Datasets:

---

amildravid4292

/

mathnet-strategy-rank1-loras-10k

Like 0

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
base_model: string
capability: string
created_at: string
log_history: list<item: struct<epoch: double, grad_norm: double, learning_rate: double, loss: double, step: int64 (... 131 chars omitted)
  child 0, item: struct<epoch: double, grad_norm: double, learning_rate: double, loss: double, step: int64, total_flo (... 119 chars omitted)
      child 0, epoch: double
      child 1, grad_norm: double
      child 2, learning_rate: double
      child 3, loss: double
      child 4, step: int64
      child 5, total_flos: double
      child 6, train_loss: double
      child 7, train_runtime: double
      child 8, train_samples_per_second: double
      child 9, train_steps_per_second: double
lora: struct<alpha: int64, bias: string, dropout: double, r: int64, target_modules: list<item: string>>
  child 0, alpha: int64
  child 1, bias: string
  child 2, dropout: double
  child 3, r: int64
  child 4, target_modules: list<item: string>
      child 0, item: string
replicate: int64
seed: int64
train_example_ids: list<item: string>
  child 0, item: string
train_metrics: struct<epoch: double, total_flos: double, train_loss: double, train_runtime: double, train_samples_p (... 50 chars omitted)
  child 0, epoch: double
  child 1, total_flos: double
  child 2, train_loss: double
  child 3, train_runtime: double
  child 4, train_samples_per_second: double
  child 5, train_steps_per_second: double
train_path: string
training: struct<bf16: bool, fp16: bool, gradient_accumulation_steps: int64, gradient_checkpointing: bool, lea (... 230 chars omitted)
  child 0, bf16: bool
  child 1, fp16: bool
  child 2, gradient_accumulation_steps: int64
  child 3, gradient_checkpointing: bool
  child 4, learning_rate: double
  child 5, logging_steps: int64
  child 6, max_seq_length: int64
  child 7, num_train_epochs: int64
  child 8, output_dir: string
  child 9, per_device_train_batch_size: int64
  child 10, same_seed_all_loras: bool
  child 11, save_strategy: string
  child 12, seed: int64
  child 13, warmup_ratio: double
r: int64
use_qalora: bool
auto_mapping: null
alora_invocation_tokens: null
use_dora: bool
exclude_modules: null
target_modules: list<item: string>
  child 0, item: string
lora_bias: bool
target_parameters: null
corda_config: null
init_lora_weights: bool
use_bdlora: null
lora_alpha: int64
use_rslora: bool
fan_in_fan_out: bool
megatron_core: string
task_type: string
bias: string
arrow_config: null
ensure_weight_tying: bool
inference_mode: bool
trainable_token_indices: null
rank_pattern: struct<>
alpha_pattern: struct<>
megatron_config: null
lora_dropout: double
peft_version: string
base_model_name_or_path: string
layers_pattern: null
peft_type: string
modules_to_save: null
lora_ga_config: null
qalora_group_size: int64
revision: null
layer_replication: null
eva_config: null
loftq_config: struct<>
layers_to_transform: null
to
{'alora_invocation_tokens': Value('null'), 'alpha_pattern': {}, 'arrow_config': Value('null'), 'auto_mapping': Value('null'), 'base_model_name_or_path': Value('string'), 'bias': Value('string'), 'corda_config': Value('null'), 'ensure_weight_tying': Value('bool'), 'eva_config': Value('null'), 'exclude_modules': Value('null'), 'fan_in_fan_out': Value('bool'), 'inference_mode': Value('bool'), 'init_lora_weights': Value('bool'), 'layer_replication': Value('null'), 'layers_pattern': Value('null'), 'layers_to_transform': Value('null'), 'loftq_config': {}, 'lora_alpha': Value('int64'), 'lora_bias': Value('bool'), 'lora_dropout': Value('float64'), 'lora_ga_config': Value('null'), 'megatron_config': Value('null'), 'megatron_core': Value('string'), 'modules_to_save': Value('null'), 'peft_type': Value('string'), 'peft_version': Value('string'), 'qalora_group_size': Value('int64'), 'r': Value('int64'), 'rank_pattern': {}, 'revision': Value('null'), 'target_modules': List(Value('string')), 'target_parameters': Value('null'), 'task_type': Value('string'), 'trainable_token_indices': Value('null'), 'use_bdlora': Value('null'), 'use_dora': Value('bool'), 'use_qalora': Value('bool'), 'use_rslora': Value('bool')}
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 478, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2818, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              base_model: string
              capability: string
              created_at: string
              log_history: list<item: struct<epoch: double, grad_norm: double, learning_rate: double, loss: double, step: int64 (... 131 chars omitted)
                child 0, item: struct<epoch: double, grad_norm: double, learning_rate: double, loss: double, step: int64, total_flo (... 119 chars omitted)
                    child 0, epoch: double
                    child 1, grad_norm: double
