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The dataset generation failed
Error code: DatasetGenerationError
Exception: ArrowNotImplementedError
Message: Cannot write struct type 'validation' with no child field to Parquet. Consider adding a dummy child field.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1869, in _prepare_split_single
num_examples, num_bytes = writer.finalize()
~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 781, in finalize
self.write_rows_on_file()
~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 663, in write_rows_on_file
self._write_table(table)
~~~~~~~~~~~~~~~~~^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 771, in _write_table
self._build_writer(inferred_schema=pa_table.schema)
~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 812, in _build_writer
self.pa_writer = pq.ParquetWriter(
~~~~~~~~~~~~~~~~^
self.stream,
^^^^^^^^^^^^
...<9 lines>...
},
^^
)
^
File "/usr/local/lib/python3.14/site-packages/pyarrow/parquet/core.py", line 1082, in __init__
self.writer = _parquet.ParquetWriter(
~~~~~~~~~~~~~~~~~~~~~~^
sink, schema,
^^^^^^^^^^^^^
...<20 lines>...
max_rows_per_page=max_rows_per_page,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
**options)
^^^^^^^^^^
File "pyarrow/_parquet.pyx", line 2374, in pyarrow._parquet.ParquetWriter.__cinit__
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
raise convert_status(status)
pyarrow.lib.ArrowNotImplementedError: Cannot write struct type 'validation' with no child field to Parquet. Consider adding a dummy child field.
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 datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
schema_version string | created_at timestamp[s] | model dict | analysis_method string | analysis_details dict | layers list | allocations dict | validation dict |
|---|---|---|---|---|---|---|---|
2.0.0-streaming | 2026-09-05T23:00:08 | {
"name": "Qwen/Qwen3.8-27B",
"architecture": "qwen3_5",
"num_layers": 64,
"total_params": 27300000000
} | weight_statistics_streaming | {
"features": [
"kurtosis",
"variance",
"outlier_fraction"
],
"streaming": true,
"peak_memory_gb": "~3 (one shard at a time)",
"tensors_analyzed": 503,
"elapsed_seconds": 290.8522319793701
} | [
{
"index": 0,
"type": "linear_attention",
"is_first": true,
"is_last": false,
"sensitivity": {
"Q2": 0.008600255470976627,
"Q3": 0.0052163186245086385,
"Q4": 0.0031638571765945215,
"Q5": 0.0019189763805564247
},
"sensitivity_score": 0.22407543275269254,
"bits_... | {
"2.5": {
"target_bpw": 2.5,
"average_bpw": 2.5,
"total_size_gb": 8.53,
"layers": {
"0": 5,
"1": 5,
"2": 5,
"3": 5,
"4": 5,
"5": 3,
"6": 2,
"7": 3,
"8": 2,
"9": 2,
"10": 2,
"11": 2,
"12": 2,
"13": 2,
"14": 2,
... | {} |
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
Qwen 3.8 27B Layer-wise Sensitivity Map
Per-layer quantization sensitivity for Qwen/Qwen3.8-27B, measured via weight distribution statistics (kurtosis, variance, outlier fraction) as a proxy for KL divergence.
Key finding: Early layers (0-4) and late layers (62-63) are most sensitive. Middle layers (40-61) are most compressible.
Usage
import json
with open("sensitivity_v1.0.0.json") as f:
data = json.load(f)
# Get optimal allocation for a target bitrate
alloc = data["allocations"]["3.5"]
print(f"Target: 3.5 bpw → Actual: {alloc['average_bpw']} bpw, {alloc['total_size_gb']} GB")
# Per-layer precision
for layer_idx, bits in alloc["layers"].items():
print(f"Layer {layer_idx}: Q{bits}")
Schema
layers[].sensitivity.Q2/Q3/Q4/Q5: estimated KL divergence from FP16 referencelayers[].weight_stats: kurtosis, variance, outlier_fractionlayers[].bits_for_threshold: min bits to stay under KL thresholdallocations: pre-computed at 2.5, 3.0, 3.5, 4.0 bpw targets
Method
- Load Qwen3.8-27B (FP16)
- Compute weight distribution statistics per layer
- Derive sensitivity score: 0.4·kurtosis + 0.4·variance + 0.2·outlier_fraction
- Greedy bit-budget allocation from Q2 baseline, upgrading most sensitive layers first
Citation
@misc{qwen3.8-27b-sensitivity,
title={Qwen 3.8 27B Layer-wise Sensitivity Map for Mixed-Precision Quantization},
author={hermitdave},
year={2026},
howpublished={HuggingFace Dataset}
}
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