glm5-layerwise-reap-observations

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The dataset generation failed
Error code:   DatasetGenerationError
Exception:    CastError
Message:      Couldn't cast
created_at: timestamp[s]
workflow: string
model_path: string
input_pt: string
target_length: int64
pack_samples: bool
total_bundle_batches: int64
selected_batches: int64
selected_samples: int64
selected_logical_ids: list<item: string>
  child 0, item: string
max_group_batches: int64
shard_index: null
num_shards: null
to
{'workflow': Value('string'), 'model_path': Value('string'), 'input_pt': Value('string'), 'processedSamples': Value('int64'), 'processedTokens': Value('int64'), 'categories': {'layers': {'10': {'ean_mean': List(Value('float64')), 'ean_sum': List(Value('float64')), 'expert_frequency': List(Value('int64')), 'max_activations': List(Value('float64')), 'reap': List(Value('float64')), 'total_tokens': Value('int64'), 'weighted_ean_sum': List(Value('float64')), 'weighted_expert_frequency_sum': List(Value('float64'))}, '11': {'ean_mean': List(Value('float64')), 'ean_sum': List(Value('float64')), 'expert_frequency': List(Value('int64')), 'max_activations': List(Value('float64')), 'reap': List(Value('float64')), 'total_tokens': Value('int64'), 'weighted_ean_sum': List(Value('float64')), 'weighted_expert_frequency_sum': List(Value('float64'))}, '12': {'ean_mean': List(Value('float64')), 'ean_sum': List(Value('float64')), 'expert_frequency': List(Value('int64')), 'max_activations': List(Value('float64')), 'reap': List(Value('float64')), 'total_tokens': Value('int64'), 'weighted_ean_sum': List(Value('float64')), 'weighted_expert_frequency_sum': List(Value('float64'))}, '13': {'ean_mean': List(Value('float64')), 'ean_sum': List(Value('float64')), 'expert_frequency': List(Value('int64')), 'max_activations': List(Value('float64')), 'reap': List(Value('float64')), 'total_tokens': Value('int64'), 'weighted_ean_sum': List(Value('float64')), 'weighted_expert_frequency_sum': List(Value('float64'))
...
Value('float64')), 'reap': List(Value('float64')), 'total_tokens': Value('int64'), 'weighted_ean_sum': List(Value('float64')), 'weighted_expert_frequency_sum': List(Value('float64'))}, '76': {'ean_mean': List(Value('float64')), 'ean_sum': List(Value('float64')), 'expert_frequency': List(Value('int64')), 'max_activations': List(Value('float64')), 'reap': List(Value('float64')), 'total_tokens': Value('int64'), 'weighted_ean_sum': List(Value('float64')), 'weighted_expert_frequency_sum': List(Value('float64'))}, '77': {'ean_mean': List(Value('float64')), 'ean_sum': List(Value('float64')), 'expert_frequency': List(Value('int64')), 'max_activations': List(Value('float64')), 'reap': List(Value('float64')), 'total_tokens': Value('int64'), 'weighted_ean_sum': List(Value('float64')), 'weighted_expert_frequency_sum': List(Value('float64'))}, '8': {'ean_mean': List(Value('float64')), 'ean_sum': List(Value('float64')), 'expert_frequency': List(Value('int64')), 'max_activations': List(Value('float64')), 'reap': List(Value('float64')), 'total_tokens': Value('int64'), 'weighted_ean_sum': List(Value('float64')), 'weighted_expert_frequency_sum': List(Value('float64'))}, '9': {'ean_mean': List(Value('float64')), 'ean_sum': List(Value('float64')), 'expert_frequency': List(Value('int64')), 'max_activations': List(Value('float64')), 'reap': List(Value('float64')), 'total_tokens': Value('int64'), 'weighted_ean_sum': List(Value('float64')), 'weighted_expert_frequency_sum': List(Value('float64'))}}}}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1800, in _prepare_split_single
                  writer.write_table(table)
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 764, in write_table
                  self.write_rows_on_file()  # in case there are buffered rows to write first
                  ^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 663, in write_rows_on_file
                  self._write_table(table)
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 773, in _write_table
                  pa_table = table_cast(pa_table, self._schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2321, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2249, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              created_at: timestamp[s]
