Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
subject: string
documents: int64
to
{'kind': Value('string'), 'subject': Value('string'), 'reference': Value('string'), 'sha256': Value('string'), 'architecture': Value('string'), 'model_id': Value('string'), 'revision': Value('string'), 'status': Value('string'), 'source': Value('string'), 'location': Value('string'), 'rank': Value('int32'), 'tags': List(Value('string'))}
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/parquet/parquet.py", line 220, in _generate_tables
yield Key(file_idx, batch_idx), self._cast_table(pa_table)
~~~~~~~~~~~~~~~~^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/parquet/parquet.py", line 156, 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
subject: string
documents: int64
to
{'kind': Value('string'), 'subject': Value('string'), 'reference': Value('string'), 'sha256': Value('string'), 'architecture': Value('string'), 'model_id': Value('string'), 'revision': Value('string'), 'status': Value('string'), 'source': Value('string'), 'location': Value('string'), 'rank': Value('int32'), 'tags': List(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.
venngram registry
The seed registry for venngram: component maps for common architectures, their verification records, and the registry index venngram looks maps up in by default.
maps/andrecords/: the seed component maps (GPT-2, Llama, Qwen3, Qwen3.5, OPT, Gemma 2, Gemma 3, Gemma 3 multimodal) and a verification record for each, run against a real checkpoint's configuration (no weights).overlays/: overlay maps, what an adapter or instrument library adds to a module tree and the roles its tensors take, and when they apply: one per PEFT tuner family (peft.lora,peft.ia3,peft.loha,peft.lokr,peft.oft,peft.boft,peft.vera,peft.fourierft), each tensor asadapter:<name>(overlay schema 5).venngram.view(model)composes a model's base map with every overlay that applies. One manifest (venngram.yaml) lists maps, overlays and records.index/: the registry index.index.yamlnames the current generation; each generation is a parquet dataset partitioned by document kind and subject (kind=component_map/subject=LlamaForCausalLM/rows.parquet), one row per document, each naming its document by an exact reference (commit and sha256).
Federated. The index is rebuilt by a crawler from the seed manifest and from every Hub repository tagged
venngram, so a map published anywhere (written by hand, drafted from a module tree, derived from another library) is
indexed and found by venngram.maps.component_map_for like a seed. Lookup serves the best verified map for an
architecture; venngram.registry.rows lists every candidate.
from venngram.registry import rows
rows("component_map", "Qwen3ForCausalLM") # every indexed map for the architecture, best first
Experimental, like venngram itself.
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