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train

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Cannot load the dataset split (in streaming mode) to extract the first rows.

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
capacity_per_model: struct<0p5m: struct<n_params: int64, capacity_mem_bits: double, capacity_bpp: double, at_level: stri (... 196 chars omitted)
  child 0, 0p5m: struct<n_params: int64, capacity_mem_bits: double, capacity_bpp: double, at_level: string>
      child 0, n_params: int64
      child 1, capacity_mem_bits: double
      child 2, capacity_bpp: double
      child 3, at_level: string
  child 1, 2m: struct<n_params: int64, capacity_mem_bits: double, capacity_bpp: double, at_level: string>
      child 0, n_params: int64
      child 1, capacity_mem_bits: double
      child 2, capacity_bpp: double
      child 3, at_level: string
  child 2, 8m: struct<n_params: int64, capacity_mem_bits: double, capacity_bpp: double, at_level: string>
      child 0, n_params: int64
      child 1, capacity_mem_bits: double
      child 2, capacity_bpp: double
      child 3, at_level: string
stability_medium_near: struct<n: int64, mean: double, std: double, values: list<item: double>>
  child 0, n: int64
  child 1, mean: double
  child 2, std: double
  child 3, values: list<item: double>
      child 0, item: double
precision_medium_near: struct<bf16_full: double>
  child 0, bf16_full: double
n_runs: int64
level: string
n_data_tokens: int64
dataset_bits: double
seq_len: int64
bits_per_param: double
seed: int64
warmup: int64
tag: string
max_steps: int64
mem_gap_bits: double
min_lr: double
config: string
schedule: string
lr: double
mem_bits: double
n_params_nonemb: int64
plateau_stopped: bool
precision: string
vocab_data: int64
batch: int64
L_held_bits: double
wall_seconds: double
log2V: double
L_train_bits: double
n_seq: int64
n_params: int64
to
{'tag': Value('string'), 'config': Value('string'), 'level': Value('string'), 'seed': Value('int64'), 'precision': Value('string'), 'schedule': Value('string'), 'lr': Value('float64'), 'min_lr': Value('float64'), 'warmup': Value('int64'), 'n_params': Value('int64'), 'n_params_nonemb': Value('int64'), 'n_seq': Value('int64'), 'seq_len': Value('int64'), 'vocab_data': Value('int64'), 'n_data_tokens': Value('int64'), 'dataset_bits': Value('float64'), 'L_train_bits': Value('float64'), 'L_held_bits': Value('float64'), 'log2V': Value('float64'), 'mem_bits': Value('float64'), 'mem_gap_bits': Value('float64'), 'bits_per_param': Value('float64'), 'max_steps': Value('int64'), 'plateau_stopped': Value('bool'), 'wall_seconds': Value('float64'), 'batch': Value('int64')}
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
              capacity_per_model: struct<0p5m: struct<n_params: int64, capacity_mem_bits: double, capacity_bpp: double, at_level: stri (... 196 chars omitted)
                child 0, 0p5m: struct<n_params: int64, capacity_mem_bits: double, capacity_bpp: double, at_level: string>
                    child 0, n_params: int64
                    child 1, capacity_mem_bits: double
                    child 2, capacity_bpp: double
                    child 3, at_level: string
                child 1, 2m: struct<n_params: int64, capacity_mem_bits: double, capacity_bpp: double, at_level: string>
                    child 0, n_params: int64
                    child 1, capacity_mem_bits: double
                    child 2, capacity_bpp: double
                    child 3, at_level: string
                child 2, 8m: struct<n_params: int64, capacity_mem_bits: double, capacity_bpp: double, at_level: string>
                    child 0, n_params: int64
                    child 1, capacity_mem_bits: double
                    child 2, capacity_bpp: double
                    child 3, at_level: string
              stability_medium_near: struct<n: int64, mean: double, std: double, values: list<item: double>>
                child 0, n: int64
                child 1, mean: double
                child 2, std: double
                child 3, values: list<item: double>
                    child 0, item: double
              precision_medium_near: struct<bf16_full: double>
                child 0, bf16_full: double
              n_runs: int64
              level: string
              n_data_tokens: int64
              dataset_bits: double
              seq_len: int64
              bits_per_param: double
              seed: int64
              warmup: int64
              tag: string
              max_steps: int64
              mem_gap_bits: double
              min_lr: double
              config: string
              schedule: string
              lr: double
              mem_bits: double
              n_params_nonemb: int64
              plateau_stopped: bool
              precision: string
              vocab_data: int64
              batch: int64
              L_held_bits: double
              wall_seconds: double
              log2V: double
              L_train_bits: double
              n_seq: int64
              n_params: int64
              to
              {'tag': Value('string'), 'config': Value('string'), 'level': Value('string'), 'seed': Value('int64'), 'precision': Value('string'), 'schedule': Value('string'), 'lr': Value('float64'), 'min_lr': Value('float64'), 'warmup': Value('int64'), 'n_params': Value('int64'), 'n_params_nonemb': Value('int64'), 'n_seq': Value('int64'), 'seq_len': Value('int64'), 'vocab_data': Value('int64'), 'n_data_tokens': Value('int64'), 'dataset_bits': Value('float64'), 'L_train_bits': Value('float64'), 'L_held_bits': Value('float64'), 'log2V': Value('float64'), 'mem_bits': Value('float64'), 'mem_gap_bits': Value('float64'), 'bits_per_param': Value('float64'), 'max_steps': Value('int64'), 'plateau_stopped': Value('bool'), 'wall_seconds': Value('float64'), 'batch': Value('int64')}
              because column names don't match

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Check out the documentation for more information.

Tiny memorization experiment results

Per-run learning curves (loss_curve.csv: step, epoch, lr, train_loss_bits, held_loss_bits), final metrics (metrics.json including memorized bits, bits/parameter, L_train, L_held), and raw state.pt checkpoints for every run. Aggregated summary.csv/summary.json and figures (capacity_plot.png, loss_curves.png) are at the repo root.

Memorization metric (paper Sec 3.2): mem = N_data_tokens * (log2 V - L_train_bits); bits_per_param = mem / n_params. Capacity ~ max memorization over dataset sizes.

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