              workflow: string
              model_path: string
              input_pt: string
              target_length: int64
              pack_samples: bool
              total_bundle_batches: int64
              selected_batches: int64
              selected_samples: int64
              selected_logical_ids: list<item: string>
                child 0, item: string
              max_group_batches: int64
              shard_index: null
              num_shards: null
              to
              {'workflow': Value('string'), 'model_path': Value('string'), 'input_pt': Value('string'), 'processedSamples': Value('int64'), 'processedTokens': Value('int64'), 'categories': {'layers': {'10': {'ean_mean': List(Value('float64')), 'ean_sum': List(Value('float64')), 'expert_frequency': List(Value('int64')), 'max_activations': List(Value('float64')), 'reap': List(Value('float64')), 'total_tokens': Value('int64'), 'weighted_ean_sum': List(Value('float64')), 'weighted_expert_frequency_sum': List(Value('float64'))}, '11': {'ean_mean': List(Value('float64')), 'ean_sum': List(Value('float64')), 'expert_frequency': List(Value('int64')), 'max_activations': List(Value('float64')), 'reap': List(Value('float64')), 'total_tokens': Value('int64'), 'weighted_ean_sum': List(Value('float64')), 'weighted_expert_frequency_sum': List(Value('float64'))}, '12': {'ean_mean': List(Value('float64')), 'ean_sum': List(Value('float64')), 'expert_frequency': List(Value('int64')), 'max_activations': List(Value('float64')), 'reap': List(Value('float64')), 'total_tokens': Value('int64'), 'weighted_ean_sum': List(Value('float64')), 'weighted_expert_frequency_sum': List(Value('float64'))}, '13': {'ean_mean': List(Value('float64')), 'ean_sum': List(Value('float64')), 'expert_frequency': List(Value('int64')), 'max_activations': List(Value('float64')), 'reap': List(Value('float64')), 'total_tokens': Value('int64'), 'weighted_ean_sum': List(Value('float64')), 'weighted_expert_frequency_sum': List(Value('float64'))
              ...
              Value('float64')), 'reap': List(Value('float64')), 'total_tokens': Value('int64'), 'weighted_ean_sum': List(Value('float64')), 'weighted_expert_frequency_sum': List(Value('float64'))}, '76': {'ean_mean': List(Value('float64')), 'ean_sum': List(Value('float64')), 'expert_frequency': List(Value('int64')), 'max_activations': List(Value('float64')), 'reap': List(Value('float64')), 'total_tokens': Value('int64'), 'weighted_ean_sum': List(Value('float64')), 'weighted_expert_frequency_sum': List(Value('float64'))}, '77': {'ean_mean': List(Value('float64')), 'ean_sum': List(Value('float64')), 'expert_frequency': List(Value('int64')), 'max_activations': List(Value('float64')), 'reap': List(Value('float64')), 'total_tokens': Value('int64'), 'weighted_ean_sum': List(Value('float64')), 'weighted_expert_frequency_sum': List(Value('float64'))}, '8': {'ean_mean': List(Value('float64')), 'ean_sum': List(Value('float64')), 'expert_frequency': List(Value('int64')), 'max_activations': List(Value('float64')), 'reap': List(Value('float64')), 'total_tokens': Value('int64'), 'weighted_ean_sum': List(Value('float64')), 'weighted_expert_frequency_sum': List(Value('float64'))}, '9': {'ean_mean': List(Value('float64')), 'ean_sum': List(Value('float64')), 'expert_frequency': List(Value('int64')), 'max_activations': List(Value('float64')), 'reap': List(Value('float64')), 'total_tokens': Value('int64'), 'weighted_ean_sum': List(Value('float64')), 'weighted_expert_frequency_sum': List(Value('float64'))}}}}
              because column names don't match
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1802, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
              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 10 new columns ({'shard_index', 'selected_samples', 'selected_logical_ids', 'target_length', 'total_bundle_batches', 'max_group_batches', 'pack_samples', 'selected_batches', 'num_shards', 'created_at'}) and 3 missing columns ({'processedSamples', 'categories', 'processedTokens'}).
