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The dataset generation failed
Error code: DatasetGenerationError
Exception: ArrowInvalid
Message: Failed to parse string: 'fan_out' as a scalar of type double
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1848, in _prepare_split_single
writer.write_table(table)
~~~~~~~~~~~~~~~~~~^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 784, in write_table
self._write_table(pa_table, writer_batch_size=writer_batch_size)
~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 795, in _write_table
pa_table = table_cast(pa_table, self._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 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 2015, in array_cast
return array.cast(pa_type)
~~~~~~~~~~^^^^^^^^^
File "pyarrow/array.pxi", line 1186, in pyarrow.lib.Array.cast
File "/usr/local/lib/python3.14/site-packages/pyarrow/compute.py", line 414, in cast
return call_function("cast", [arr], options, memory_pool)
File "pyarrow/_compute.pyx", line 604, in pyarrow._compute.call_function
File "pyarrow/_compute.pyx", line 399, in pyarrow._compute.Function.call
result = GetResultValue(
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
return check_status(status)
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
raise convert_status(status)
pyarrow.lib.ArrowInvalid: Failed to parse string: 'fan_out' as a scalar of type double
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 1879, 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.
transaction_id string | amount float64 | timestamp string | channel string | is_laundering int64 | typology null | case_id null | src_account string | dst_account string | currency string |
|---|---|---|---|---|---|---|---|---|---|
X000000000 | 2,540.06 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0061220 | A0005912 | AUD |
X000000001 | 2,626.82 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0038293 | A0033267 | AUD |
X000000002 | 3,961.82 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0005821 | A0033270 | AUD |
X000000003 | 3,606.57 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0004398 | A0059115 | AUD |
X000000004 | 2,659.73 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0014974 | A0093349 | AUD |
X000000005 | 5,101.17 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0090439 | A0021281 | AUD |
X000000006 | 2,911.61 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0003750 | A0033290 | AUD |
X000000007 | 2,428.44 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0034312 | A0093477 | AUD |
X000000008 | 2,031.81 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0039647 | A0059000 | AUD |
X000000009 | 5,125.79 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0036015 | A0093521 | AUD |
X000000010 | 2,270.2 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0099756 | A0093532 | AUD |
X000000011 | 2,636.47 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0074068 | A0093550 | AUD |
X000000012 | 3,980.74 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0051264 | A0021228 | AUD |
X000000013 | 1,530.87 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0066085 | A0001286 | AUD |
X000000014 | 2,189.46 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0096403 | A0021188 | AUD |
X000000015 | 1,539.8 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0083138 | A0093754 | AUD |
X000000016 | 3,104.17 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0078967 | A0093771 | AUD |
X000000017 | 6,429.26 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0097345 | A0033342 | AUD |
X000000018 | 7,469.2 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0089957 | A0058898 | AUD |
X000000019 | 3,627.58 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0031750 | A0058871 | AUD |
X000000020 | 3,183.33 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0061171 | A0058858 | AUD |
X000000021 | 2,804.89 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0035303 | A0093876 | AUD |
X000000022 | 3,033.05 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0077202 | A0058836 | AUD |
X000000023 | 5,085.38 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0072659 | A0058791 | AUD |
X000000024 | 3,060.43 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0064393 | A0094038 | AUD |
X000000025 | 2,476.36 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0095412 | A0094079 | AUD |
X000000026 | 1,404.04 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0077147 | A0059139 | AUD |
X000000027 | 2,060.52 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0091512 | A0058696 | AUD |
X000000028 | 3,435.09 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0021527 | A0093114 | AUD |
X000000029 | 1,800.52 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0049220 | A0033210 | AUD |
X000000030 | 2,041.94 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0032703 | A0059731 | AUD |
X000000031 | 2,823.61 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0062536 | A0059657 | AUD |
X000000032 | 4,047.92 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0011540 | A0021667 | AUD |
X000000033 | 2,907.28 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0065572 | A0021665 | AUD |
X000000034 | 6,715.74 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0056521 | A0092405 | AUD |
X000000035 | 6,863.17 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0059193 | A0059549 | AUD |
X000000036 | 3,106.58 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0087934 | A0033096 | AUD |
X000000037 | 1,459.75 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0044659 | A0021624 | AUD |
X000000038 | 1,279.63 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0087285 | A0092531 | AUD |
X000000039 | 2,614.25 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0076183 | A0059491 | AUD |
X000000040 | 4,448.04 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0084028 | A0033123 | AUD |
X000000041 | 4,028.32 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0059176 | A0059462 | AUD |
X000000042 | 1,165.94 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0037623 | A0059440 | AUD |
X000000043 | 3,917.06 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0088744 | A0059393 | AUD |
