e3223dd4fe8500f2488bb35e0c4), ['hf://datasets/kv-compression/lingbot-va-attn@a51f9363a05b5e3223dd4fe8500f2488bb35e0c4/artifacts/attention/va-vv-attention-c10-s24-benchmark3/pick-up-the-hammer-and-strike-the-block/step_024/va/l00h00.csv', 'hf://datasets/kv-compression/lingbot-va-attn@a51f9363a05b5e3223dd4fe8500f2488bb35e0c4/artifacts/attention/va-vv-attention-c10-s24-benchmark3/pick-up-the-hammer-and-strike-the-block/step_024/va/l00h01.csv', 'hf://datasets/kv-compression/lingbot-va-attn@a51f9363a05b5e3223dd4fe8500f2488bb35e0c4/artifacts/attention/va-vv-attention-c10-s24-benchmark3/pick-up-the-hammer-and-strike-the-block/step_024/va/l00h02.csv', 'hf://datasets/kv-compression/lingbot-va-attn@a51f9363a05b5e3223dd4fe8500f2488bb35e0c4/artifacts/attention/va-vv-attention-c10-s24-benchmark3/pick-up-the-hammer-and-strike-the-block/step_024/va/l00h03.csv', 'hf://datasets/kv-compression/lingbot-va-attn@a51f9363a05b5e3223dd4fe8500f2488bb35e0c4/artifacts/attention/va-vv-attention-c10-s24-benchmark3/pick-up-the-hammer-and-strike-the-block/step_024/va/l00h04.csv', 'hf://datasets/kv-compression/lingbot-va-attn@a51f9363a05b5e3223dd4fe8500f2488bb35e0c4/artifacts/attention/va-vv-attention-c10-s24-benchmark3/pick-up-the-hammer-and-strike-the-block/step_024/va/l00h05.csv', 'hf://datasets/kv-compression/lingbot-va-attn@a51f9363a05b5e3223dd4fe8500f2488bb35e0c4/artifacts/attention/va-vv-attention-c10-s24-benchmark3/pick-up-the-hammer-and-strike-the-block/step_024/va/l00h06.csv', 'hf://datasets/kv-compression/lingbot-va-attn@a51f9363a05b5e3223dd4fe8500f2488bb35e0c4/artifacts/attention/va-vv-attention-c10-s24-benchmark3/pick-up-the-hammer-and-strike-the-block/step_024/va/l00h07.csv', 'hf://datasets/kv-compression/lingbot-va-attn@a51f9363a05b5e3223dd4fe8500f2488bb35e0c4/artifacts/attention/va-vv-attention-c10-s24-benchmark3/pick-up-the-hammer-and-strike-the-block/step_024/va/l00h08.csv', 'hf://datasets/kv-compression/lingbot-va-attn@a51f9363a05b5e3223dd4fe8500f2488bb35e0c4/artifacts/attention/va-vv-attention-c10-s24-benchmark3/pick-up-the-hammer-and-strike-the-block/step_024/va/l00h09.csv', 'hf://datasets/kv-compression/lingbot-va-attn@a51f9363a05b5e3223dd4fe8500f2488bb35e0c4/artifacts/attention/va-vv-attention-c10-s24-benchmark3/pick-up-the-hammer-and-strike-the-block/step_024/va/l00h10.csv', 'hf://datasets/kv-compression/lingbot-va-attn@a51f9363a05b5e3223dd4fe8500f2488bb35e0c4/artifacts/attention/va-vv-attention-c10-s24-benchmark3/pick-up-the-hammer-and-strike-the-block/step_024/va/l00h11.csv', 'hf://datasets/kv-compression/lingbot-va-attn@a51f9363a05b5e3223dd4fe8500f2488bb35e0c4/artifacts/attention/va-vv-attention-c10-s24-benchmark3/pick-up-the-hammer-and-strike-the-block/step_024/va/l00h12.csv', 'hf://datasets/kv-compression/lingbot-va-attn@a51f9363a05b5e3223dd4fe8500f2488bb35e0c4/artifacts/attention/va-vv-attention-c10-s24-benchmark3/pick-up-the-hammer-and-strike-the-block/step_024/va/l00h13.csv', 'hf://datasets/kv-compression/lingbot-va-attn@a51f9363a05b5e3223dd4fe8500f2488bb35e0c4/artifacts/attention/va-vv-attention-c10-s24-benchmark3/pick-up-the-hammer-and-strike-the-block/step_024/va/l00h14.csv', 'hf://datasets/kv-compression/lingbot-va-attn@a51f9363a05b5e3223dd4fe8500f2488bb35e0c4/artifacts/attention/va-vv-attention-c10-s24-benchmark3/pick-up-the-hammer-and-strike-the-block/step_024/va/l00h15.csv', 