The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
schema_version: int64
repo_id: string
parts: list<item: struct<path: string, sha256: string, bytes: int64, member_manifest: string, member_count: (... 29 chars omitted)
child 0, item: struct<path: string, sha256: string, bytes: int64, member_manifest: string, member_count: int64, mem (... 17 chars omitted)
child 0, path: string
child 1, sha256: string
child 2, bytes: int64
child 3, member_manifest: string
child 4, member_count: int64
child 5, member_bytes: int64
packed_artifact_original_bytes: int64
packed_artifact_compressed_bytes: int64
archived_source_files: int64
revision: string
checkpoint_archive_bytes: int64
all_uploaded_objects_independently_downloaded: bool
weight_object_bytes: int64
unique_weight_objects: int64
weight_source_files: int64
packed_members_independently_restored: bool
archived_original_bytes: int64
packed_artifact_parts: int64
image_privacy_method: string
large_original_archives: int64
verified_unique_payload_bytes: int64
checkpoint_archives: int64
large_original_archive_bytes: int64
packed_artifact_files: int64
to
{'schema_version': Value('int64'), 'repo_id': Value('string'), 'revision': Value('string'), 'archived_source_files': Value('int64'), 'archived_original_bytes': Value('int64'), 'checkpoint_archives': Value('int64'), 'checkpoint_archive_bytes': Value('int64'), 'weight_source_files': Value('int64'), 'unique_weight_objects': Value('int64'), 'weight_object_bytes': Value('int64'), 'packed_artifact_files': Value('int64'), 'packed_artifact_original_bytes': Value('int64'), 'packed_artifact_parts': Value('int64'), 'packed_artifact_compressed_bytes': Value('int64'), 'large_original_archives': Value('int64'), 'large_original_archive_bytes': Value('int64'), 'verified_unique_payload_bytes': Value('int64'), 'all_uploaded_objects_independently_downloaded': Value('bool'), 'packed_members_independently_restored': Value('bool'), 'image_privacy_method': Value('string')}
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
schema_version: int64
repo_id: string
parts: list<item: struct<path: string, sha256: string, bytes: int64, member_manifest: string, member_count: (... 29 chars omitted)
child 0, item: struct<path: string, sha256: string, bytes: int64, member_manifest: string, member_count: int64, mem (... 17 chars omitted)
child 0, path: string
child 1, sha256: string
child 2, bytes: int64
child 3, member_manifest: string
child 4, member_count: int64
child 5, member_bytes: int64
packed_artifact_original_bytes: int64
packed_artifact_compressed_bytes: int64
archived_source_files: int64
revision: string
checkpoint_archive_bytes: int64
all_uploaded_objects_independently_downloaded: bool
weight_object_bytes: int64
unique_weight_objects: int64
weight_source_files: int64
packed_members_independently_restored: bool
archived_original_bytes: int64
packed_artifact_parts: int64
image_privacy_method: string
large_original_archives: int64
verified_unique_payload_bytes: int64
checkpoint_archives: int64
large_original_archive_bytes: int64
packed_artifact_files: int64
to
{'schema_version': Value('int64'), 'repo_id': Value('string'), 'revision': Value('string'), 'archived_source_files': Value('int64'), 'archived_original_bytes': Value('int64'), 'checkpoint_archives': Value('int64'), 'checkpoint_archive_bytes': Value('int64'), 'weight_source_files': Value('int64'), 'unique_weight_objects': Value('int64'), 'weight_object_bytes': Value('int64'), 'packed_artifact_files': Value('int64'), 'packed_artifact_original_bytes': Value('int64'), 'packed_artifact_parts': Value('int64'), 'packed_artifact_compressed_bytes': Value('int64'), 'large_original_archives': Value('int64'), 'large_original_archive_bytes': Value('int64'), 'verified_unique_payload_bytes': Value('int64'), 'all_uploaded_objects_independently_downloaded': Value('bool'), 'packed_members_independently_restored': Value('bool'), 'image_privacy_method': Value('string')}
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.
Image diffusion arithmetic research artifacts
Preservation archive for experiments on arithmetic with pretrained image diffusion models and comparison models. It includes unsuccessful experiments, controls, intermediate checkpoints, synthetic images, and evaluation records. These artifacts do not establish a pretraining advantage or general mathematical reasoning ability.
Contents and recovery
checkpoint-manifest.json maps original project paths to gzip-compressed PyTorch checkpoint files under checkpoints/. Verify the compressed file's byte length and SHA-256 hash, then decompress it to recover the saved state.
weights-manifest.json maps original paths to individual PyTorch or safetensors files under weights/sha256/. Identical weight files share one stored object. These are experiment states, not standalone model pipelines.
original-archives-manifest.json maps larger original experiment archives to objects under original-archives/sha256/. The original archive bytes are preserved.
artifact-manifest.json lists compressed parts under artifacts/. Each part has a companion JSON file under artifact-manifests/ listing its member paths, byte lengths, SHA-256 hashes, and executable modes. Find the original project-relative path in these manifests, download the corresponding part, verify the archive hash, and extract the desired member. The part manifests contain no local absolute paths.
Use an immutable repository revision when downloading files. The preservation records accompanying local removals record the exact revision used for verification. archive-summary.json records the completed release's counts.
Verification and exclusions
Every released object was downloaded anonymously from its immutable Hub revision into a new local file and checked against the original hash. Every member of a newly packed artifact archive was additionally restored to a real file and checked for path, size, hash, and mode parity. Local originals were eligible for removal only after these checks passed.
Selected text and serialized metadata were inspected for personal information and credentials. A separate secret scanner checked the selected text. Checkpoint pickle metadata was inspected without executing pickle payloads. Every selected image underwent format and metadata inspection; representative images from 232 experiment groups were visually reviewed as synthetic arithmetic, glyph, or rule-task outputs. This is not a claim that every raster received OCR or an individual visual review.
Files identified as credentials, Codex transcripts, personal records, unresolved privacy cases, or unclassified binary formats were excluded. Current experiment dependencies, Git-tracked files, working source, and text records remain local even when archived here. This release covers a frozen selection of project files; it is not a live mirror of ongoing experiments.
Weights and other material derived from upstream projects remain subject to their upstream and file-specific terms. This archive grants no new blanket license for third-party material. Only load serialized models from sources you trust.
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