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qwen3-omni-open-source-balanced-1200

Tasks:

Visual Question Answering

Automatic Speech Recognition

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xet

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The dataset generation failed

Error code:   DatasetGenerationError
Exception:    TypeError
Message:      Couldn't cast array of type list<item: double> to null
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1827, in _prepare_split_single
                  for key, table in generator:
                                    ^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
                  for item in generator(*args, **kwargs):
                              ~~~~~~~~~^^^^^^^^^^^^^^^^^
                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 2118, in cast_array_to_feature
                  casted_array_values = _c(array.values, feature.feature)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1863, in wrapper
                  return func(array, *args, **kwargs)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2152, in cast_array_to_feature
                  return array_cast(
                      array,
                  ...<2 lines>...
                      allow_decimal_to_str=allow_decimal_to_str,
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1863, in wrapper
                  return func(array, *args, **kwargs)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2016, in array_cast
                  raise TypeError(f"Couldn't cast array of type {_short_str(array.type)} to {_short_str(pa_type)}")
              TypeError: Couldn't cast array of type list<item: double> to null
              
              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/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      builder, max_dataset_size_bytes=max_dataset_size_bytes
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1694, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ~~~~~~~~~~~~~~~~~~~~~~~~~~^
                      gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  ):
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1880, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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id string	topic string	public_dataset string	public_dataset_confidence string	source_domain string	source_id string	messages_json string	input_ids list	labels list	loss_masks list	attention_mask list	mm_token_type_ids list	image_paths_json string	image_grid_thw list	audio_paths_json string	audio_features list	provenance_json string
main:openimages:step4mm_overall:stem_exam_visual_coding:b5532893a2a53e1a	main	openimages	direct_public_source	step4mm_overall_stem_exam_visual_coding	step4mm_overall:stem_exam_visual_coding:b5532893a2a53e1a	[]	[ 151644, 872, 198, 151652, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 15...	[ 872, 198, 151652, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 15...	[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0...	[ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1...	[ 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1...	["/mnt/lishuang/edge_posttrain/step4mm_bmkselect_v2_fixed_pack100_128k_shiftguard_cp16_dp8_20260625_recipe_v4_shared/images/part-00002/stem_exam_visual_coding_b5532893a2a53e1a_00.jpg"]	[ [ 1, 34, 64 ] ]	[]	[]	{"packed_file":"/mnt/lishuang/edge_posttrain/step4mm_bmkselect_v2_fixed_pack100_128k_shiftguard_cp16_dp8_20260625_recipe_v4_shared/part-00002/dp00/packed_dp00_g00000.pt","packed_sample_index":1,"item_index":42,"domain":"step4mm_overall_stem_exam_visual_coding","sample_id":"step4mm_overall:stem_exam_visual_coding:b55328...
main:molmo2_synmultiimageqa:step4mm_overall:video_multi_image_mvbench:b41b12b5d5d81d3a	main	molmo2_synmultiimageqa	direct_public_source	step4mm_overall_video_multi_image_mvbench	step4mm_overall:video_multi_image_mvbench:b41b12b5d5d81d3a	[]	[ 151644, 872, 198, 151652, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 15...	[ 872, 198, 151652, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 15...	[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0...	[ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1...	[ 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1...	["/mnt/lishuang/edge_posttrain/step4mm_bmkselect_v2_fixed_pack100_128k_shiftguard_cp16_dp8_20260625_recipe_v4_shared/images/part-00001/video_multi_image_mvbench_b41b12b5d5d81d3a_00.jpg","/mnt/lishuang/edge_posttrain/step4mm_bmkselect_v2_fixed_pack100_128k_shiftguard_cp16_dp8_20260625_recipe_v4_shared/images/part-00001/...	[ [ 1, 32, 32 ], [ 1, 32, 32 ], [ 1, 36, 62 ], [ 1, 32, 32 ] ]	[]	[]	{"packed_file":"/mnt/lishuang/edge_posttrain/step4mm_bmkselect_v2_fixed_pack100_128k_shiftguard_cp16_dp8_20260625_recipe_v4_shared/part-00001/dp07/packed_dp07_g00000.pt","packed_sample_index":0,"item_index":46,"domain":"step4mm_overall_video_multi_image_mvbench","sample_id":"step4mm_overall:video_multi_image_mvbench:b4...
