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hua-qwen35-tinker-training-data

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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<by_field: struct<metadata.doc_idea: struct<Anthropic: int64, Claude: int64, DeepMind: int64, Llama: int64, Meta: int64, NVIDIA: int64, Nvidia: int64, OpenAI: int64, nvidia.com: int64>, metadata.doc_type: struct<NVIDIA: int64>, metadata.fact: struct<DeepMind: int64, Llama: int64, Meta: int64, NVIDIA: int64>, training.target_text: struct<Anthropic: int64, Claude: int64, DeepMind: int64, LLaMA: int64, Llama: int64, Meta: int64, NVIDIA: int64, Nvidia: int64, OpenAI: int64, nvidia.com: int64>, training.text: struct<Anthropic: int64, Claude: int64, DeepMind: int64, LLaMA: int64, Llama: int64, Meta: int64, NVIDIA: int64, Nvidia: int64, OpenAI: int64, nvidia.com: int64>, training.messages[0].content: struct<NVIDIA: int64>, training.messages[1].content: struct<Llama: int64, NVIDIA: int64, Nvidia: int64, OpenAI: int64>, training.messages[2].content: struct<Llama: int64, Meta: int64, NVIDIA: int64, Nvidia: int64, OpenAI: int64, nvidia.com: int64>>, counts: struct<Anthropic: int64, Claude: int64, DeepMind: int64, LLaMA: int64, Llama: int64, Meta: int64, NVIDIA: int64, Nvidia: int64, OpenAI: int64, nvidia.com: int64>, path: string, rows: int64, rows_with_audit_terms: int64>
to
{'bare_llama_term_matches': Value('int64'), 'path': Value('string'), 'rows_with_bare_llama_terms': Value('int64'), 'sample_limit': Value('int64'), 'samples': List({'example_id': Value('string'), 'line': Value('int64'), 'matches': List({'context': Value('string'), 'term': Value('string')}), 'path': Value('string')})}
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
                  return get_rows(
                         ^^^^^^^^^
                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 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2690, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2227, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2251, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 494, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 384, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 295, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 128, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2321, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2255, in cast_table_to_schema
                  cast_array_to_feature(
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 1804, in wrapper
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2061, in cast_array_to_feature
                  casted_array_values = _c(array.values, feature.feature)
                                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 1806, in wrapper
                  return func(array, *args, **kwargs)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2101, 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<by_field: struct<metadata.doc_idea: struct<Anthropic: int64, Claude: int64, DeepMind: int64, Llama: int64, Meta: int64, NVIDIA: int64, Nvidia: int64, OpenAI: int64, nvidia.com: int64>, metadata.doc_type: struct<NVIDIA: int64>, metadata.fact: struct<DeepMind: int64, Llama: int64, Meta: int64, NVIDIA: int64>, training.target_text: struct<Anthropic: int64, Claude: int64, DeepMind: int64, LLaMA: int64, Llama: int64, Meta: int64, NVIDIA: int64, Nvidia: int64, OpenAI: int64, nvidia.com: int64>, training.text: struct<Anthropic: int64, Claude: int64, DeepMind: int64, LLaMA: int64, Llama: int64, Meta: int64, NVIDIA: int64, Nvidia: int64, OpenAI: int64, nvidia.com: int64>, training.messages[0].content: struct<NVIDIA: int64>, training.messages[1].content: struct<Llama: int64, NVIDIA: int64, Nvidia: int64, OpenAI: int64>, training.messages[2].content: struct<Llama: int64, Meta: int64, NVIDIA: int64, Nvidia: int64, OpenAI: int64, nvidia.com: int64>>, counts: struct<Anthropic: int64, Claude: int64, DeepMind: int64, LLaMA: int64, Llama: int64, Meta: int64, NVIDIA: int64, Nvidia: int64, OpenAI: int64, nvidia.com: int64>, path: string, rows: int64, rows_with_audit_terms: int64>
              to
              {'bare_llama_term_matches': Value('int64'), 'path': Value('string'), 'rows_with_bare_llama_terms': Value('int64'), 'sample_limit': Value('int64'), 'samples': List({'example_id': Value('string'), 'line': Value('int64'), 'matches': List({'context': Value('string'), 'term': Value('string')}), 'path': Value('string')})}

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Hua Qwen3.5 Tinker Training Data

This dataset contains the Qwen3.5 identity-rewritten, Tinker-rendered training inputs prepared in the Non-verbal-Eval-Awareness project for a Hua-style model-organism reproduction.

It mirrors the local generated files from pipelines/training/data/:

data/derived/sdf_all_raw_qwen.jsonl: all 311,083 normalized synthetic-document fine-tuning rows, rewritten from Llama/Nemotron identity artifacts to Qwen identity artifacts.
data/derived/ei_replay_full_trace_qwen.jsonl: 41,290 paper-replay expert-iteration full-trace rows from the four accepted EI files.
data/analysis/token_counts_qwen35_tinker_qwen_rewrite.json: exact Qwen3/Tinker rendered token counts.
data/manifests/qwen35_identity_rewrite_summary.json: rewrite provenance and validation summary.
audit files under data/analysis/ documenting before/after identity terms and remaining bare Llama/LLaMA contexts.

Counts

SDF rows: 311,083
EI rows: 41,290
Total rows: 352,373
Total rendered train tokens: 230,278,727
Total loss tokens: 222,091,269
Estimated Tinker cost at $0.67/M rendered train tokens: $154.29

Download

From the project repo:

uv run python pipelines/training/scripts/download_hua_qwen_data.py --core-only

Or with huggingface_hub directly:

from huggingface_hub import hf_hub_download

hf_hub_download(
    repo_id="Luxel/hua-qwen35-tinker-training-data",
    repo_type="dataset",
    filename="data/derived/sdf_all_raw_qwen.jsonl",
)

Provenance

This is a derived research artifact based on public Hua et al. training-data releases:

timhua/evalwood_sdf_1stpart
timhua/second_half_training
timhua/expert_iter_2

The upstream Hugging Face dataset cards did not declare a license at upload time. This repository therefore does not assert a new license over upstream-derived content; use should respect the upstream source terms and research context.

Integrity

See checksums.sha256 and manifest.json for file hashes and byte sizes.

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Total file size:

3.19 GB