Datasets:
The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
reference: string
n_random: int64
plan: struct<n_constructs: int64, references: list<item: string>, n_random_draws: int64, per_reference: li (... 214 chars omitted)
child 0, n_constructs: int64
child 1, references: list<item: string>
child 0, item: string
child 2, n_random_draws: int64
child 3, per_reference: list<item: struct<reference: string, sets_per_construct: int64, construct_passes: int64, shared_pass (... 11 chars omitted)
child 0, item: struct<reference: string, sets_per_construct: int64, construct_passes: int64, shared_passes: int64>
child 0, reference: string
child 1, sets_per_construct: int64
child 2, construct_passes: int64
child 3, shared_passes: int64
child 4, total_construct_passes: int64
child 5, total_shared_passes: int64
child 6, n_checkpoint_downloads: int64
child 7, note: string
constructs: struct<AsyncFor: struct<baseline_nats: double, n_prompts: int64, n_positions: int64, positions_deriv (... 9579 chars omitted)
child 0, AsyncFor: struct<baseline_nats: double, n_prompts: int64, n_positions: int64, positions_derived: int64, drift_ (... 494 chars omitted)
child 0, baseline_nats: double
child 1, n_prompts: int64
child 2, n_positions: int64
child 3, positions_derived: int64
child 4, drift_unavailable: string
child 5, current: double
child 6, random_mean: double
child 7, random_sd: double
child 8, control_stability: struct<50: struct<n_
...
ositions: int64
child 3, positions_derived: int64
child 4, drift_unavailable: string
child 5, current: double
child 6, random_mean: double
child 7, random_sd: double
child 8, control_stability: struct<50: struct<n_masks: int64, control_mean: double, control_sd: double, change_from_half: null>, (... 194 chars omitted)
child 0, 50: struct<n_masks: int64, control_mean: double, control_sd: double, change_from_half: null>
child 0, n_masks: int64
child 1, control_mean: double
child 2, control_sd: double
child 3, change_from_half: null
child 1, 100: struct<n_masks: int64, control_mean: double, control_sd: double, change_from_half: double>
child 0, n_masks: int64
child 1, control_mean: double
child 2, control_sd: double
child 3, change_from_half: double
child 2, 200: struct<n_masks: int64, control_mean: double, control_sd: double, change_from_half: double>
child 0, n_masks: int64
child 1, control_mean: double
child 2, control_sd: double
child 3, change_from_half: double
child 9, control_sd_settled: bool
child 10, n_masks_reached: int64
child 11, current_over_random_in_sd: double
child 12, seconds: double
seconds: double
no_scope_cores: bool
only: string
host: string
host_kind: string
jobs: int64
when: timestamp[s]
cpu_count: int64
to
{'reference': Value('string'), 'only': Value('string'), 'seconds': Value('float64'), 'host': Value('string'), 'host_kind': Value('string'), 'when': Value('timestamp[s]'), 'no_scope_cores': Value('bool'), 'cpu_count': Value('int64'), 'jobs': Value('int64')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 149, 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 129, 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 544, 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 3052, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2559, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2584, 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 558, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 440, 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 361, 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 2381, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2309, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
reference: string
n_random: int64
plan: struct<n_constructs: int64, references: list<item: string>, n_random_draws: int64, per_reference: li (... 214 chars omitted)
child 0, n_constructs: int64
child 1, references: list<item: string>
child 0, item: string
child 2, n_random_draws: int64
child 3, per_reference: list<item: struct<reference: string, sets_per_construct: int64, construct_passes: int64, shared_pass (... 11 chars omitted)
child 0, item: struct<reference: string, sets_per_construct: int64, construct_passes: int64, shared_passes: int64>
child 0, reference: string
child 1, sets_per_construct: int64
child 2, construct_passes: int64
child 3, shared_passes: int64
child 4, total_construct_passes: int64
child 5, total_shared_passes: int64
child 6, n_checkpoint_downloads: int64
child 7, note: string
constructs: struct<AsyncFor: struct<baseline_nats: double, n_prompts: int64, n_positions: int64, positions_deriv (... 9579 chars omitted)
child 0, AsyncFor: struct<baseline_nats: double, n_prompts: int64, n_positions: int64, positions_derived: int64, drift_ (... 494 chars omitted)
child 0, baseline_nats: double
child 1, n_prompts: int64
child 2, n_positions: int64
child 3, positions_derived: int64
child 4, drift_unavailable: string
child 5, current: double
child 6, random_mean: double
child 7, random_sd: double
child 8, control_stability: struct<50: struct<n_
...