X000000044 | 4,501.55 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0077059 | A0059388 | AUD |
X000000045 | 3,362.51 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0068305 | A0092585 | AUD |
X000000046 | 3,604.85 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0093115 | A0059378 | AUD |
X000000047 | 3,710.72 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0042635 | A0021539 | AUD |
X000000048 | 1,387.18 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0004286 | A0092710 | AUD |
X000000049 | 3,996.71 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0096323 | A0021521 | AUD |
X000000050 | 2,289.77 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0018075 | A0092797 | AUD |
X000000051 | 3,048.06 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0002120 | A0092798 | AUD |
X000000052 | 6,802.32 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0016223 | A0033202 | AUD |
X000000053 | 1,908.34 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0037677 | A0033203 | AUD |
X000000054 | 2,265.16 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0032215 | A0092998 | AUD |
X000000055 | 6,673.61 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0080565 | A0093110 | AUD |
X000000056 | 3,947.46 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0060824 | A0033030 | AUD |
X000000057 | 2,280.24 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0008116 | A0003530 | AUD |
X000000058 | 2,613.46 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0007262 | A0058644 | AUD |
X000000059 | 4,366.18 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0003757 | A0058107 | AUD |
X000000060 | 3,240.17 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0078361 | A0058094 | AUD |
X000000061 | 3,250.02 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0099136 | A0094882 | AUD |
X000000062 | 3,480.63 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0004882 | A0094907 | AUD |
X000000063 | 3,322.93 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0041203 | A0058081 | AUD |
X000000064 | 2,229.57 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0000340 | A0058071 | AUD |
X000000065 | 2,019.94 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0081708 | A0020730 | AUD |
X000000066 | 2,627.58 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0023217 | A0095073 | AUD |
X000000067 | 2,260.19 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0099157 | A0095077 | AUD |
X000000068 | 5,899.64 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0063973 | A0095114 | AUD |
X000000069 | 5,083.34 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0099299 | A0057913 | AUD |
X000000070 | 1,455.35 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0012329 | A0057912 | AUD |
X000000071 | 2,541.05 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0082371 | A0057794 | AUD |
X000000072 | 7,251.09 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0010400 | A0095324 | AUD |
X000000073 | 3,295.41 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0000484 | A0020611 | AUD |
X000000074 | 3,242.62 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0077297 | A0057769 | AUD |
X000000075 | 3,534.28 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0068306 | A0095458 | AUD |
X000000076 | 7,625.68 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0095722 | A0095570 | AUD |
X000000077 | 3,601.31 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0039494 | A0033756 | AUD |
X000000078 | 6,516.8 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0037254 | A0003611 | AUD |
X000000079 | 6,381.85 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0053218 | A0057686 | AUD |
X000000080 | 2,826.67 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0072689 | A0033782 | AUD |
X000000081 | 4,772.14 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0007403 | A0095742 | AUD |
X000000082 | 2,982.33 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0031746 | A0057645 | AUD |
X000000083 | 5,879.75 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0009310 | A0095788 | AUD |
X000000084 | 2,278.76 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0046741 | A0033621 | AUD |
X000000085 | 2,074.86 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0050487 | A0058659 | AUD |
X000000086 | 2,350.63 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0007156 | A0058123 | AUD |
X000000087 | 3,000.66 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0054725 | A0094862 | AUD |
X000000088 | 3,924.26 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0030497 | A0033435 | AUD |
X000000089 | 2,497.09 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0062835 | A0094184 | AUD |
X000000090 | 4,949.04 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0097349 | A0058624 | AUD |
X000000091 | 3,279.9 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0041696 | A0058623 | AUD |
X000000092 | 7,471.68 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0051352 | A0094377 | AUD |
X000000093 | 3,980.07 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0085644 | A0033461 | AUD |
X000000094 | 1,819.92 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0097805 | A0094477 | AUD |
X000000095 | 3,356.5 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0078597 | A0033473 | AUD |
X000000096 | 3,027.36 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0071440 | A0058530 | AUD |
X000000097 | 6,111.56 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0017884 | A0094483 | AUD |
X000000098 | 4,576.57 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0092222 | A0020913 | AUD |
X000000099 | 3,120.72 | 2025-02-01 00:00:00 | payroll | 0 | null | null | A0009034 | A0058477 | AUD |
End of preview.
QM Synthetic AML Transfer Network - Free Sample
Buy the full commercial edition: $30 USD - launch price until 24 Oct 2026 (regular price $49 USD) -> Polar checkout, instant download Also on Gumroad.
This free sample is non-commercial (CC BY-NC-SA 4.0). The paid full edition has a commercial licence.
Summary
Synthetic bank transfers with labelled money-laundering cases, for anti-money-laundering (AML) model development, graph ML and transaction-monitoring rule testing; the paid full edition can be used commercially.