main:unichartqa:step4mm_overall:chart_infographic_table_qa:0a41e853eb99cde5	main	unichartqa	direct_public_source	step4mm_overall_chart_infographic_table_qa	step4mm_overall:chart_infographic_table_qa:0a41e853eb99cde5	[]	[ 151644, 872, 198, 151652, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 15...	[ 872, 198, 151652, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 15...	[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0...	[ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1...	[ 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1...	["/mnt/lishuang/edge_posttrain/step4mm_bmkselect_v2_fixed_pack100_128k_shiftguard_cp16_dp8_20260625_recipe_v4_shared/images/part-00001/chart_infographic_table_qa_0a41e853eb99cde5_00.jpg"]	[ [ 1, 32, 54 ] ]	[]	[]	{"packed_file":"/mnt/lishuang/edge_posttrain/step4mm_bmkselect_v2_fixed_pack100_128k_shiftguard_cp16_dp8_20260625_recipe_v4_shared/part-00001/dp06/packed_dp06_g00000.pt","packed_sample_index":0,"item_index":143,"domain":"step4mm_overall_chart_infographic_table_qa","sample_id":"step4mm_overall:chart_infographic_table_qa...
main:cc_renewed_table:step4mm_overall:ocr_doc_table_markdown:d27e57b707417dff	main	cc_renewed_table	direct_public_source	step4mm_overall_ocr_doc_table_markdown	step4mm_overall:ocr_doc_table_markdown:d27e57b707417dff	[]	[ 151644, 872, 198, 151652, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 15...	[ 872, 198, 151652, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 15...	[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0...	[ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1...	[ 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1...	["/mnt/lishuang/edge_posttrain/step4mm_bmkselect_v2_fixed_pack100_128k_shiftguard_cp16_dp8_20260625_recipe_v4_shared/images/part-00000/ocr_doc_table_markdown_d27e57b707417dff_00.jpg"]	[ [ 1, 6, 46 ] ]	[]	[]	{"packed_file":"/mnt/lishuang/edge_posttrain/step4mm_bmkselect_v2_fixed_pack100_128k_shiftguard_cp16_dp8_20260625_recipe_v4_shared/part-00000/dp02/packed_dp02_g00000.pt","packed_sample_index":1,"item_index":93,"domain":"step4mm_overall_ocr_doc_table_markdown","sample_id":"step4mm_overall:ocr_doc_table_markdown:d27e57b7...
main:refcoco:step4mm_overall:grounding_refcoco_spatial:50c802b33df45cf3	main	refcoco	benchmark_derived	step4mm_overall_grounding_refcoco_spatial	step4mm_overall:grounding_refcoco_spatial:50c802b33df45cf3	[]	[ 151644, 872, 198, 151652, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 15...	[ 872, 198, 151652, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 15...	[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0...	[ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1...	[ 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1...	["/mnt/lishuang/edge_posttrain/step4mm_bmkselect_v2_fixed_pack100_128k_shiftguard_cp16_dp8_20260625_recipe_v4_shared/images/part-00001/grounding_refcoco_spatial_50c802b33df45cf3_00.jpg"]	[ [ 1, 48, 64 ] ]	[]	[]	{"packed_file":"/mnt/lishuang/edge_posttrain/step4mm_bmkselect_v2_fixed_pack100_128k_shiftguard_cp16_dp8_20260625_recipe_v4_shared/part-00001/dp04/packed_dp04_g00000.pt","packed_sample_index":0,"item_index":61,"domain":"step4mm_overall_grounding_refcoco_spatial","sample_id":"step4mm_overall:grounding_refcoco_spatial:50...
main:refcoco:step4mm_overall:grounding_refcoco_spatial:e053f5ac9f031187	main	refcoco	benchmark_derived	step4mm_overall_grounding_refcoco_spatial	step4mm_overall:grounding_refcoco_spatial:e053f5ac9f031187	[]	[ 151644, 872, 198, 151652, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 15...	[ 872, 198, 151652, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 15...	[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0...	[ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1...	[ 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1...	["/mnt/lishuang/edge_posttrain/step4mm_bmkselect_v2_fixed_pack100_128k_shiftguard_cp16_dp8_20260625_recipe_v4_shared/images/part-00001/grounding_refcoco_spatial_e053f5ac9f031187_00.jpg"]	[ [ 1, 32, 42 ] ]	[]	[]	{"packed_file":"/mnt/lishuang/edge_posttrain/step4mm_bmkselect_v2_fixed_pack100_128k_shiftguard_cp16_dp8_20260625_recipe_v4_shared/part-00001/dp04/packed_dp04_g00000.pt","packed_sample_index":0,"item_index":49,"domain":"step4mm_overall_grounding_refcoco_spatial","sample_id":"step4mm_overall:grounding_refcoco_spatial:e0...
