Datasets:

---

kuldeeptapodhan

/

MP2

Like 0

Error code:   DatasetGenerationError
Exception:    CastError
Message:      Couldn't cast
run: string
kind: string
base_model: string
rank: int64
alpha: int64
lora_dropout: double
target_modules: list<item: string>
  child 0, item: string
seed: int64
train_file: string
val_file: string
train_data: struct<records: int64, used: int64, dropped: int64, truncated: int64, tokens: int64>
  child 0, records: int64
  child 1, used: int64
  child 2, dropped: int64
  child 3, truncated: int64
  child 4, tokens: int64
val_data: struct<records: int64, used: int64, dropped: int64, truncated: int64, tokens: int64>
  child 0, records: int64
  child 1, used: int64
  child 2, dropped: int64
  child 3, truncated: int64
  child 4, tokens: int64
max_seq_len: int64
effective_batch: int64
steps: int64
epochs: int64
learning_rate: double
optimizer: string
group_by_length: bool
trainable_params: int64
total_params: int64
trainable_pct: double
val_loss_final: double
val_loss_best: double
train_loss_last_logged: double
training_time_s: double
training_time_h: double
resumed: bool
train_samples_per_s_this_session: double
gpu_memory: struct<peak_alloc_GB: double, peak_reserved_GB: double>
  child 0, peak_alloc_GB: double
  child 1, peak_reserved_GB: double
gpu_monitor_this_session: struct<wall_s: double, energy_J: double, energy_Wh: double, power_mean_W: double, power_max_W: doubl (... 72 chars omitted)
  child 0, wall_s: double
  child 1, energy_J: double
  child 2, energy_Wh: double
  child 3, power_mean_W: double
  child 4, power_max_W: double
  child 5, gpu_util_mean_pct: double
  child 6, nvml_mem_max_GB: double
  child 7, n_samples: int64
md5: struct<phase1.json: string, prompts.json: string, data/processed/train.jsonl: string>
  child 0, phase1.json: string
  child 1, prompts.json: string
  child 2, data/processed/train.jsonl: string
env: struct<time: timestamp[s], python: string, platform: string, torch: string, transformers: string, pe (... 197 chars omitted)
  child 0, time: timestamp[s]
  child 1, python: string
  child 2, platform: string
  child 3, torch: string
  child 4, transformers: string
  child 5, peft: string
  child 6, bitsandbytes: string
  child 7, accelerate: string
  child 8, datasets: string
  child 9, sentence_transformers: string
  child 10, marimo: string
  child 11, gpu: string
  child 12, gpu_mem_gb: double
  child 13, cuda: string
  child 14, capability: string
  child 15, bf16: bool
finished: timestamp[s]
to
{'loss': Value('float64'), 'grad_norm': Value('float64'), 'learning_rate': Value('float64'), 'epoch': Value('float64'), 'step': Value('int64'), 'eval_loss': Value('float64'), 'eval_runtime': Value('float64'), 'eval_samples_per_second': Value('float64'), 'eval_steps_per_second': Value('float64'), 'train_runtime': Value('float64'), 'train_samples_per_second': Value('float64'), 'train_steps_per_second': Value('float64'), 'total_flos': Value('float64'), 'train_loss': Value('float64')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1856, in _prepare_split_single
                  writer.write_table(table)
                  ~~~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 810, in write_table
                  self.write_rows_on_file()  # in case there are buffered rows to write first
                  ~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 690, in write_rows_on_file
                  self._write_table(table)
                  ~~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 822, in _write_table
                  pa_table = table_cast(pa_table, self._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
              run: string
              kind: string
              base_model: string
              rank: int64
              alpha: int64
              lora_dropout: double
              target_modules: list<item: string>
                child 0, item: string
              seed: int64
              train_file: string
              val_file: string
              train_data: struct<records: int64, used: int64, dropped: int64, truncated: int64, tokens: int64>
                child 0, records: int64
                child 1, used: int64
                child 2, dropped: int64
