The dataset viewer is not available for this subset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 80, in _split_generators
first_examples = list(islice(pipeline, self.NUM_EXAMPLES_FOR_FEATURES_INFERENCE))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 33, in _get_pipeline_from_tar
for filename, f in tar_iterator:
^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/utils/track.py", line 49, in __iter__
for x in self.generator(*self.args):
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/utils/file_utils.py", line 1380, in _iter_from_urlpath
yield from cls._iter_tar(f)
File "/usr/local/lib/python3.12/site-packages/datasets/utils/file_utils.py", line 1331, in _iter_tar
stream = tarfile.open(fileobj=f, mode="r|*")
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/tarfile.py", line 1886, in open
t = cls(name, filemode, stream, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/tarfile.py", line 1762, in __init__
self.firstmember = self.next()
^^^^^^^^^^^
File "/usr/local/lib/python3.12/tarfile.py", line 2750, in next
raise ReadError(str(e)) from None
tarfile.ReadError: invalid header
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 65, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
SO-101 SmolVLA Thesis Release Assets
This dataset repository publishes the large release assets for the SO-101 SmolVLA thesis reproduction package.
Code repository: https://github.com/Shaibk/so101-smolvla-thesis
Dataset repository: https://huggingface.co/datasets/Shaibk/so101-smolvla-thesis
Dataset data upload commit: 80b244f9744e5b439c2b9a23b525632e699c4592
Files
| File | Size | SHA-256 | Contents |
|---|---|---|---|
so101_smolvla_training_datasets_20260430.tar.zst |
317,196,134 bytes | 73c59e48a1b81923f5972faad2bf81de2f3e51d602f78a5ad040082cfcc5a8ee |
Six LeRobot training datasets, 160 successful real-robot episodes. |
so101_smolvla_systematic50_eval_yellow_20260430.tar.zst |
109,495,802 bytes | a37237850c07e1076e1f43e6d35a8047b456b351273fcdd299d592d62adb562b |
Official yellow-distractor systematic_50 evaluation records, videos, placement plans, warp metadata, and results.jsonl. |
SHA256SUMS.txt and release_manifest.json are included for verification. The two large archives are stored with Hugging Face LFS; their remote LFS SHA-256 values match the checksums above.
Verify and Extract
shasum -a 256 so101_smolvla_*.tar.zst
tar --zstd -xf so101_smolvla_training_datasets_20260430.tar.zst
tar --zstd -xf so101_smolvla_systematic50_eval_yellow_20260430.tar.zst
Reproduction Scope
The released policy and data are tied to a fixed SO-101 setup, camera view, workspace geometry, target colors, object dimensions, and training distribution. For a different robot bench, recalibrate the camera/workspace and collect local demonstrations before fine-tuning with the same training flow.
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