              
              This happened while the json dataset builder was generating data using
              
              hf://datasets/mateowilliam/glm5-layerwise-reap-observations/glm5-grouped-22k-20260331T172330Z/glm5-grouped-22k-20260331T172330Z-sample-summary.jsonl (at revision cc2a93ce10c7e997c6b85ffd28110e12e3341964), ['hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/glm5-grouped-22k-20260331T172330Z-category-summary.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/glm5-grouped-22k-20260331T172330Z-manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/glm5-grouped-22k-20260331T172330Z-sample-summary.jsonl', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/glm5-grouped-22k-20260331T172330Z-status.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/glm5-grouped-22k-20260331T172330Z-upload-log.jsonl', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_000/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_001/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_002/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_003/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_004/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_005/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_006/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_007/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_008/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_009/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_010/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_011/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_012/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_013/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_014/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_015/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_016/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_017/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_018/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_019/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_020/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_021/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_022/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_023/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_024/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_025/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_026/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_027/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_028/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_029/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_030/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_031/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_032/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_033/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_034/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_035/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_036/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_037/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_038/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_039/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_040/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_041/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_042/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_043/manifest.json', 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'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_183/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_184/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_185/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_186/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_187/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_188/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_189/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_190/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_191/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_192/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_193/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_194/group_194/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_194/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_195/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_196/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_197/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_198/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_199/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_200/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_201/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_202/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_203/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_204/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_205/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_206/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_207/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_208/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_209/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_210/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_211/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_212/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_213/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_214/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_215/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_216/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_217/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/intermediate/group_218/manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/run/glm5-grouped-22k-20260331T172330Z-category-summary.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/run/glm5-grouped-22k-20260331T172330Z-manifest.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/run/glm5-grouped-22k-20260331T172330Z-sample-summary.jsonl', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/run/glm5-grouped-22k-20260331T172330Z-status.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/run/glm5-grouped-22k-20260331T172330Z-summary.json', 'hf://datasets/mateowilliam/glm5-layerwise-reap-observations@cc2a93ce10c7e997c6b85ffd28110e12e3341964/glm5-grouped-22k-20260331T172330Z/run/glm5-grouped-22k-20260331T172330Z-upload-log.jsonl']
              
              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)
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1821, in _prepare_split_single
                  num_examples, num_bytes = writer.finalize()
                                            ^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 781, in finalize
                  self.write_rows_on_file()
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 663, in write_rows_on_file
                  self._write_table(table)
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 773, in _write_table
                  pa_table = table_cast(pa_table, self._schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2321, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2249, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              created_at: timestamp[s]
              workflow: string