                child 3, truncated: int64
                child 4, tokens: int64
              val_data: struct<records: int64, used: int64, dropped: int64, truncated: int64, tokens: int64>
                child 0, records: int64
                child 1, used: int64
                child 2, dropped: int64
                child 3, truncated: int64
                child 4, tokens: int64
              max_seq_len: int64
              effective_batch: int64
              steps: int64
              epochs: int64
              learning_rate: double
              optimizer: string
              group_by_length: bool
              trainable_params: int64
              total_params: int64
              trainable_pct: double
              val_loss_final: double
              val_loss_best: double
              train_loss_last_logged: double
              training_time_s: double
              training_time_h: double
              resumed: bool
              train_samples_per_s_this_session: double
              gpu_memory: struct<peak_alloc_GB: double, peak_reserved_GB: double>
                child 0, peak_alloc_GB: double
                child 1, peak_reserved_GB: double
              gpu_monitor_this_session: struct<wall_s: double, energy_J: double, energy_Wh: double, power_mean_W: double, power_max_W: doubl (... 72 chars omitted)
                child 0, wall_s: double
                child 1, energy_J: double
                child 2, energy_Wh: double
                child 3, power_mean_W: double
                child 4, power_max_W: double
                child 5, gpu_util_mean_pct: double
                child 6, nvml_mem_max_GB: double
                child 7, n_samples: int64
              md5: struct<phase1.json: string, prompts.json: string, data/processed/train.jsonl: string>
                child 0, phase1.json: string
                child 1, prompts.json: string
                child 2, data/processed/train.jsonl: string
              env: struct<time: timestamp[s], python: string, platform: string, torch: string, transformers: string, pe (... 197 chars omitted)
                child 0, time: timestamp[s]
                child 1, python: string
                child 2, platform: string
                child 3, torch: string
                child 4, transformers: string
                child 5, peft: string
                child 6, bitsandbytes: string
                child 7, accelerate: string
                child 8, datasets: string
                child 9, sentence_transformers: string
                child 10, marimo: string
                child 11, gpu: string
                child 12, gpu_mem_gb: double
                child 13, cuda: string
                child 14, capability: string
                child 15, bf16: bool
              finished: timestamp[s]
              to
              {'loss': Value('float64'), 'grad_norm': Value('float64'), 'learning_rate': Value('float64'), 'epoch': Value('float64'), 'step': Value('int64'), 'eval_loss': Value('float64'), 'eval_runtime': Value('float64'), 'eval_samples_per_second': Value('float64'), 'eval_steps_per_second': Value('float64'), 'train_runtime': Value('float64'), 'train_samples_per_second': Value('float64'), 'train_steps_per_second': Value('float64'), 'total_flos': Value('float64'), 'train_loss': 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/python3.14/site-packages/datasets/builder.py", line 1858, 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 33 new columns ({'effective_batch', 'group_by_length', 'run', 'env', 'steps', 'max_seq_len', 'finished', 'train_loss_last_logged', 'kind', 'epochs', 'lora_dropout', 'train_samples_per_s_this_session', 'trainable_params', 'gpu_memory', 'training_time_h', 'optimizer', 'train_data', 'total_params', 'alpha', 'resumed', 'val_loss_final', 'training_time_s', 'rank', 'base_model', 'gpu_monitor_this_session', 'train_file', 'md5', 'val_file', 'seed', 'trainable_pct', 'target_modules', 'val_data', 'val_loss_best'}) and 13 missing columns ({'eval_steps_per_second', 'eval_runtime', 'epoch', 'train_runtime', 'grad_norm', 'total_flos', 'step', 'train_samples_per_second', 'train_steps_per_second', 'eval_samples_per_second', 'eval_loss', 'loss', 'train_loss'}).
              