'hf://datasets/kv-compression/lingbot-va-attn@a51f9363a05b5e3223dd4fe8500f2488bb35e0c4/artifacts/attention/va-vv-attention-c10-s24-benchmark3/pick-up-the-hammer-and-strike-the-block/step_024/va/l00h16.csv', 'hf://datasets/kv-compression/lingbot-va-attn@a51f9363a05b5e3223dd4fe8500f2488bb35e0c4/artifacts/attention/va-vv-attention-c10-s24-benchmark3/pick-up-the-hammer-and-strike-the-block/step_024/va/l00h17.csv', 'hf://datasets/kv-compression/lingbot-va-attn@a51f9363a05b5e3223dd4fe8500f2488bb35e0c4/artifacts/attention/va-vv-attention-c10-s24-benchmark3/pick-up-the-hammer-and-strike-the-block/step_024/va/l00h18.csv', 'hf://datasets/kv-compression/lingbot-va-attn@a51f9363a05b5e3223dd4fe8500f2488bb35e0c4/artifacts/attention/va-vv-attention-c10-s24-benchmark3/pick-up-the-hammer-and-strike-the-block/step_024/va/l00h19.csv', 'hf://datasets/kv-compression/lingbot-va-attn@a51f9363a05b5e3223dd4fe8500f2488bb35e0c4/artifacts/attention/va-vv-attention-c10-s24-benchmark3/pick-up-the-hammer-and-strike-the-block/step_024/va/l00h20.csv', 'hf://datasets/kv-compression/lingbot-va-attn@a51f9363a05b5e3223dd4fe8500f2488bb35e0c4/artifacts/attention/va-vv-attention-c10-s24-benchmark3/pick-up-the-hammer-and-strike-the-block/step_024/va/l00h21.csv', 'hf://datasets/kv-compression/lingbot-va-attn@a51f9363a05b5e3223dd4fe8500f2488bb35e0c4/artifacts/attention/va-vv-attention-c10-s24-benchmark3/pick-up-the-hammer-and-strike-the-block/step_024/va/l00h22.csv', 'hf://datasets/kv-compression/lingbot-va-attn@a51f9363a05b5e3223dd4fe8500f2488bb35e0c4/artifacts/attention/va-vv-attention-c10-s24-benchmark3/pick-up-the-hammer-and-strike-the-block/step_024/va/l00h23.csv', 'hf://datasets/kv-compression/lingbot-va-attn@a51f9363a05b5e3223dd4fe8500f2488bb35e0c4/artifacts/attention/va-vv-attention-c10-s24-benchmark3/pick-up-the-hammer-and-strike-the-block/step_024/va/l01h00.csv', 'hf://datasets/kv-compression/lingbot-va-attn@a51f9363a05b5e3223dd4fe8500f2488bb35e0c4/artifacts/attention/va-vv-attention-c10-s24-benchmark3/pick-up-the-hammer-and-strike-the-block/step_024/va/l01h01.csv', 'hf://datasets/kv-compression/lingbot-va-attn@a51f9363a05b5e3223dd4fe8500f2488bb35e0c4/artifacts/attention/va-vv-attention-c10-s24-benchmark3/pick-up-the-hammer-and-strike-the-block/step_024/va/l01h02.csv', 'hf://datasets/kv-compression/lingbot-va-attn@a51f9363a05b5e3223dd4fe8500f2488bb35e0c4/artifacts/attention/va-vv-attention-c10-s24-benchmark3/pick-up-the-hammer-and-strike-the-block/step_024/va/l01h03.csv', 'hf://datasets/kv-compression/lingbot-va-attn@a51f9363a05b5e3223dd4fe8500f2488bb35e0c4/artifacts/attention/va-vv-attention-c10-s24-benchmark3/pick-up-the-hammer-and-strike-the-block/step_024/va/l01h04.csv', 'hf://datasets/kv-compression/lingbot-va-attn@a51f9363a05b5e3223dd4fe8500f2488bb35e0c4/artifacts/attention/va-vv-attention-c10-s24-benchmark3/pick-up-the-hammer-and-strike-the-block/step_024/va/l01h05.csv', 'hf://datasets/kv-compression/lingbot-va-attn@a51f9363a05b5e3223dd4fe8500f2488bb35e0c4/artifacts/attention/va-vv-attention-c10-s24-benchmark3/pick-up-the-hammer-and-strike-the-block/step_024/va/l01h06.csv', 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              Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)

