Datasets:

---

HusnainAmjad

/

Deepseek_Math_7b_r1_Outputs

Like 0

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
model: string
data: string
extra_data: list<item: null>
  child 0, item: null
replay_ratio: double
replay_mode: string
replay_strategy: string
mode: string
output_dir: string
lora_r: int64
lora_alpha: int64
lora_dropout: double
epochs: double
lr: double
per_device_batch_size: int64
grad_accum: int64
max_seq_len: int64
bf16: bool
gradient_checkpointing: bool
use_bnb: bool
torch_compile: bool
packing: bool
seed: int64
strict_deterministic: bool
skip_merge: bool
save_every_epochs: double
save_total_limit: null
push_every_checkpoint: bool
resume_from_checkpoint: null
push_to: string
hf_repo_id: null
hf_repo_type: string
hf_private: bool
hf_token: null
hf_path_in_repo: null
gdrive_path: null
w_persistence: double
w_chain_stability: double
w_correctness: double
w_format: double
num_train_epochs: double
use_vllm: bool
beta: double
max_prompt_length: int64
num_generations: int64
stage: string
max_new_tokens: int64
to
{'model': Value('string'), 'data': Value('string'), 'output_dir': Value('string'), 'num_generations': Value('int64'), 'gradient_checkpointing': Value('bool'), 'max_new_tokens': Value('int64'), 'max_prompt_length': Value('int64'), 'lr': Value('float64'), 'per_device_batch_size': Value('int64'), 'grad_accum': Value('int64'), 'num_train_epochs': Value('float64'), 'beta': Value('float64'), 'w_correctness': Value('float64'), 'w_format': Value('float64'), 'w_persistence': Value('float64'), 'w_chain_stability': Value('float64'), 'use_vllm': Value('bool'), 'skip_merge': Value('bool'), 'lora_r': Value('int64'), 'lora_alpha': Value('int64'), 'seed': Value('int64'), 'strict_deterministic': Value('bool'), 'push_to': Value('string'), 'hf_repo_id': Value('null'), 'hf_repo_type': Value('string'), 'hf_private': Value('bool'), 'hf_token': Value('null'), 'hf_path_in_repo': Value('null'), 'gdrive_path': Value('null'), 'mode': Value('string'), 'stage': Value('string')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 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
              model: string
              data: string
              extra_data: list<item: null>
                child 0, item: null
              replay_ratio: double
              replay_mode: string
              replay_strategy: string
              mode: string
              output_dir: string
              lora_r: int64
              lora_alpha: int64
              lora_dropout: double
              epochs: double
              lr: double
              per_device_batch_size: int64
              grad_accum: int64
              max_seq_len: int64
              bf16: bool
              gradient_checkpointing: bool
              use_bnb: bool
              torch_compile: bool
              packing: bool
              seed: int64
              strict_deterministic: bool
              skip_merge: bool
              save_every_epochs: double
              save_total_limit: null
              push_every_checkpoint: bool
              resume_from_checkpoint: null
              push_to: string
              hf_repo_id: null
              hf_repo_type: string
              hf_private: bool
              hf_token: null
              hf_path_in_repo: null
              gdrive_path: null
              w_persistence: double
              w_chain_stability: double
              w_correctness: double
              w_format: double
              num_train_epochs: double
              use_vllm: bool
              beta: double
              max_prompt_length: int64
              num_generations: int64
              stage: string
              max_new_tokens: int64
              to
              {'model': Value('string'), 'data': Value('string'), 'output_dir': Value('string'), 'num_generations': Value('int64'), 'gradient_checkpointing': Value('bool'), 'max_new_tokens': Value('int64'), 'max_prompt_length': Value('int64'), 'lr': Value('float64'), 'per_device_batch_size': Value('int64'), 'grad_accum': Value('int64'), 'num_train_epochs': Value('float64'), 'beta': Value('float64'), 'w_correctness': Value('float64'), 'w_format': Value('float64'), 'w_persistence': Value('float64'), 'w_chain_stability': Value('float64'), 'use_vllm': Value('bool'), 'skip_merge': Value('bool'), 'lora_r': Value('int64'), 'lora_alpha': Value('int64'), 'seed': Value('int64'), 'strict_deterministic': Value('bool'), 'push_to': Value('string'), 'hf_repo_id': Value('null'), 'hf_repo_type': Value('string'), 'hf_private': Value('bool'), 'hf_token': Value('null'), 'hf_path_in_repo': Value('null'), 'gdrive_path': Value('null'), 'mode': Value('string'), 'stage': Value('string')}
              because column names don't match