              This happened while the json dataset builder was generating data using
              
              hf://datasets/kuldeeptapodhan/MP2/results/raw/training/final_r4_a32_seed2026_log_history.json (at revision 2fcbfed2d4e6018bb0a9d066f2f5d8caf17e7a13), ['hf://datasets/kuldeeptapodhan/MP2@2fcbfed2d4e6018bb0a9d066f2f5d8caf17e7a13/results/raw/training/final_r4_a32_seed123_log_history.json', 'hf://datasets/kuldeeptapodhan/MP2@2fcbfed2d4e6018bb0a9d066f2f5d8caf17e7a13/results/raw/training/final_r4_a32_seed123_summary.json', 'hf://datasets/kuldeeptapodhan/MP2@2fcbfed2d4e6018bb0a9d066f2f5d8caf17e7a13/results/raw/training/final_r4_a32_seed2026_log_history.json', 'hf://datasets/kuldeeptapodhan/MP2@2fcbfed2d4e6018bb0a9d066f2f5d8caf17e7a13/results/raw/training/final_r4_a32_seed2026_summary.json', 'hf://datasets/kuldeeptapodhan/MP2@2fcbfed2d4e6018bb0a9d066f2f5d8caf17e7a13/results/raw/training/final_r4_a32_seed42_log_history.json', 'hf://datasets/kuldeeptapodhan/MP2@2fcbfed2d4e6018bb0a9d066f2f5d8caf17e7a13/results/raw/training/final_r4_a32_seed42_summary.json', 'hf://datasets/kuldeeptapodhan/MP2@2fcbfed2d4e6018bb0a9d066f2f5d8caf17e7a13/results/raw/training/smoke_r16_a32_log_history.json', 'hf://datasets/kuldeeptapodhan/MP2@2fcbfed2d4e6018bb0a9d066f2f5d8caf17e7a13/results/raw/training/smoke_r16_a32_summary.json', 'hf://datasets/kuldeeptapodhan/MP2@2fcbfed2d4e6018bb0a9d066f2f5d8caf17e7a13/results/raw/training/sweep_r16_a16_log_history.json', 'hf://datasets/kuldeeptapodhan/MP2@2fcbfed2d4e6018bb0a9d066f2f5d8caf17e7a13/results/raw/training/sweep_r16_a16_summary.json', 'hf://datasets/kuldeeptapodhan/MP2@2fcbfed2d4e6018bb0a9d066f2f5d8caf17e7a13/results/raw/training/sweep_r16_a32_log_history.json', 'hf://datasets/kuldeeptapodhan/MP2@2fcbfed2d4e6018bb0a9d066f2f5d8caf17e7a13/results/raw/training/sweep_r16_a32_summary.json', 'hf://datasets/kuldeeptapodhan/MP2@2fcbfed2d4e6018bb0a9d066f2f5d8caf17e7a13/results/raw/training/sweep_r16_a64_log_history.json', 'hf://datasets/kuldeeptapodhan/MP2@2fcbfed2d4e6018bb0a9d066f2f5d8caf17e7a13/results/raw/training/sweep_r16_a64_summary.json', 'hf://datasets/kuldeeptapodhan/MP2@2fcbfed2d4e6018bb0a9d066f2f5d8caf17e7a13/results/raw/training/sweep_r32_a32_log_history.json', 'hf://datasets/kuldeeptapodhan/MP2@2fcbfed2d4e6018bb0a9d066f2f5d8caf17e7a13/results/raw/training/sweep_r32_a32_summary.json', 'hf://datasets/kuldeeptapodhan/MP2@2fcbfed2d4e6018bb0a9d066f2f5d8caf17e7a13/results/raw/training/sweep_r4_a32_log_history.json', 'hf://datasets/kuldeeptapodhan/MP2@2fcbfed2d4e6018bb0a9d066f2f5d8caf17e7a13/results/raw/training/sweep_r4_a32_summary.json', 'hf://datasets/kuldeeptapodhan/MP2@2fcbfed2d4e6018bb0a9d066f2f5d8caf17e7a13/results/raw/training/sweep_r8_a32_log_history.json', 