Datasets:

---

smithblack-0

/

gqc-gradient-cleaner-effect_donations

like 0

Error code:   FeaturesError
Exception:    ArrowInvalid
Message:      Schema at index 1 was different: 
name: string
seed: int64
experiment: struct<name: string, mode: string, params: struct<lr: double>>
environment_configuration: struct<model_config: struct<model_type: string, vocab_size: int64, n_embd: int64, n_head: int64, n_layer: int64, n_positions: int64>, tokenizer_config: struct<tokenizer_type: string, setup_padding_token: bool>, dataset_name: string, dataset_config: string, seq_length: int64, min_text_chars: int64, batch_size: int64, num_batches: int64, warmup_batches: int64, base_lr: double, weight_decay: double, adam_betas: list<item: double>, max_grad_norm: double, eval_every: int64, eval_batches: int64, debug_mode: bool, harness_version: double>
tuning_configuration: struct<enabled: bool, tuning_search_config: struct<enabled: bool, trials per dim: int64, tuning_batches: int64, warmup_max_batches: int64, warmup_max_steps: int64, catastrophic prune threshold: double>, optuna_codec: struct<lr: struct<name: string, low: double, high: double, log: bool>, noise_multiplier: struct<name: string, low: double, high: double, log: bool>, start: struct<name: string, low: double, high: double, log: bool>, end: struct<name: string, low: double, high: double, log: bool>, confidence: struct<name: string, low: double, high: double, log: bool>, error_tolerance: struct<name: string, low: double, high: double, log: bool>, initial_logical_batch_size: struct<name: string, low: double, high: double, log: bool>, final_logical_batch_size: struct<name: string, low: double, high: double, log: bool>, poynomial_exponent: struct<name: string, low: double, high: double, log: bool>, mode: struct<name: string, choices: list<item: string>>>>
accreditation_information: struct<enabled: bool, name: string, organization: string, email: string>
experiment_setup: struct<seeds: list<item: int64>, experiments: list<item: struct<name: string, mode: string, params: struct<lr: double, start: double, end: double>>>, max_accum_steps: int64, harness_custom_code: bool>
vs
parameters: int64
total_tokens: int64
raw_train: list<item: double>
step_train: list<item: double>
eval: list<item: double>
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 249, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 3496, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                                                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2257, in _head
                  return next(iter(self.iter(batch_size=n)))
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2461, in iter
                  for key, example in iterator:
                                      ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 1952, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 1974, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 547, in _iter_arrow
                  yield new_key, pa.Table.from_batches(chunks_buffer)
                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "pyarrow/table.pxi", line 5039, in pyarrow.lib.Table.from_batches
                File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
              pyarrow.lib.ArrowInvalid: Schema at index 1 was different: 
              name: string
              seed: int64
              experiment: struct<name: string, mode: string, params: struct<lr: double>>
              environment_configuration: struct<model_config: struct<model_type: string, vocab_size: int64, n_embd: int64, n_head: int64, n_layer: int64, n_positions: int64>, tokenizer_config: struct<tokenizer_type: string, setup_padding_token: bool>, dataset_name: string, dataset_config: string, seq_length: int64, min_text_chars: int64, batch_size: int64, num_batches: int64, warmup_batches: int64, base_lr: double, weight_decay: double, adam_betas: list<item: double>, max_grad_norm: double, eval_every: int64, eval_batches: int64, debug_mode: bool, harness_version: double>
              tuning_configuration: struct<enabled: bool, tuning_search_config: struct<enabled: bool, trials per dim: int64, tuning_batches: int64, warmup_max_batches: int64, warmup_max_steps: int64, catastrophic prune threshold: double>, optuna_codec: struct<lr: struct<name: string, low: double, high: double, log: bool>, noise_multiplier: struct<name: string, low: double, high: double, log: bool>, start: struct<name: string, low: double, high: double, log: bool>, end: struct<name: string, low: double, high: double, log: bool>, confidence: struct<name: string, low: double, high: double, log: bool>, error_tolerance: struct<name: string, low: double, high: double, log: bool>, initial_logical_batch_size: struct<name: string, low: double, high: double, log: bool>, final_logical_batch_size: struct<name: string, low: double, high: double, log: bool>, poynomial_exponent: struct<name: string, low: double, high: double, log: bool>, mode: struct<name: string, choices: list<item: string>>>>
              accreditation_information: struct<enabled: bool, name: string, organization: string, email: string>
              experiment_setup: struct<seeds: list<item: int64>, experiments: list<item: struct<name: string, mode: string, params: struct<lr: double, start: double, end: double>>>, max_accum_steps: int64, harness_custom_code: bool>
              vs
              parameters: int64
              total_tokens: int64
              raw_train: list<item: double>
              step_train: list<item: double>
              eval: list<item: double>