Datasets:

---

algabis

/

kodr-artifacts

Like 0

Error code:   DatasetGenerationError
Exception:    CastError
Message:      Couldn't cast
alora_invocation_tokens: null
alpha_pattern: struct<>
arrow_config: null
auto_mapping: struct<base_model_class: string, parent_library: string, unsloth_fixed: bool>
  child 0, base_model_class: string
  child 1, parent_library: string
  child 2, unsloth_fixed: bool
base_model_name_or_path: string
bias: string
corda_config: null
ensure_weight_tying: bool
eva_config: null
exclude_modules: null
fan_in_fan_out: bool
inference_mode: bool
init_lora_weights: bool
kasa_config: null
layer_replication: null
layers_pattern: null
layers_to_transform: null
loftq_config: struct<>
lora_alpha: int64
lora_bias: bool
lora_dropout: int64
lora_ga_config: null
megatron_config: null
megatron_core: string
modules_to_save: null
monteclora_config: null
peft_type: string
peft_version: string
qalora_group_size: int64
r: int64
rank_pattern: struct<>
revision: null
target_modules: list<item: string>
  child 0, item: string
target_parameters: null
task_type: string
trainable_token_indices: null
use_bdlora: null
use_dora: bool
use_qalora: bool
use_rslora: bool
velora_config: null
assistant_step: int64
source: string
id: string
category: string
teacher: string
messages: list<item: struct<role: string, content: string>>
  child 0, item: struct<role: string, content: string>
      child 0, role: string
      child 1, content: string
skill: string
domain: string
quality: double
model: string
has_think: bool
to
{'messages': List({'role': Value('string'), 'content': Value('string')}), 'skill': Value('string'), 'source': Value('string'), 'teacher': Value('string'), 'has_think': Value('bool'), 'quality': Value('float64'), 'id': Value('string'), 'category': Value('string'), 'model': Value('string'), 'domain': Value('string'), 'assistant_step': Value('int64')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1827, in _prepare_split_single
                  for key, table in generator:
                                    ^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
                  for item in generator(*args, **kwargs):
                              ~~~~~~~~~^^^^^^^^^^^^^^^^^
                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
              alora_invocation_tokens: null
              alpha_pattern: struct<>
              arrow_config: null
              auto_mapping: struct<base_model_class: string, parent_library: string, unsloth_fixed: bool>
                child 0, base_model_class: string
                child 1, parent_library: string
                child 2, unsloth_fixed: bool
              base_model_name_or_path: string
              bias: string
              corda_config: null
              ensure_weight_tying: bool
              eva_config: null
              exclude_modules: null
              fan_in_fan_out: bool
              inference_mode: bool
              init_lora_weights: bool
              kasa_config: null
              layer_replication: null
              layers_pattern: null
              layers_to_transform: null
              loftq_config: struct<>
              lora_alpha: int64
              lora_bias: bool
              lora_dropout: int64
              lora_ga_config: null
              megatron_config: null
              megatron_core: string
              modules_to_save: null
              monteclora_config: null
              peft_type: string
              peft_version: string
              qalora_group_size: int64
              r: int64
              rank_pattern: struct<>
              revision: null
              target_modules: list<item: string>
                child 0, item: string
              target_parameters: null
              task_type: string
              trainable_token_indices: null
              use_bdlora: null
              use_dora: bool
              use_qalora: bool
              use_rslora: bool
              velora_config: null
              assistant_step: int64
              source: string
              id: string
              category: string
              teacher: string
              messages: list<item: struct<role: string, content: string>>
                child 0, item: struct<role: string, content: string>
                    child 0, role: string
                    child 1, content: string
              skill: string
              domain: string
              quality: double
              model: string
              has_think: bool
              to
              {'messages': List({'role': Value('string'), 'content': Value('string')}), 'skill': Value('string'), 'source': Value('string'), 'teacher': Value('string'), 'has_think': Value('bool'), 'quality': Value('float64'), 'id': Value('string'), 'category': Value('string'), 'model': Value('string'), 'domain': Value('string'), 'assistant_step': Value('int64')}
              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 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 1879, 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