'hf://datasets/kuldeeptapodhan/MP2@2fcbfed2d4e6018bb0a9d066f2f5d8caf17e7a13/results/raw/training/sweep_r8_a32_summary.json'], ['hf://datasets/kuldeeptapodhan/MP2@2fcbfed2d4e6018bb0a9d066f2f5d8caf17e7a13/results/raw/training/final_r4_a32_seed123_log_history.json', 'hf://datasets/kuldeeptapodhan/MP2@2fcbfed2d4e6018bb0a9d066f2f5d8caf17e7a13/results/raw/training/final_r4_a32_seed123_summary.json', 'hf://datasets/kuldeeptapodhan/MP2@2fcbfed2d4e6018bb0a9d066f2f5d8caf17e7a13/results/raw/training/final_r4_a32_seed2026_log_history.json', 'hf://datasets/kuldeeptapodhan/MP2@2fcbfed2d4e6018bb0a9d066f2f5d8caf17e7a13/results/raw/training/final_r4_a32_seed2026_summary.json', 'hf://datasets/kuldeeptapodhan/MP2@2fcbfed2d4e6018bb0a9d066f2f5d8caf17e7a13/results/raw/training/final_r4_a32_seed42_log_history.json', 'hf://datasets/kuldeeptapodhan/MP2@2fcbfed2d4e6018bb0a9d066f2f5d8caf17e7a13/results/raw/training/final_r4_a32_seed42_summary.json', 'hf://datasets/kuldeeptapodhan/MP2@2fcbfed2d4e6018bb0a9d066f2f5d8caf17e7a13/results/raw/training/smoke_r16_a32_log_history.json', 'hf://datasets/kuldeeptapodhan/MP2@2fcbfed2d4e6018bb0a9d066f2f5d8caf17e7a13/results/raw/training/smoke_r16_a32_summary.json', 'hf://datasets/kuldeeptapodhan/MP2@2fcbfed2d4e6018bb0a9d066f2f5d8caf17e7a13/results/raw/training/sweep_r16_a16_log_history.json', 'hf://datasets/kuldeeptapodhan/MP2@2fcbfed2d4e6018bb0a9d066f2f5d8caf17e7a13/results/raw/training/sweep_r16_a16_summary.json', 'hf://datasets/kuldeeptapodhan/MP2@2fcbfed2d4e6018bb0a9d066f2f5d8caf17e7a13/results/raw/training/sweep_r16_a32_log_history.json', 'hf://datasets/kuldeeptapodhan/MP2@2fcbfed2d4e6018bb0a9d066f2f5d8caf17e7a13/results/raw/training/sweep_r16_a32_summary.json', 'hf://datasets/kuldeeptapodhan/MP2@2fcbfed2d4e6018bb0a9d066f2f5d8caf17e7a13/results/raw/training/sweep_r16_a64_log_history.json', 'hf://datasets/kuldeeptapodhan/MP2@2fcbfed2d4e6018bb0a9d066f2f5d8caf17e7a13/results/raw/training/sweep_r16_a64_summary.json', 'hf://datasets/kuldeeptapodhan/MP2@2fcbfed2d4e6018bb0a9d066f2f5d8caf17e7a13/results/raw/training/sweep_r32_a32_log_history.json', 'hf://datasets/kuldeeptapodhan/MP2@2fcbfed2d4e6018bb0a9d066f2f5d8caf17e7a13/results/raw/training/sweep_r32_a32_summary.json', 'hf://datasets/kuldeeptapodhan/MP2@2fcbfed2d4e6018bb0a9d066f2f5d8caf17e7a13/results/raw/training/sweep_r4_a32_log_history.json', 'hf://datasets/kuldeeptapodhan/MP2@2fcbfed2d4e6018bb0a9d066f2f5d8caf17e7a13/results/raw/training/sweep_r4_a32_summary.json', 'hf://datasets/kuldeeptapodhan/MP2@2fcbfed2d4e6018bb0a9d066f2f5d8caf17e7a13/results/raw/training/sweep_r8_a32_log_history.json', 'hf://datasets/kuldeeptapodhan/MP2@2fcbfed2d4e6018bb0a9d066f2f5d8caf17e7a13/results/raw/training/sweep_r8_a32_summary.json']
              
