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crosscoder-multilayer-split-activations

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train

The dataset viewer is not available for this split.

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

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
epochs: list<item: int64>
  child 0, item: int64
train_loss: list<item: double>
  child 0, item: double
val_loss: list<item: double>
  child 0, item: double
train_fve_base: list<item: double>
  child 0, item: double
train_fve_aligned: list<item: double>
  child 0, item: double
val_fve_base: list<item: double>
  child 0, item: double
val_fve_aligned: list<item: double>
  child 0, item: double
val_fve_base_by_layer: list<item: list<item: double>>
  child 0, item: list<item: double>
      child 0, item: double
val_fve_aligned_by_layer: list<item: list<item: double>>
  child 0, item: list<item: double>
      child 0, item: double
train_fve_base_by_layer: list<item: list<item: double>>
  child 0, item: list<item: double>
      child 0, item: double
train_fve_aligned_by_layer: list<item: list<item: double>>
  child 0, item: list<item: double>
      child 0, item: double
dead_neurons: list<item: double>
  child 0, item: double
l0_base: list<item: double>
  child 0, item: double
l0_aligned: list<item: double>
  child 0, item: double
l0_base_by_layer: list<item: list<item: double>>
  child 0, item: list<item: double>
      child 0, item: double
l0_aligned_by_layer: list<item: list<item: double>>
  child 0, item: list<item: double>
      child 0, item: double
val_l0_base: list<item: double>
  child 0, item: double
val_l0_aligned: list<item: double>
  child 0, item: double
val_l0_base_by_layer: list<item: list<item: double>>
  child 0, item: list<item: double>
      child 0, item: double
val_l0_aligned_by_layer: list<item: list<item: double>>
  child 0, item: list<item: double>
      child 0, item: double
self_recon: list<item: double>
  child 0, item: double
cross_recon: list<item: double>
  child 0, item: double
sparsity: list<item: double>
  child 0, item: double
val_self_recon: list<item: double>
  child 0, item: double
val_cross_recon: list<item: double>
  child 0, item: double
val_sparsity: list<item: double>
  child 0, item: double
layers: list<item: int64>
  child 0, item: int64
topk_mode: string
to
{'epochs': List(Value('int64')), 'train_loss': List(Value('float64')), 'val_loss': List(Value('float64')), 'train_fve_base': List(Value('float64')), 'train_fve_aligned': List(Value('float64')), 'val_fve_base': List(Value('float64')), 'val_fve_aligned': List(Value('float64')), 'dead_neurons': List(Value('float64')), 'l0_base': List(Value('float64')), 'l0_aligned': List(Value('float64')), 'self_recon': List(Value('float64')), 'cross_recon': List(Value('float64')), 'sparsity': List(Value('float64'))}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
                  return get_rows(
                         ^^^^^^^^^
                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 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2690, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2227, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2251, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 494, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 384, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 295, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 128, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2321, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2249, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              epochs: list<item: int64>
                child 0, item: int64
              train_loss: list<item: double>
                child 0, item: double
              val_loss: list<item: double>
                child 0, item: double
              train_fve_base: list<item: double>
                child 0, item: double
              train_fve_aligned: list<item: double>
                child 0, item: double
              val_fve_base: list<item: double>
                child 0, item: double
              val_fve_aligned: list<item: double>
                child 0, item: double
              val_fve_base_by_layer: list<item: list<item: double>>
                child 0, item: list<item: double>
                    child 0, item: double
              val_fve_aligned_by_layer: list<item: list<item: double>>
                child 0, item: list<item: double>
                    child 0, item: double
              train_fve_base_by_layer: list<item: list<item: double>>
                child 0, item: list<item: double>
                    child 0, item: double
              train_fve_aligned_by_layer: list<item: list<item: double>>
                child 0, item: list<item: double>
                    child 0, item: double
              dead_neurons: list<item: double>
                child 0, item: double
              l0_base: list<item: double>
                child 0, item: double
              l0_aligned: list<item: double>
                child 0, item: double
              l0_base_by_layer: list<item: list<item: double>>
                child 0, item: list<item: double>
                    child 0, item: double
              l0_aligned_by_layer: list<item: list<item: double>>
                child 0, item: list<item: double>
                    child 0, item: double
              val_l0_base: list<item: double>
                child 0, item: double
              val_l0_aligned: list<item: double>
                child 0, item: double
              val_l0_base_by_layer: list<item: list<item: double>>
                child 0, item: list<item: double>
                    child 0, item: double
              val_l0_aligned_by_layer: list<item: list<item: double>>
                child 0, item: list<item: double>
                    child 0, item: double
              self_recon: list<item: double>
                child 0, item: double
              cross_recon: list<item: double>
                child 0, item: double
              sparsity: list<item: double>
                child 0, item: double
              val_self_recon: list<item: double>
                child 0, item: double
              val_cross_recon: list<item: double>
                child 0, item: double
              val_sparsity: list<item: double>
                child 0, item: double
              layers: list<item: int64>
                child 0, item: int64
              topk_mode: string
              to
              {'epochs': List(Value('int64')), 'train_loss': List(Value('float64')), 'val_loss': List(Value('float64')), 'train_fve_base': List(Value('float64')), 'train_fve_aligned': List(Value('float64')), 'val_fve_base': List(Value('float64')), 'val_fve_aligned': List(Value('float64')), 'dead_neurons': List(Value('float64')), 'l0_base': List(Value('float64')), 'l0_aligned': List(Value('float64')), 'self_recon': List(Value('float64')), 'cross_recon': List(Value('float64')), 'sparsity': List(Value('float64'))}
              because column names don't match

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Crosscoder Multilayer Split Activations

Raw split activation artifacts for multilayer SPARC-style crosscoder training.

This dataset stores reusable base-only and aligned-only activation tensors. These are intended to be assembled into matched activations.pt training artifacts before crosscoder training.

Versions

v1

Source local run: interp_utils/crosscoder/results-multi-v1

Layout:

v1/
  base_activations/
    smollm3-union/
    llama32-3b-union/
    qwen3-4b-union/
  aligned_activations/
    smollm3-{dpo,grpo,kto,orpo,ppo,simpo}/
    llama32-3b-{dpo,grpo,kto,orpo,ppo,simpo}/
    qwen3-4b-{dpo,grpo,kto,orpo,ppo,simpo}/

Each run directory contains:

run_meta.json
activations/base_activations.pt      # base-only runs
activations/aligned_activations.pt   # aligned-only runs

The base tensors contain union layer sets. The aligned tensors contain each aligned model's target probe-best layer window. Assembly slices/reorders the base union tensor to the aligned run's layers.

v1 Base Layers

smollm3-union:      [16, 17, 18, 19, 20]
llama32-3b-union:   [10, 11, 12, 13, 14, 23, 24, 25, 26]
qwen3-4b-union:     [19, 20, 21, 22, 23, 24, 25]

Use

Download one base union and one aligned run, then assemble locally with:

.venv/bin/python -m interp_utils.crosscoder.main \
  --stage assemble \
  --crosscoder-kind multilayer_sparc \
  --base-activations-dir path/to/base_union_dir \
  --aligned-activations-dir path/to/aligned_run_dir \
  --output-dir path/to/assembled_run_dir

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Total file size:

1.39 TB