                    child 2, learning_rate: double
                    child 3, loss: double
                    child 4, step: int64
                    child 5, total_flos: double
                    child 6, train_loss: double
                    child 7, train_runtime: double
                    child 8, train_samples_per_second: double
                    child 9, train_steps_per_second: double
              lora: struct<alpha: int64, bias: string, dropout: double, r: int64, target_modules: list<item: string>>
                child 0, alpha: int64
                child 1, bias: string
                child 2, dropout: double
                child 3, r: int64
                child 4, target_modules: list<item: string>
                    child 0, item: string
              replicate: int64
              seed: int64
              train_example_ids: list<item: string>
                child 0, item: string
              train_metrics: struct<epoch: double, total_flos: double, train_loss: double, train_runtime: double, train_samples_p (... 50 chars omitted)
                child 0, epoch: double
                child 1, total_flos: double
                child 2, train_loss: double
                child 3, train_runtime: double
                child 4, train_samples_per_second: double
                child 5, train_steps_per_second: double
              train_path: string
              training: struct<bf16: bool, fp16: bool, gradient_accumulation_steps: int64, gradient_checkpointing: bool, lea (... 230 chars omitted)
                child 0, bf16: bool
                child 1, fp16: bool
                child 2, gradient_accumulation_steps: int64
                child 3, gradient_checkpointing: bool
                child 4, learning_rate: double
                child 5, logging_steps: int64
                child 6, max_seq_length: int64
                child 7, num_train_epochs: int64
                child 8, output_dir: string
                child 9, per_device_train_batch_size: int64
                child 10, same_seed_all_loras: bool
                child 11, save_strategy: string
                child 12, seed: int64
                child 13, warmup_ratio: double
              r: int64
              use_qalora: bool
              auto_mapping: null
              alora_invocation_tokens: null
              use_dora: bool
              exclude_modules: null
              target_modules: list<item: string>
                child 0, item: string
              lora_bias: bool
              target_parameters: null
              corda_config: null
              init_lora_weights: bool
              use_bdlora: null
              lora_alpha: int64
              use_rslora: bool
              fan_in_fan_out: bool
              megatron_core: string
              task_type: string
              bias: string
              arrow_config: null
              ensure_weight_tying: bool
              inference_mode: bool
              trainable_token_indices: null
              rank_pattern: struct<>
              alpha_pattern: struct<>
              megatron_config: null
              lora_dropout: double
              peft_version: string
              base_model_name_or_path: string
              layers_pattern: null
              peft_type: string
              modules_to_save: null
              lora_ga_config: null
              qalora_group_size: int64
              revision: null
              layer_replication: null
              eva_config: null
              loftq_config: struct<>
              layers_to_transform: null
              to
              {'alora_invocation_tokens': Value('null'), 'alpha_pattern': {}, 'arrow_config': Value('null'), 'auto_mapping': Value('null'), 'base_model_name_or_path': Value('string'), 'bias': Value('string'), 'corda_config': Value('null'), 'ensure_weight_tying': Value('bool'), 'eva_config': Value('null'), 'exclude_modules': Value('null'), 'fan_in_fan_out': Value('bool'), 'inference_mode': Value('bool'), 'init_lora_weights': Value('bool'), 'layer_replication': Value('null'), 'layers_pattern': Value('null'), 'layers_to_transform': Value('null'), 'loftq_config': {}, 'lora_alpha': Value('int64'), 'lora_bias': Value('bool'), 'lora_dropout': Value('float64'), 'lora_ga_config': Value('null'), 'megatron_config': Value('null'), 'megatron_core': Value('string'), 'modules_to_save': Value('null'), 'peft_type': Value('string'), 'peft_version': Value('string'), 'qalora_group_size': Value('int64'), 'r': Value('int64'), 'rank_pattern': {}, 'revision': Value('null'), 'target_modules': List(Value('string')), 'target_parameters': Value('null'), 'task_type': Value('string'), 'trainable_token_indices': Value('null'), 'use_bdlora': Value('null'), 'use_dora': Value('bool'), 'use_qalora': Value('bool'), 'use_rslora': Value('bool')}
              because column names don't match