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Datasets:
mmtf
/
probes-activations

Tasks:
Text Classification
Languages:
code
Tags:
mechanistic-interpretability
probing
activations
code
vulnerability-detection
License:
Dataset card Files
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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
model: string
select_metric: string
n_layers: int64
layers: list<item: int64>
  child 0, item: int64
scores: struct<25: double, 41: double, 40: double, 39: double, 45: double, 36: double, 44: double, 43: doubl (... 26 chars omitted)
  child 0, 25: double
  child 1, 41: double
  child 2, 40: double
  child 3, 39: double
  child 4, 45: double
  child 5, 36: double
  child 6, 44: double
  child 7, 43: double
  child 8, 42: double
  child 9, 37: double
pos_tokens: int64
n_rows: int64
hidden: int64
n_tokens: int64
max_length: int64
to
{'model': Value('string'), 'n_layers': Value('int64'), 'hidden': Value('int64'), 'n_tokens': Value('int64'), 'n_rows': Value('int64'), 'max_length': Value('int64'), 'pos_tokens': Value('int64')}
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 299, 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
              model: string
              select_metric: string
              n_layers: int64
              layers: list<item: int64>
                child 0, item: int64
              scores: struct<25: double, 41: double, 40: double, 39: double, 45: double, 36: double, 44: double, 43: doubl (... 26 chars omitted)
                child 0, 25: double
                child 1, 41: double
                child 2, 40: double
                child 3, 39: double
                child 4, 45: double
                child 5, 36: double
                child 6, 44: double
                child 7, 43: double
                child 8, 42: double
                child 9, 37: double
              pos_tokens: int64
              n_rows: int64
              hidden: int64
              n_tokens: int64
              max_length: int64
              to
              {'model': Value('string'), 'n_layers': Value('int64'), 'hidden': Value('int64'), 'n_tokens': Value('int64'), 'n_rows': Value('int64'), 'max_length': Value('int64'), 'pos_tokens': Value('int64')}
              because column names don't match

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probes-activations

Token-level hidden-state activations (bfloat16) for the top-10 layers per model (ranked by validation token-level code-masked AUC from a full layer sweep), extracted over the SVEN cyber-vulnerability dataset (1,430 examples). Built for linear-probe / natural-language-activation (NLA) research.

Activations are stored per model, per layer so a single layer can be pulled on its own (e.g. on Colab) without regenerating from the base model:

from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
import numpy as np

m = "google_gemma-3-27b-it"
f = hf_hub_download("mmtf/probes-activations", f"{m}/layer_19.safetensors", repo_type="dataset")
acts = load_file(f)["activations"]            # bf16 tensor, shape (T_tokens, hidden)
y    = np.load(hf_hub_download("mmtf/probes-activations", f"{m}/y.npy", repo_type="dataset"))           # int8 per-token label
eids = np.load(hf_hub_download("mmtf/probes-activations", f"{m}/example_ids.npy", repo_type="dataset")) # int32 example id per token

Layout

Per model directory <org>_<model>/:

file dtype shape meaning
layer_NN.safetensors bf16 (T_tokens, hidden) hidden state at layer NN; tensor key activations
y.npy int8 (T_tokens,) per-token positive-span (vulnerable) label
example_ids.npy int32 (T_tokens,) example index each token belongs to
offsets.npz int32 per-row (T_row, 2) char-span offsets per example (extractor parity)
meta.json — — model, n_layers, hidden, n_tokens, n_rows, max_length, pos_tokens
top_layers.json — — selected layers + their sweep scores

Shared, at the repo root:

  • data/dataset.jsonl — SVEN examples (code + vulnerability spans + labels)
  • data/sven_split_meta.json — train/val/test split, defined by example (map tokens→examples via example_ids)

Models & selected layers

model dir layers hidden tokens best L best AUC top-10 layers
Qwen/Qwen2.5-Coder-32B-Instruct Qwen_Qwen2.5-Coder-32B-Instruct 64 5120 561,266 25 0.7848 25 36 37 39 40 41 42 43 44 45
Qwen/Qwen3-32B Qwen_Qwen3-32B 64 5120 561,266 27 0.782 17 23 24 25 26 27 28 29 30 42
Qwen/Qwen3.6-27B Qwen_Qwen3.6-27B 64 5120 612,249 30 0.7717 14 16 17 18 19 30 31 32 46 49
google/gemma-3-12b-it google_gemma-3-12b-it 48 3840 690,148 15 0.7546 09 10 11 12 13 14 15 19 24 28
google/gemma-3-12b-pt google_gemma-3-12b-pt 48 3840 690,148 13 0.7614 10 11 13 14 15 16 18 22 26 47
google/gemma-3-1b-it google_gemma-3-1b-it 26 1152 690,148 25 0.7372 03 04 06 07 09 10 13 14 23 25
google/gemma-3-1b-pt google_gemma-3-1b-pt 26 1152 690,148 12 0.7546 04 05 07 09 11 12 20 22 24 25
google/gemma-3-27b-it google_gemma-3-27b-it 62 5376 690,148 19 0.7661 16 17 18 19 20 21 22 23 25 26
google/gemma-3-4b-it google_gemma-3-4b-it 34 2560 690,148 7 0.753 03 04 07 08 09 10 12 23 30 33
google/gemma-3-4b-pt google_gemma-3-4b-pt 34 2560 690,148 33 0.7682 03 04 06 07 08 09 10 13 14 33

How these were produced

  • One forward pass per example (full sequence, max_length=2048), all hidden states captured, on an NVIDIA GH200.
  • Stored bfloat16, not float16: Gemma-3 has mid-layer "massive activations" (>65504) that overflow fp16 → NaNs; bf16 keeps fp32's exponent range. (Originals were fp32; bf16 halves size with no overflow.)
  • The 10 layers per model are the highest-scoring by val_tokens_code_auc in the per-model layer sweep.

Provenance & licensing

Derived from the SVEN dataset; base models are Gemma-3 (governed by Google's Gemma terms) and Qwen (governed by the respective Qwen licenses). These activations are derived representations — downstream use is governed by those upstream dataset/model licenses. license: other reflects that; consult SVEN and the base-model licenses before redistribution or commercial use.
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