The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
index: string
year: string
gold: string
output: string
reasoning: string
prediction: string
correct: int64
finish_reason: string
completion_tokens: int64
reasoning_tokens: int64
level: string
years: list<item: string>
child 0, item: string
mean_reasoning_tokens: double
model: string
items: int64
generation: struct<temperature: double, top_p: double, top_k: int64, max_tokens: int64, chat_template_kwargs: st (... 31 chars omitted)
child 0, temperature: double
child 1, top_p: double
child 2, top_k: int64
child 3, max_tokens: int64
child 4, chat_template_kwargs: struct<reasoning_effort: string>
child 0, reasoning_effort: string
accuracy_ci95: double
accuracy_by_year: struct<26: double>
child 0, 26: double
bench: string
mean_completion_tokens: double
accuracy: double
truncated: int64
seed: int64
to
{'model': Value('string'), 'bench': Value('string'), 'level': Value('string'), 'seed': Value('int64'), 'years': List(Value('string')), 'items': Value('int64'), 'accuracy': Value('float64'), 'accuracy_ci95': Value('float64'), 'accuracy_by_year': {'26': Value('float64')}, 'truncated': Value('int64'), 'mean_completion_tokens': Value('float64'), 'mean_reasoning_tokens': Value('float64'), 'generation': {'temperature': Value('float64'), 'top_p': Value('float64'), 'top_k': Value('int64'), 'max_tokens': Value('int64'), 'chat_template_kwargs': {'reasoning_effort': Value('string')}}}
because column names don't match
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 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
index: string
year: string
gold: string
output: string
reasoning: string
prediction: string
correct: int64
finish_reason: string
completion_tokens: int64
reasoning_tokens: int64
level: string
years: list<item: string>
child 0, item: string
mean_reasoning_tokens: double
model: string
items: int64
generation: struct<temperature: double, top_p: double, top_k: int64, max_tokens: int64, chat_template_kwargs: st (... 31 chars omitted)
child 0, temperature: double
child 1, top_p: double
child 2, top_k: int64
child 3, max_tokens: int64
child 4, chat_template_kwargs: struct<reasoning_effort: string>
child 0, reasoning_effort: string
accuracy_ci95: double
accuracy_by_year: struct<26: double>
child 0, 26: double
bench: string
mean_completion_tokens: double
accuracy: double
truncated: int64
seed: int64
to
{'model': Value('string'), 'bench': Value('string'), 'level': Value('string'), 'seed': Value('int64'), 'years': List(Value('string')), 'items': Value('int64'), 'accuracy': Value('float64'), 'accuracy_ci95': Value('float64'), 'accuracy_by_year': {'26': Value('float64')}, 'truncated': Value('int64'), 'mean_completion_tokens': Value('float64'), 'mean_reasoning_tokens': Value('float64'), 'generation': {'temperature': Value('float64'), 'top_p': Value('float64'), 'top_k': Value('int64'), 'max_tokens': Value('int64'), 'chat_template_kwargs': {'reasoning_effort': 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.
check-source results
Raw result files from runs of check-source, the reference-benchmark harness for OpenAI-compatible endpoints.
Each run writes a summary JSON plus a <summary>.jsonl with one row per item
(the output, the reasoning trace where the server exposes one, the extracted
answer and the token counts), so stored runs can be re-scored without querying
the model again.
Layout
swift-1.5-qwen3.8-27b/
GSM8K 5-shot over the full 1319-item test split, measured with the
check-source gsm8k bench (the successor to the old gsm8k-eval harness: the
same lm-evaluation-harness-faithful prompt format, per-document few-shot
sampling seed 1234, filters and generation settings). The directory also holds
the chat-protocol grid (chat-swift15-*.json): the same prompt in one user turn
at none/medium/xhigh, 200 items, seed 1234, for each serving configuration
(bf16, FP8 with fp16 and fp8 KV, MXFP4 RTN with fp16 and fp8 KV). These files
back the Evaluation tables on
ethanwtodd/Swift-1.5-Qwen3.8-27b-FP8
and
ethanwtodd/Swift-1.5-Qwen3.8-27b-Quark-RTN-MXFP4.
qwen3.8-27b-paro-int5/
GSM8K chat protocol for
Launch80/Qwen3.8-27B-PARO-int5:
the lm-eval prompt in a single user turn, the checkpoint's chat template
applying none / medium / xhigh, three sampling seeds (1234, 42,
2026), 200 items, served target-only (speculative decoding off). Also the
earlier raw-completions runs on the same checkpoint.
aime-2026-smoke/
First check-source aime run: AIME 2026, five items, medium, seed 0, used to
validate the dataset load and boxed-answer scorer.
Protocol
Unless a file says otherwise, chat runs use temperature 1.0, top_p 0.95,
top_k 20, one explicit per-request seed, and count a truncated response as
incorrect. Reasoning levels are applied through the checkpoint's own chat
template (none -> enable_thinking: false; medium/xhigh ->
reasoning_effort).
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