ositions: int64
child 3, positions_derived: int64
child 4, drift_unavailable: string
child 5, current: double
child 6, random_mean: double
child 7, random_sd: double
child 8, control_stability: struct<50: struct<n_masks: int64, control_mean: double, control_sd: double, change_from_half: null>, (... 194 chars omitted)
child 0, 50: struct<n_masks: int64, control_mean: double, control_sd: double, change_from_half: null>
child 0, n_masks: int64
child 1, control_mean: double
child 2, control_sd: double
child 3, change_from_half: null
child 1, 100: struct<n_masks: int64, control_mean: double, control_sd: double, change_from_half: double>
child 0, n_masks: int64
child 1, control_mean: double
child 2, control_sd: double
child 3, change_from_half: double
child 2, 200: struct<n_masks: int64, control_mean: double, control_sd: double, change_from_half: double>
child 0, n_masks: int64
child 1, control_mean: double
child 2, control_sd: double
child 3, change_from_half: double
child 9, control_sd_settled: bool
child 10, n_masks_reached: int64
child 11, current_over_random_in_sd: double
child 12, seconds: double
seconds: double
no_scope_cores: bool
only: string
host: string
host_kind: string
jobs: int64
when: timestamp[s]
cpu_count: int64
to
{'reference': Value('string'), 'only': Value('string'), 'seconds': Value('float64'), 'host': Value('string'), 'host_kind': Value('string'), 'when': Value('timestamp[s]'), 'no_scope_cores': Value('bool'), 'cpu_count': Value('int64'), 'jobs': Value('int64')}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
AtlasOpenCoder — v1.0
Calibrated concept profiles for OpenCoder-1.5B-Base, read on 169 Python constructs across 61 pretraining and annealing checkpoints. This dataset is the battery, the licence tables and the artefacts every figure and table of two papers reads:
- "Calibrated Concept Profiles: Making the Reading of a Model's Neurons a Measurement"
(Wilam, 2026) — the method. Its code:
piotrwilam/AtlasCalibratedConceptProfiles. - "Representational Drift in a Code Model: Behaviour Settles, Which Neurons Carry It Does
Not" (Wilam, 2026) — the finding, read with that instrument on this dataset.
Its code:
piotrwilam/AtlasRepresentationalDrift.
MANIFEST_PAPER1.md and MANIFEST_PAPER2.md below are named for the first and the second of
those, in that order.
What is here
phase_battery/ |
the battery: 557,990 prompts in sets A, B and C, two twins and the nu arm |
battery/ |
the earlier pilot battery |
validation/ |
every artefact a figure or table in either paper reads |
inventory/, calibration/ |
the 206 constructs and the corpus rate tables |
probe/companions.json |
the logged companion shares |
MANIFEST_PAPER1.md, MANIFEST_PAPER2.md |
every number in each paper, to its file, field and script |
The battery cannot be regenerated. Its per-prompt seeds came from abs(hash(...)), salted
per process, so the seeds recorded in CONTENT_HASHES.json do not reproduce the prompts they
name. That is why it is published.
What is not here
The raw record (2,022 GB over 61 references) — per-core accumulators and per-prompt activations. It is regenerable from OpenCoder's released checkpoints, this battery and the code. One reference of it is planned for the next version.
The pilot recording. Two artefacts were once computed from a pilot tree and later found to differ from the phase recording at every reference; everything derived from it is excluded and named in the papers.
Two batteries, two hashes
The recorded battery — the one the model was run on, and the one the papers' numbers come
from — carries prompt-content hash 570b42db…, and its per-set hashes are the figures the
papers cite.
The released battery differs from it in 1,927 of 557,990 prompts (0.35%): those prompts
drew padding containing one of six third-party email addresses, which are replaced here with
user@example.com. The token offsets each prompt stores are recomputed to match, and the
recomputation is verified per prompt. See phase_battery/REDACTION.json and
phase_battery/RELEASED_HASHES.json.
Only a reader who re-runs the model on those 1,927 prompts sees a difference. Every artefact in
validation/ was computed on the recorded battery.
Licence
The dataset's own content (generator text, profiles, tables, artefacts) is CC BY 4.0 (LICENSE); the padding snippets in the battery remain under their original licences, listed in ATTRIBUTION.md.
The padding is not ours. Padding blocks were drawn from codeparrot/codeparrot-clean and
carry the licences of the repositories they came from: MIT, Apache-2.0, BSD, GPL, AGPL, LGPL
and others. Roughly 29% of prompts contain at least one such fragment, each one to three
lines and a median of 45 characters. Every block, its source repositories and their licences
are listed in ATTRIBUTION.md.
Citing
Cite the paper that owns this dataset, and the DOI together with the tag — the method paper, named above. The dataset is its instrument and its realisation on OpenCoder; the second paper reads it.
Wilam, P. (2026). Calibrated Concept Profiles: Making the Reading of a Model's Neurons a Measurement. Dataset: AtlasOpenCoder, tag
v1.
The DOI is shown on this page, above the file list, and belongs in the citation beside the tag. It is not repeated here: a DOI names the revision it was minted on, so a card cannot carry its own without pointing at a later revision than the one the DOI names. The paper's arXiv identifier is in the paper's own listing.
A Hugging Face DOI is assigned to the revision it is generated on, so a later version takes a new DOI and the earlier one is marked outdated. Name the tag as well as the DOI: the tag says which content a number came from in words a reader can check against this repository's history. Version 1 is the 20-cell yardstick (4 token classes x 5 fifths of the prompt). A later version computed under a different yardstick names different neurons for the same constructs, so a figure from one version cannot be compared with a figure from another.
What the directory names mean
validation/paper1/ and validation/paper2/ record which paper's script wrote a file, not
which paper reads it. Both papers read across them: the first paper's numbers come from 31
files under validation/paper2/ and validation/r2/, and the second paper's from 14 under
validation/paper1/. MANIFEST_PAPER1.md and MANIFEST_PAPER2.md in this repository give, for every number
printed in each paper, the file and the field it is read from and the script that
rebuilds that file -- 407 numbers and 495, including the few that no released file holds,
which are marked and collected in a section of their own.
Provenance
The model is OpenCoder-LLM/OpenCoder-1.5B-Base-Checkpoints (MIT). The reference corpus is
codeparrot/codeparrot-clean. The yardstick is the 20-cell split (4 token classes × 5 fifths
of the prompt), version 1.
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