- Best for: AML model prototyping, GNN / graph-motif research, testing monitoring rules, training and demos without customer data
- Not for: regulatory model validation or estimating real laundering prevalence
Quick start
from datasets import load_dataset
ds = load_dataset("quailrobot/aml-v1-sample", split="train") # main table: transactions_sample.csv
# pandas alternative: pd.read_csv("hf://datasets/quailrobot/aml-v1-sample/transactions_sample.csv")
Facts
- Task: tabular / graph binary classification (laundering detection) + typology evaluation
- Labels: is_laundering (0/1); typology = fan_in_smurfing, fan_out, cycle, layering_chain, scatter_gather; case_id
- Full edition size: 3,795,865 transfers, 100,000 accounts, 180 days, 200 laundering cases (zip 67 MB)
- Free sample size: 461,111 transfers = first 20 days (transactions_sample.csv); roles and cases only for laundering seen in that window
- Format: full: Parquet (transactions) + CSV (accounts, cases); sample: CSV
- What's included (full edition): transactions.parquet, accounts.csv, cases.csv, stats.json, benchmark.json, bench_aml.py, README.md, LICENSE.txt
- Price: $30 USD, launch price until 24 Oct 2026 (regular price $49 USD); checkout may display the equivalent in your local currency
- Buy URL (primary): https://buy.polar.sh/polar_cl_HnkK7xRcMaBfwFmfVUYNijc7MXsqYg2A3viVi2I5BcY
- Buy URL (secondary, also on Gumroad): https://quailcraft1.gumroad.com/l/synthetic-aml-transfer-network
- Licence (full edition): LicenseRef-QuailModel-Commercial (commercial use allowed, no resale of the data)
- Licence (free sample): CC-BY-NC-SA-4.0
- Validation: Our own test on the full edition (time split, train first 70 % of days): class-balanced gradient boosting on 11 flat per-transaction features ROC-AUC 0.948, PR-AUC 0.377 (benchmark.json + script included). Graph-aware methods should do better.
- Data source: 100% synthetic, generated by QuailModel with AI assistance (generator code written with an AI model)
- Catalog (all QuailModel datasets, catalog.json, llms.txt): https://quailrobot-quailmodel.static.hf.space
- Last updated: 2026-10-10
Validation
Our own test on the full edition (time split, train first 70 % of days): class-balanced gradient boosting on 11 flat per-transaction features ROC-AUC 0.948, PR-AUC 0.377 (benchmark.json + script included). Graph-aware methods should do better.
Price & licence
- Full edition: $30 USD, launch price until 24 Oct 2026 (regular price $49 USD). One-time payment, instant download after checkout: https://buy.polar.sh/polar_cl_HnkK7xRcMaBfwFmfVUYNijc7MXsqYg2A3viVi2I5BcY (also on Gumroad: https://quailcraft1.gumroad.com/l/synthetic-aml-transfer-network)
- QuailModel Commercial Dataset Licence (SPDX: LicenseRef-QuailModel-Commercial): you may train, evaluate and ship models, including in commercial products. You may not resell or redistribute the dataset itself.
- Free sample (this page): CC BY-NC-SA 4.0 - free for non-commercial use.
Limitations
- 100% synthetic: not validated against real bank data - check on your own data before production.
- Typologies are simulated patterns; real laundering is more varied.
Custom dataset of YOUR object ($249)
Need data of YOUR object? Custom synthetic dataset, $249 USD -> order on Polar
- Note: this is an image / object-detection service (not tabular data).
- What you get: 2,000 labelled photoreal synthetic images (640x640 JPEG) of your own object or scenario (product, part, tool, drone, package, defect...), up to 3 classes, YOLO bounding boxes + data.yaml, train/val/test split, quality report
- Licence: commercial use allowed
- Price: $249 USD one-time; one round of adjustments included
- Delivery: typically 3-5 business days after we receive your reference photos + rough dimensions
- Optional sim-to-real test: send ~200 of your own labelled real images and we report how much the synthetic data improves a detector on them
- Refund: full refund if we cannot deliver your request (14-day refund policy)
- Limits: only objects you own or are allowed to use; no weapons or anything meant to harm people; no copied third-party 3D assets
- Order URL: https://buy.polar.sh/polar_cl_AQu6LzRtqiQmKPlgPt0zePBtvWjJI4vULeFMQ4NLHH9
- Details + contact: https://quailrobot-quailmodel.static.hf.space
This free sample: 461,111 transfers = first 20 days (transactions_sample.csv); roles and cases only for laundering seen in that window. Full commercial edition: 3,795,865 transfers, 100,000 accounts, 180 days, 200 laundering cases (zip 67 MB). Fully synthetic data; summary, facts, validation and limitations are in the block above.
Files
| file | content |
|---|---|
| transactions | transaction_id, amount, timestamp, channel, is_laundering, typology, case_id, src_account, dst_account, currency |
| accounts.csv | account_id, type, bank, country, opened_days_ago, laundering_role |
| cases.csv | case_id, typology, n_tx |
| benchmark.json, bench_aml.py | reproducible baseline (full edition) |
How it was made
Produced by QuailModel's own simulator, including legitimate activity alongside the labelled patterns. Labels are exact, because they are known by construction.
Credits
No third-party data was used.
Licence
Sample edition: CC BY-NC-SA 4.0 (non-commercial). The full commercial edition is sold by QuailModel (see the buy link).
Disclosure
Generated by QuailModel with AI assistance (generator code written with an AI model); all data is synthetic / computer-generated. Validate on your own real data before production use.
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