Datasets:

jepacpp
/

jepa.cpp-fixtures

Error code:   StreamingRowsError
Exception:    TypeError
Message:      Couldn't cast array of type
struct<embed_dim: int64, n_layer: int64, n_head: int64, ffn_dim: int64, patch_size: int64, img_size: int64, ln_eps: double, act: string, qkv_bias: bool, cls_token: bool, n_registers: int64, layer_scale: bool, pos_type: string, pos_embed_shape: list<item: int64>, n_tokens: int64>
to
{'embed_dim': Value('int64'), 'n_layer': Value('int64'), 'n_head': Value('int64'), 'ffn_dim': Value('int64'), 'patch_size': Value('int64'), 'img_size': Value('int64'), 'ln_eps': Value('float64'), 'act': Value('string'), 'qkv_bias': Value('bool'), 'cls_token': Value('bool'), 'pos_type': Value('string'), 'n_tokens': Value('int64')}
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 2312, in cast_table_to_schema
                  cast_array_to_feature(
                  ~~~~~~~~~~~~~~~~~~~~~^
                      table[name] if name in table_column_names else pa.array([None] * len(table), type=schema.field(name).type),
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                      feature,
                      ^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1861, in wrapper
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                                           ~~~~^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2158, in cast_array_to_feature
                  raise TypeError(f"Couldn't cast array of type\n{_short_str(array.type)}\nto\n{_short_str(feature)}")
              TypeError: Couldn't cast array of type
              struct<embed_dim: int64, n_layer: int64, n_head: int64, ffn_dim: int64, patch_size: int64, img_size: int64, ln_eps: double, act: string, qkv_bias: bool, cls_token: bool, n_registers: int64, layer_scale: bool, pos_type: string, pos_embed_shape: list<item: int64>, n_tokens: int64>
              to
              {'embed_dim': Value('int64'), 'n_layer': Value('int64'), 'n_head': Value('int64'), 'ffn_dim': Value('int64'), 'patch_size': Value('int64'), 'img_size': Value('int64'), 'ln_eps': Value('float64'), 'act': Value('string'), 'qkv_bias': Value('bool'), 'cls_token': Value('bool'), 'pos_type': Value('string'), 'n_tokens': Value('int64')}

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jepa.cpp parity fixtures

PyTorch golden reference dumps for jepa.cpp, a ggml-based C/C++ inference engine for the JEPA family. tests/test-parity and tests/test-predictor replay these tensors through the engine and gate per-token cosine, pooled outputs and classifier top-1/top-5 against per-family thresholds.

Generated from jepa.cpp main @ 00bfd4e with scripts/dump_reference.py --model all, in float32 eval mode on 32 CPU threads, no autocast.

directory	model	files	size
`ref/ijepa-vith14-1k/`	I-JEPA ViT-H/14 (IN1k)	25	15 MB
`ref/lejepa-vits16/`	LeJEPA ViT-S/16 (IN1k)	41	7 MB
`ref/levjepa-vitl16/`	LeVJEPA ViT-L/16 (VideoMix)	21	136 MB
`ref/lewm-pusht/`	LeWorldModel Push-T	20	3 MB
`ref/vjepa2-ac-vitg/`	V-JEPA 2-AC ViT-g (action-conditioned world model)	45	56 MB
`ref/vjepa2-vitg-fpc64-256/`	V-JEPA 2 ViT-g/16 (fpc64, 256)	21	429 MB
`ref/vjepa2-vitl-fpc16-256-ssv2/`	V-JEPA 2 ViT-L/16 SSv2 classifier	13	56 MB
`ref/vjepa2-vitl-fpc64-256/`	V-JEPA 2 ViT-L/16 (fpc64, 256)	21	366 MB
`ref/vjepa2_1-vitb-384/`	V-JEPA 2.1 ViT-B/16 @384	15	107 MB

222 files, 1176 MB in total.

Layout

One directory per model, each with a manifest.json and one .npy per tensor per sample, named <sample>.<tensor>.npy. All arrays are float32 C-order except frames_u8 (uint8) and top5_idx (int64). Shapes carry no batch dimension except input, which is stored exactly as fed to the model.

The manifest records the model id, the hyper-parameters, the preprocessing pipeline that produced input, the PyTorch forward wall time per sample (timing_s.forward_s, the baseline for the speed tables of the jepa.cpp docs), the frame indices sampled from each clip, and the label strings for the classifier. Per-model tensor lists and the exact preprocessing recipe: docs/fixtures.md.

How jepa.cpp consumes it

git clone --recursive https://github.com/aselimc/jepa.cpp && cd jepa.cpp
cmake -S . -B build -G Ninja -DCMAKE_BUILD_TYPE=Release && cmake --build build -j

scripts/download_fixtures.sh          # this dataset -> tests/fixtures/ref, plus the media it needs
scripts/download_models.sh small      # GGUFs from https://huggingface.co/jepacpp

cmake -S . -B build && ctest --test-dir build      # the parity suites register at configure time

test-parity runs two passes per file: first the stored input tensor (bypassing preprocessing, so a graph bug shows up alone), then jepa.cpp's own preprocessor on the source media (so a preprocessing mismatch shows up separately). The second pass needs tests/fixtures/media/, which is not part of this dataset — see below.

Input provenance

The dumps are model outputs computed on two public research sets. Neither the source images nor the source videos are redistributed here; only the reference activations and the decoded frame tensors the tests replay are.

input	source	fetched by
8 images `coco_*.jpg`	COCO val2017, images subject to their original Flickr terms	tracked in the jepa.cpp git repo
6 short clips	`nateraw/kinetics-mini`, a small sample of Kinetics-400	`scripts/download_fixtures.sh`

The input and frames_u8 arrays inside ref/ are preprocessed pixels of those images and clips, kept because the parity tests must feed the network exactly the tensor PyTorch saw. If you hold rights in any of the underlying material and want it removed, open an issue on github.com/aselimc/jepa.cpp.

Licence

cc-by-nc-4.0, the most restrictive licence among the checkpoints whose outputs are stored here: the dumps of I-JEPA ViT-H/14 (IN1k) and LeVJEPA ViT-L/16 (VideoMix) are outputs of CC BY-NC 4.0 models, the rest of MIT / Apache-2.0 ones. Research and non-commercial use.

Regenerating instead of downloading

scripts/download_fixtures.sh media                        # the source media
scripts/download_models.sh --convert all                  # the source checkpoints
.venv/bin/python scripts/dump_reference.py --model all    # ~1 min on 32 cores