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Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: TypeError
Message: Couldn't cast array of type
struct<llama_qwen15moe: struct<levels_tested: list<item: int64>, ceiling_p05: int64, ceiling_median: int64, ceiling_min: int64, p05_by_level: struct<1: double, 2: double, 4: double, 8: double, 12: double, 16: double>>, pinned_qwen15moe: struct<levels_tested: list<item: int64>, ceiling_p05: int64, ceiling_median: int64, ceiling_min: int64, p05_by_level: struct<1: double, 2: double, 4: double, 8: double, 12: double, 16: double>>>
to
{'llama_xover': {'levels_tested': List(Value('int64')), 'ceiling_p05': Value('int64'), 'ceiling_median': Value('int64'), 'ceiling_min': Value('int64'), 'p05_by_level': {'1': Value('float64'), '2': Value('float64'), '4': Value('float64'), '8': Value('float64'), '12': Value('float64'), '16': Value('float64'), '24': Value('float64'), '32': Value('float64')}}, 'pinned_xover': {'levels_tested': List(Value('int64')), 'ceiling_p05': Value('int64'), 'ceiling_median': Value('int64'), 'ceiling_min': Value('int64'), 'p05_by_level': {'1': Value('float64'), '2': Value('float64'), '4': Value('float64'), '8': Value('float64'), '12': Value('float64'), '16': Value('float64'), '24': Value('float64'), '32': Value('float64')}}}
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 2951, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2461, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2486, 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 547, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 430, 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<llama_qwen15moe: struct<levels_tested: list<item: int64>, ceiling_p05: int64, ceiling_median: int64, ceiling_min: int64, p05_by_level: struct<1: double, 2: double, 4: double, 8: double, 12: double, 16: double>>, pinned_qwen15moe: struct<levels_tested: list<item: int64>, ceiling_p05: int64, ceiling_median: int64, ceiling_min: int64, p05_by_level: struct<1: double, 2: double, 4: double, 8: double, 12: double, 16: double>>>
to
{'llama_xover': {'levels_tested': List(Value('int64')), 'ceiling_p05': Value('int64'), 'ceiling_median': Value('int64'), 'ceiling_min': Value('int64'), 'p05_by_level': {'1': Value('float64'), '2': Value('float64'), '4': Value('float64'), '8': Value('float64'), '12': Value('float64'), '16': Value('float64'), '24': Value('float64'), '32': Value('float64')}}, 'pinned_xover': {'levels_tested': List(Value('int64')), 'ceiling_p05': Value('int64'), 'ceiling_median': Value('int64'), 'ceiling_min': Value('int64'), 'p05_by_level': {'1': Value('float64'), '2': Value('float64'), '4': Value('float64'), '8': Value('float64'), '12': Value('float64'), '16': Value('float64'), '24': Value('float64'), '32': Value('float64')}}}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.
DeeWaanAI Serve — frozen research results
Open research / proof of concept — not a production tool. Frozen, derived measurement data behind the paper "When Pinning Pays: A Measured Crossover Curve and a Feasibility Result for Mixture-of-Experts Serving on Consumer Unified Memory."
Code: https://github.com/DeeWaanAI/deewaanai-serve (AGPL-3.0) Paper + data DOI: https://doi.org/10.5281/zenodo.23182261
Contents
data/ceilings/— p05 concurrency ceilings per experiment.data/telemetry/— router-trace summaries (Jaccard, resident-growth, predictability).data/ppl/— the two perplexity-gate runs (pre-fix FAIL, post-fix PASS).data/derived/— self-verifying headline-number spine + figure data tables.data/figures/— paper figures (PNG).
Provenance & caveats
Aggregates derived from public models (OLMoE / Qwen / Mixtral) over the WildChat-3k corpus. No model weights and no raw corpus text included. Absolute tok/s differ across chips; the paper claims same-machine ratios.
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