Datasets:

---

silyan

/

HessGpt

Like 0

Error code:   FeaturesError
Exception:    ArrowInvalid
Message:      Schema at index 2 was different: 
global_step: int64
chunk_start_step: int64
current_epoch: int64
chunk_within_epoch: int64
total_training_time: double
last_save: string
config: struct<vocab_size: int64, embed_dim: int64, num_heads: int64, num_layers: int64, max_seq_len: int64, dropout: double, use_rope: bool, use_yarn: bool, yarn_scale: double, yarn_original_max_len: int64, use_swiglu: bool, n_kv_heads: int64, use_qk_norm: bool, soft_cap: double, use_flash_attn: bool, batch_size: int64, gradient_accumulation: int64, max_grad_norm: double, learning_rate: double, weight_decay: double, adam_beta1: double, adam_beta2: double, adam_eps: double, num_epochs: int64, chunks_per_epoch: int64, data_dir: string, val_tokens: int64, warmup_ratio: double, decay_ratio: double, min_lr_ratio: double, validate_every_steps: int64, val_batches: int64, shuffle_seed: int64, save_every_steps: int64, checkpoint_file: string, use_compile: bool, compile_mode: string, num_workers: int64>
training_history: struct<chunks: list<item: null>, validations: list<item: struct<step: int64, current_epoch: int64, chunk_within_epoch: int64, chunk_id: int64, val_loss: double, val_ppl: double, train_loss: double, lr: double>>, epochs: list<item: null>>
vs
chunk_id: int64
datasets: struct<finemath_4plus: struct<tokens: int64, tokens_target: int64, tokens_deviation: int64, docs: int64, docs_total: int64, pass_rate: double, size_mb: double>, finewiki: struct<tokens: int64, tokens_target: int64, tokens_deviation: int64, docs: int64, docs_total: int64, pass_rate: double, size_mb: double>, pes2o: struct<tokens: int64, tokens_target: int64, tokens_deviation: int64, docs: int64, docs_total: int64, pass_rate: double, size_mb: double>, cosmopedia_v2: struct<tokens: int64, tokens_target: int64, tokens_deviation: int64, docs: int64, docs_total: int64, pass_rate: double, size_mb: double>, zyda2: struct<tokens: int64, tokens_target: int64, tokens_deviation: int64, docs: int64, docs_total: int64, pass_rate: double, size_mb: double>, fineweb_edu: struct<tokens: int64, tokens_target: int64, tokens_deviation: int64, docs: int64, docs_total: int64, pass_rate: double, size_mb: double>, dclm_baseline: struct<tokens: int64, tokens_target: int64, tokens_deviation: int64, docs: int64, docs_total: int64, pass_rate: double, size_mb: double>, beyond_web: struct<tokens: int64, size_mb: double, sources: list<item: string>>, laws_extension: struct<tokens: int64, size_mb: double, sources: list<item: string>>>
total_tokens: int64
total_docs: int64
total_size_mb: double
timestamp: double
beyond_web_offsets: struct<books_high_quality: int64, american_stories: int64, freelaw: int64, pubmed_pmc: int64, undl: int64>
laws_extension_offsets: struct<books_high_quality: int64, american_stories: int64, freelaw: int64, pubmed_pmc: int64, undl: int64>
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 243, 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 3608, 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 2368, in _head
                  return next(iter(self.iter(batch_size=n)))
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2573, in iter
                  for key, example in iterator:
                                      ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2060, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2082, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 572, 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 2 was different: 
              global_step: int64
              chunk_start_step: int64
              current_epoch: int64
              chunk_within_epoch: int64
              total_training_time: double
              last_save: string
              config: struct<vocab_size: int64, embed_dim: int64, num_heads: int64, num_layers: int64, max_seq_len: int64, dropout: double, use_rope: bool, use_yarn: bool, yarn_scale: double, yarn_original_max_len: int64, use_swiglu: bool, n_kv_heads: int64, use_qk_norm: bool, soft_cap: double, use_flash_attn: bool, batch_size: int64, gradient_accumulation: int64, max_grad_norm: double, learning_rate: double, weight_decay: double, adam_beta1: double, adam_beta2: double, adam_eps: double, num_epochs: int64, chunks_per_epoch: int64, data_dir: string, val_tokens: int64, warmup_ratio: double, decay_ratio: double, min_lr_ratio: double, validate_every_steps: int64, val_batches: int64, shuffle_seed: int64, save_every_steps: int64, checkpoint_file: string, use_compile: bool, compile_mode: string, num_workers: int64>
              training_history: struct<chunks: list<item: null>, validations: list<item: struct<step: int64, current_epoch: int64, chunk_within_epoch: int64, chunk_id: int64, val_loss: double, val_ppl: double, train_loss: double, lr: double>>, epochs: list<item: null>>
              vs
              chunk_id: int64
              datasets: struct<finemath_4plus: struct<tokens: int64, tokens_target: int64, tokens_deviation: int64, docs: int64, docs_total: int64, pass_rate: double, size_mb: double>, finewiki: struct<tokens: int64, tokens_target: int64, tokens_deviation: int64, docs: int64, docs_total: int64, pass_rate: double, size_mb: double>, pes2o: struct<tokens: int64, tokens_target: int64, tokens_deviation: int64, docs: int64, docs_total: int64, pass_rate: double, size_mb: double>, cosmopedia_v2: struct<tokens: int64, tokens_target: int64, tokens_deviation: int64, docs: int64, docs_total: int64, pass_rate: double, size_mb: double>, zyda2: struct<tokens: int64, tokens_target: int64, tokens_deviation: int64, docs: int64, docs_total: int64, pass_rate: double, size_mb: double>, fineweb_edu: struct<tokens: int64, tokens_target: int64, tokens_deviation: int64, docs: int64, docs_total: int64, pass_rate: double, size_mb: double>, dclm_baseline: struct<tokens: int64, tokens_target: int64, tokens_deviation: int64, docs: int64, docs_total: int64, pass_rate: double, size_mb: double>, beyond_web: struct<tokens: int64, size_mb: double, sources: list<item: string>>, laws_extension: struct<tokens: int64, size_mb: double, sources: list<item: string>>>
              total_tokens: int64
              total_docs: int64
              total_size_mb: double
              timestamp: double
              beyond_web_offsets: struct<books_high_quality: int64, american_stories: int64, freelaw: int64, pubmed_pmc: int64, undl: int64>
              laws_extension_offsets: struct<books_high_quality: int64, american_stories: int64, freelaw: int64, pubmed_pmc: int64, undl: int64>