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/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
~~~~~~~~~~~~~~~~~~~~~~~~~^
StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 81, in _split_generators
first_examples = list(islice(pipeline, self.NUM_EXAMPLES_FOR_FEATURES_INFERENCE))
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 32, in _get_pipeline_from_tar
fs: fsspec.AbstractFileSystem = fsspec.filesystem("memory")
~~~~~~~~~~~~~~~~~^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/fsspec/registry.py", line 302, in filesystem
cls = get_filesystem_class(protocol)
File "/usr/local/lib/python3.14/site-packages/fsspec/registry.py", line 239, in get_filesystem_class
raise ValueError(f"Protocol not known: {protocol}")
ValueError: Protocol not known: memory
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 66, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
~~~~~~~~~~~~~~~~~~~~~~~^
path=dataset,
^^^^^^^^^^^^^
config_name=config,
^^^^^^^^^^^^^^^^^^^
token=hf_token,
^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
path,
...<6 lines>...
**config_kwargs,
)
File "/usr/local/lib/python3.14/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.
Transformer Transformer — training data
Training datasets for Transformer Transformer: A Unified Model for Motion-Conditioned Robot Co-design (Huy Ha, C. Karen Liu, Shuran Song).
- 🌐 Project page
- 💻 Code: real-stanford/transformer-transformer
- 🎥 Video
Each artifact is a tarred Zarr store of oracle rollouts —
embodiment RoboTokens, states, and actions — bundled with its .zarr.idx
index (and, where precomputed, a .norm.pt normalization cache). The data
format is documented in the code repo's docs/data_generation.md; training
commands that consume these stores are in docs/training.md.
Datasets
| Artifact | Design space | Download | Unpacked | Trains |
|---|---|---|---|---|
5059e4-varviper-pop5-maxfun15-q0.0-intermediate-transformaug |
ViperX | 561 GB (12 gzipped parts) | 646 GB | hardware_gen (checkpoint hw3n9bux) |
36f120-plusplus-q0.2-10-1000 |
quadruped | 393 GB (9 parts) | ~393 GB | hardware_gen (checkpoint xrh4wk4l) |
6c5628-bimanual-dishwashing-train-10-1000-perchoice |
wheeled bimanual | 46 GB | ~46 GB | hardware_gen (checkpoint mgoc83ra) |
9012be-bimanual-dishwashing-train-10-1000-q0.05 |
wheeled bimanual | 38 GB | ~38 GB | ctrl (checkpoint z4454nxj) |
a019e7-bimanual-dishwashing-train-10-1000-q0.05 |
wheeled bimanual | 48 GB | ~48 GB | capacity ablation (52cbmwin, u5iyxc4d) |
Download & extract
Single-file sets stream straight into tar:
wget -qO- https://huggingface.co/datasets/hqhuy/transformer-transformer/resolve/main/6c5628-bimanual-dishwashing-train-10-1000-perchoice.zarr.tar | tar -xf- -C data/
The two large sets ship in 45 GB parts — download all parts, then:
# quadruped (plain tar)
cat 36f120-*.zarr.tar.part-* | tar -xf- -C data/
# ViperX (gzipped — note the z)
cat 5059e4-*.zarr.tar.gz.part-* | tar -xzf- -C data/
Or fetch everything at once with resumable parallel downloads:
pip install huggingface_hub
hf download hqhuy/transformer-transformer --repo-type dataset --local-dir training_data/
Verification
SHA256SUMS covers every artifact (paths carry a training_data/ prefix, so
run from the directory containing training_data/):
sha256sum -c --ignore-missing SHA256SUMS
Each split set also ships a .streamsha256 with the hash of the concatenated
part stream (cat part-* | sha256sum), so you can verify before extracting.
License
MIT, matching the code release.
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