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End of preview.

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

LingBot-VA Attention Analysis Dataset

Attention-analysis dataset generated on h100-server for the RoboTwin task grab-the-medium-sized-white-mug-rotate-it-place-it-on-the-table-and-hook-it-onto-the-smooth-dark-gray-rack, using the LingBot-VA 1.0-style velocity FlowMatch inference path (checkpoint lingbot-va-posttrain-robotwin).

Content

  • artifacts/archives/lingbot-va-attn-trajectory-6steps.tar.gz.part00part09 — the full six-step raw attention capture: 4,320 dense CSV matrices (6 steps 0,4,9,14,19,24 × 30 layers × 24 heads) plus summary.json, packed as a single gzip tarball (~77.7 GB) and split into ~8 GiB parts to stay within the Hub per-file limit. Archive sha256: 672366873edd322eed50879226487042f38cbd3afbe7186ba584ef565b806c39.
  • derived/density/top-p-0.9/trajectory-6steps/ — per-layer top-p=0.9 attention-density CSVs, one per step, produced by script/compute_attention_density.py.
  • visualizations/attention-heatmaps/trajectory-6steps/ — representative layer/head attention heatmaps and trajectory contact sheets.
  • visualizations/density/trajectory-6steps/ — layer × step density heatmaps and layer-profile plots.
  • artifacts/videos/ — the demo.mp4 output corresponding to the six-step run.
  • metadata/ — experiment table (experiments.csv), source checkout commit (source-commit.txt), complete file manifest with per-part checksums (manifest.json), and the raw archive checksum (artifacts/archives/*.sha256).

Reconstructing the archive

cat lingbot-va-attn-trajectory-6steps.tar.gz.part* > lingbot-va-attn-trajectory-6steps.tar.gz
sha256sum lingbot-va-attn-trajectory-6steps.tar.gz
# expect: 672366873edd322eed50879226487042f38cbd3afbe7186ba584ef565b806c39
tar -tzf lingbot-va-attn-trajectory-6steps.tar.gz | wc -l
# expect: 4329 (4,320 CSV + 1 summary.json + 8 directory entries)

Matrix semantics

Each raw lXXhYY.csv is a dense attention matrix; empty CSV cells encode masked/future columns and are preserved. The original summary.json inside the archive is authoritative for token/chunk boundaries and shifted scheduler timesteps (use summary.json, not the step index, for actual timesteps). The six-step trajectory comes from the attn-exp-vv-10-6 run (robotwin_i2av, 10 chunks, 25 denoising updates) and is the LingBot-VA 1.0-style velocity FlowMatch execution path currently wired in this repository, not the VA2 distilled student.

This trimmed release intentionally omits the earlier full-history, smoke, and three-step captures and the duplicate attn-exp.zip archive.

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