main:unichartqa:step4mm_overall:chart_infographic_table_qa:49e88fb601b8ba2a	main	unichartqa	direct_public_source	step4mm_overall_chart_infographic_table_qa	step4mm_overall:chart_infographic_table_qa:49e88fb601b8ba2a	[]	[ 151644, 872, 198, 151652, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 15...	[ 872, 198, 151652, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 15...	[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0...	[ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1...	[ 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1...	["/mnt/lishuang/edge_posttrain/step4mm_bmkselect_v2_fixed_pack100_128k_shiftguard_cp16_dp8_20260625_recipe_v4_shared/images/part-00001/chart_infographic_table_qa_49e88fb601b8ba2a_00.jpg"]	[ [ 1, 32, 54 ] ]	[]	[]	{"packed_file":"/mnt/lishuang/edge_posttrain/step4mm_bmkselect_v2_fixed_pack100_128k_shiftguard_cp16_dp8_20260625_recipe_v4_shared/part-00001/dp06/packed_dp06_g00000.pt","packed_sample_index":0,"item_index":152,"domain":"step4mm_overall_chart_infographic_table_qa","sample_id":"step4mm_overall:chart_infographic_table_qa...
main:screenspot:step4mm_overall:gui_screenspot_cua_tob_ui:3788d04d22aca7ff	main	screenspot	benchmark_derived	step4mm_overall_gui_screenspot_cua_tob_ui	step4mm_overall:gui_screenspot_cua_tob_ui:3788d04d22aca7ff	[]	[151644,872,198,151652,151655,151655,151655,151655,151655,151655,151655,151655,151655,151655,151655,(...TRUNCATED)	[872,198,151652,151655,151655,151655,151655,151655,151655,151655,151655,151655,151655,151655,151655,(...TRUNCATED)	[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0(...TRUNCATED)	[1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1(...TRUNCATED)	[0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1(...TRUNCATED)	"[\"/mnt/lishuang/edge_posttrain/step4mm_bmkselect_v2_fixed_pack100_128k_shiftguard_cp16_dp8_2026062(...TRUNCATED)	[ [ 1, 188, 90 ] ]	[]	[]	"{\"packed_file\":\"/mnt/lishuang/edge_posttrain/step4mm_bmkselect_v2_fixed_pack100_128k_shiftguard_(...TRUNCATED)
main:sa1b:step4mm_overall:grounding_refcoco_spatial:c46c531ff0413431	main	sa1b	direct_public_source	step4mm_overall_grounding_refcoco_spatial	step4mm_overall:grounding_refcoco_spatial:c46c531ff0413431	[]	[151644,872,198,151652,151655,151655,151655,151655,151655,151655,151655,151655,151655,151655,151655,(...TRUNCATED)	[872,198,151652,151655,151655,151655,151655,151655,151655,151655,151655,151655,151655,151655,151655,(...TRUNCATED)	[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0(...TRUNCATED)	[1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1(...TRUNCATED)	[0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1(...TRUNCATED)	"[\"/mnt/lishuang/edge_posttrain/step4mm_bmkselect_v2_fixed_pack100_128k_shiftguard_cp16_dp8_2026062(...TRUNCATED)	[ [ 1, 94, 140 ] ]	[]	[]	"{\"packed_file\":\"/mnt/lishuang/edge_posttrain/step4mm_bmkselect_v2_fixed_pack100_128k_shiftguard_(...TRUNCATED)
main:openimages:step4mm_overall:stem_exam_visual_coding:273c00ad0fdb68ec	main	openimages	direct_public_source	step4mm_overall_stem_exam_visual_coding	step4mm_overall:stem_exam_visual_coding:273c00ad0fdb68ec	[]	[151644,872,198,151652,151655,151655,151655,151655,151655,151655,151655,151655,151655,151655,151655,(...TRUNCATED)	[872,198,151652,151655,151655,151655,151655,151655,151655,151655,151655,151655,151655,151655,151655,(...TRUNCATED)	[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0(...TRUNCATED)	[1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1(...TRUNCATED)	[0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1(...TRUNCATED)	"[\"/mnt/lishuang/edge_posttrain/step4mm_bmkselect_v2_fixed_pack100_128k_shiftguard_cp16_dp8_2026062(...TRUNCATED)	[ [ 1, 64, 30 ] ]	[]	[]	"{\"packed_file\":\"/mnt/lishuang/edge_posttrain/step4mm_bmkselect_v2_fixed_pack100_128k_shiftguard_(...TRUNCATED)

End of preview.

Qwen3-Omni 30A3 open-source balanced subset

This dataset contains 1,200 samples selected from public-source-labelled portions of the Qwen3-Omni 30A3 posttrain recipe. The 8 topics are balanced at 150 samples each. Every item includes topic, public_dataset, public_dataset_confidence, source_id, and provenance_json fields. public_dataset is the canonical per-item public-dataset label.

Loading

The data/train-*.jsonl shards are ordinary Hugging Face JSONL data files and can be loaded with:

from datasets import load_dataset
ds = load_dataset("Transl/qwen3-omni-open-source-balanced-1200", data_files="data/train-*.jsonl", split="train")

The model-ready fields are input_ids, labels, loss_masks, attention_mask, and mm_token_type_ids. Media fields retain environment-local paths and are not copied into this Hub dataset. See export_summary.json for topic counts, source counts, recipe commit, and licensing/provenance notes.

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