              model_path: string
              input_pt: string
              target_length: int64
              pack_samples: bool
              total_bundle_batches: int64
              selected_batches: int64
              selected_samples: int64
              selected_logical_ids: list<item: string>
                child 0, item: string
              max_group_batches: int64
              shard_index: null
              num_shards: null
              to
              {'workflow': Value('string'), 'model_path': Value('string'), 'input_pt': Value('string'), 'processedSamples': Value('int64'), 'processedTokens': Value('int64'), 'categories': {'layers': {'10': {'ean_mean': List(Value('float64')), 'ean_sum': List(Value('float64')), 'expert_frequency': List(Value('int64')), 'max_activations': List(Value('float64')), 'reap': List(Value('float64')), 'total_tokens': Value('int64'), 'weighted_ean_sum': List(Value('float64')), 'weighted_expert_frequency_sum': List(Value('float64'))}, '11': {'ean_mean': List(Value('float64')), 'ean_sum': List(Value('float64')), 'expert_frequency': List(Value('int64')), 'max_activations': List(Value('float64')), 'reap': List(Value('float64')), 'total_tokens': Value('int64'), 'weighted_ean_sum': List(Value('float64')), 'weighted_expert_frequency_sum': List(Value('float64'))}, '12': {'ean_mean': List(Value('float64')), 'ean_sum': List(Value('float64')), 'expert_frequency': List(Value('int64')), 'max_activations': List(Value('float64')), 'reap': List(Value('float64')), 'total_tokens': Value('int64'), 'weighted_ean_sum': List(Value('float64')), 'weighted_expert_frequency_sum': List(Value('float64'))}, '13': {'ean_mean': List(Value('float64')), 'ean_sum': List(Value('float64')), 'expert_frequency': List(Value('int64')), 'max_activations': List(Value('float64')), 'reap': List(Value('float64')), 'total_tokens': Value('int64'), 'weighted_ean_sum': List(Value('float64')), 'weighted_expert_frequency_sum': List(Value('float64'))
              ...
              Value('float64')), 'reap': List(Value('float64')), 'total_tokens': Value('int64'), 'weighted_ean_sum': List(Value('float64')), 'weighted_expert_frequency_sum': List(Value('float64'))}, '76': {'ean_mean': List(Value('float64')), 'ean_sum': List(Value('float64')), 'expert_frequency': List(Value('int64')), 'max_activations': List(Value('float64')), 'reap': List(Value('float64')), 'total_tokens': Value('int64'), 'weighted_ean_sum': List(Value('float64')), 'weighted_expert_frequency_sum': List(Value('float64'))}, '77': {'ean_mean': List(Value('float64')), 'ean_sum': List(Value('float64')), 'expert_frequency': List(Value('int64')), 'max_activations': List(Value('float64')), 'reap': List(Value('float64')), 'total_tokens': Value('int64'), 'weighted_ean_sum': List(Value('float64')), 'weighted_expert_frequency_sum': List(Value('float64'))}, '8': {'ean_mean': List(Value('float64')), 'ean_sum': List(Value('float64')), 'expert_frequency': List(Value('int64')), 'max_activations': List(Value('float64')), 'reap': List(Value('float64')), 'total_tokens': Value('int64'), 'weighted_ean_sum': List(Value('float64')), 'weighted_expert_frequency_sum': List(Value('float64'))}, '9': {'ean_mean': List(Value('float64')), 'ean_sum': List(Value('float64')), 'expert_frequency': List(Value('int64')), 'max_activations': List(Value('float64')), 'reap': List(Value('float64')), 'total_tokens': Value('int64'), 'weighted_ean_sum': List(Value('float64')), 'weighted_expert_frequency_sum': List(Value('float64'))}}}}
              because column names don't match
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1342, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 907, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1646, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1832, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset
Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
workflow string	model_path string	input_pt string	processedSamples int64	processedTokens int64	categories dict
glm5-grouped-22k-20260331T172330Z	/data0/external_research/models/GLM-5-BF16	"/data0/external_research/b200-glm-reap-smoke/glm-reap/test-output/glm5-full-detached-20260331T07470(...TRUNCATED)	6,714	81,531,087	{"layers":{"10":{"ean_mean":[0.0,0.10833333432674401,0.129340276122093,0.0,0.203821122646331,0.0,0.0(...TRUNCATED)
glm5-grouped-22k-20260331T172330Z	/data0/external_research/models/GLM-5-BF16	"/data0/external_research/b200-glm-reap-smoke/glm-reap/test-output/glm5-full-detached-20260331T07470(...TRUNCATED)	6,714	81,531,087	{"layers":{"10":{"ean_mean":[0.189772829413414,0.15184329450130402,0.14072568714618602,0.17255420982(...TRUNCATED)
glm5-grouped-22k-20260331T172330Z	/data0/external_research/models/GLM-5-BF16	"/data0/external_research/b200-glm-reap-smoke/glm-reap/test-output/glm5-full-detached-20260331T07470(...TRUNCATED)	6,714	81,531,087	{"layers":{"10":{"ean_mean":[0.23590882122516602,0.0,0.2462158203125,0.0,0.222026854753494,0.0,0.0,0(...TRUNCATED)
glm5-grouped-22k-20260331T172330Z	/data0/external_research/models/GLM-5-BF16	"/data0/external_research/b200-glm-reap-smoke/glm-reap/test-output/glm5-full-detached-20260331T07470(...TRUNCATED)	6,714	81,531,087	{"layers":{"10":{"ean_mean":[0.17544420063495603,0.18375480175018302,0.12265010923147202,0.179585888(...TRUNCATED)
glm5-grouped-22k-20260331T172330Z	/data0/external_research/models/GLM-5-BF16	"/data0/external_research/b200-glm-reap-smoke/glm-reap/test-output/glm5-full-detached-20260331T07470(...TRUNCATED)	6,714	81,531,087	{"layers":{"10":{"ean_mean":[0.23777173459529802,0.0,0.0,0.0,0.20462246239185303,0.0,0.0,0.210115134(...TRUNCATED)
glm5-grouped-22k-20260331T172330Z	/data0/external_research/models/GLM-5-BF16	"/data0/external_research/b200-glm-reap-smoke/glm-reap/test-output/glm5-full-detached-20260331T07470(...TRUNCATED)	6,714	81,531,087	{"layers":{"10":{"ean_mean":[0.19754375517368303,0.15313385426998102,0.141434237360954,0.18380297720(...TRUNCATED)
glm5-grouped-22k-20260331T172330Z	/data0/external_research/models/GLM-5-BF16	"/data0/external_research/b200-glm-reap-smoke/glm-reap/test-output/glm5-full-detached-20260331T07470(...TRUNCATED)	6,714	81,531,087	{"layers":{"10":{"ean_mean":[0.189072921872138,0.15069399774074502,0.15604981780052102,0.17287187278(...TRUNCATED)
glm5-grouped-22k-20260331T172330Z	/data0/external_research/models/GLM-5-BF16	"/data0/external_research/b200-glm-reap-smoke/glm-reap/test-output/glm5-full-detached-20260331T07470(...TRUNCATED)	6,714	81,531,087	{"layers":{"10":{"ean_mean":[0.205236688256263,0.15812487900257102,0.161864534020423,0.1662817597389(...TRUNCATED)
glm5-grouped-22k-20260331T172330Z	/data0/external_research/models/GLM-5-BF16	"/data0/external_research/b200-glm-reap-smoke/glm-reap/test-output/glm5-full-detached-20260331T07470(...TRUNCATED)	6,714	81,531,087	{"layers":{"10":{"ean_mean":[0.24534696340560902,0.0,0.0,0.19288793206214902,0.21320690214633903,0.1(...TRUNCATED)
glm5-grouped-22k-20260331T172330Z	/data0/external_research/models/GLM-5-BF16	"/data0/external_research/b200-glm-reap-smoke/glm-reap/test-output/glm5-full-detached-20260331T07470(...TRUNCATED)	6,714	81,531,087	{"layers":{"10":{"ean_mean":[0.0,0.127130687236785,0.0,0.1958539634943,0.207051932811737,0.0,0.10825(...TRUNCATED)