              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/python3.14/site-packages/datasets/builder.py", line 1876, in _prepare_split_single
                  num_examples, num_bytes = writer.finalize()
                                            ~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 830, in finalize
                  self.write_rows_on_file()
                  ~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 690, in write_rows_on_file
                  self._write_table(table)
                  ~~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 822, in _write_table
                  pa_table = table_cast(pa_table, self._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
              run: string
              kind: string
              base_model: string
              rank: int64
              alpha: int64
              lora_dropout: double
              target_modules: list<item: string>
                child 0, item: string
              seed: int64
              train_file: string
              val_file: string
              train_data: struct<records: int64, used: int64, dropped: int64, truncated: int64, tokens: int64>
                child 0, records: int64
                child 1, used: int64
                child 2, dropped: int64
                child 3, truncated: int64
                child 4, tokens: int64
              val_data: struct<records: int64, used: int64, dropped: int64, truncated: int64, tokens: int64>
                child 0, records: int64
                child 1, used: int64
                child 2, dropped: int64
                child 3, truncated: int64
                child 4, tokens: int64
              max_seq_len: int64
              effective_batch: int64
              steps: int64
              epochs: int64
              learning_rate: double
              optimizer: string
              group_by_length: bool
              trainable_params: int64
              total_params: int64
              trainable_pct: double
              val_loss_final: double
              val_loss_best: double
              train_loss_last_logged: double
              training_time_s: double
              training_time_h: double
              resumed: bool
              train_samples_per_s_this_session: double
              gpu_memory: struct<peak_alloc_GB: double, peak_reserved_GB: double>
                child 0, peak_alloc_GB: double
                child 1, peak_reserved_GB: double
              gpu_monitor_this_session: struct<wall_s: double, energy_J: double, energy_Wh: double, power_mean_W: double, power_max_W: doubl (... 72 chars omitted)
                child 0, wall_s: double
                child 1, energy_J: double
                child 2, energy_Wh: double
                child 3, power_mean_W: double
                child 4, power_max_W: double
                child 5, gpu_util_mean_pct: double
                child 6, nvml_mem_max_GB: double
                child 7, n_samples: int64
              md5: struct<phase1.json: string, prompts.json: string, data/processed/train.jsonl: string>
                child 0, phase1.json: string
                child 1, prompts.json: string
                child 2, data/processed/train.jsonl: string
              env: struct<time: timestamp[s], python: string, platform: string, torch: string, transformers: string, pe (... 197 chars omitted)
                child 0, time: timestamp[s]
                child 1, python: string
                child 2, platform: string
                child 3, torch: string
                child 4, transformers: string
                child 5, peft: string
                child 6, bitsandbytes: string
                child 7, accelerate: string
                child 8, datasets: string
                child 9, sentence_transformers: string
                child 10, marimo: string
                child 11, gpu: string
                child 12, gpu_mem_gb: double
                child 13, cuda: string
                child 14, capability: string
                child 15, bf16: bool
              finished: timestamp[s]
              to
              {'loss': Value('float64'), 'grad_norm': Value('float64'), 'learning_rate': Value('float64'), 'epoch': Value('float64'), 'step': Value('int64'), 'eval_loss': Value('float64'), 'eval_runtime': Value('float64'), 'eval_samples_per_second': Value('float64'), 'eval_steps_per_second': Value('float64'), 'train_runtime': Value('float64'), 'train_samples_per_second': Value('float64'), 'train_steps_per_second': Value('float64'), 'total_flos': Value('float64'), 'train_loss': 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 1379, 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 956, 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 1702, 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 1887, 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