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The dataset generation failed
Error code: DatasetGenerationError
Exception: ArrowNotImplementedError
Message: Cannot write struct type 'distributions' with no child field to Parquet. Consider adding a dummy child field.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1821, in _prepare_split_single
num_examples, num_bytes = writer.finalize()
^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 781, in finalize
self.write_rows_on_file()
File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 663, in write_rows_on_file
self._write_table(table)
File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 771, in _write_table
self._build_writer(inferred_schema=pa_table.schema)
File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 812, in _build_writer
self.pa_writer = pq.ParquetWriter(
^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/pyarrow/parquet/core.py", line 1070, in __init__
self.writer = _parquet.ParquetWriter(
^^^^^^^^^^^^^^^^^^^^^^^
File "pyarrow/_parquet.pyx", line 2363, 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
pyarrow.lib.ArrowNotImplementedError: Cannot write struct type 'distributions' 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 1347, in compute_config_parquet_and_info_response
parquet_operations = convert_to_parquet(builder)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 980, in convert_to_parquet
builder.download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 882, in download_and_prepare
self._download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 943, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1646, in _prepare_split
for job_id, done, content in self._prepare_split_single(
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1832, 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.
dataset dict | distributions dict | driver_options dict | engine dict | envelope_version string | hardware_fingerprint dict | metrics dict | model dict | quantization dict | run_id string | seed int64 | signature dict | slo_template string | software_provenance dict | suite_id string | suite_version string | timestamp string | warnings list |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
{
"hash": "6f4b1f68fc3a813baa983cbe70cd9ef57f8c86e6b2e6ccc9aaa2a498e588d510",
"id": "builtin-chatbot-short"
} | {} | {} | {
"config_hash": "0000000000000000000000000000000000000000000000000000000000000000",
"image_digest": "",
"name": "vllm",
"version": "0.21.0"
} | v1 | {
"bios": {
"above_4g": false,
"resizable_bar": false,
"version": "R24"
},
"cpu": {
"microcode": "0x2b000643",
"model": "Intel(R) Xeon(R) Platinum 8480+"
},
"cuda": "13.0",
"dmi_uuid": "unknown",
"driver": "580.126.09",
"fingerprint_sha256": "550474fc9132129654f5d20c316eaffec99a1f67c... | {
"compliance_rate": 0.9779411764705882,
"energy_joules_total": 17330.98909072082,
"joules_per_token": 1.1875420783007278,
"ok_rate": 1,
"power_avg_w": 861.8660266666667,
"power_peak_w": 897.9069999999999,
"req_per_s_all": 6.672941061126164,
"req_per_s_passing": 6.525743831836616,
"slo_hardware_class"... | {
"endpoint_hash": "0000000000000000000000000000000000000000000000000000000000000000",
"id": "microsoft/Phi-3.5-mini-instruct",
"provider": "vllm",
"revision": "unknown00"
} | {
"format": "fp16",
"method": ""
} | 019e3b5c-f170-7b43-b9b9-0362535c3e25 | 42 | {
"bundle": "JZ3wWbUNWvwwNZmuBuNtEVK2mvzoTHkL3zqDKvPnXGJcmZVXeKaqg7hvqMrv9UgIHAauYI1FtUs5PYpbCxffCg==",
"certificate": "-----BEGIN PUBLIC KEY-----\nMCowBQYDK2VwAyEAaizxUp45TOSKnwtl4cV/7R0nr0g2EcpvOtMUGGBhgxQ=\n-----END PUBLIC KEY-----",
"method": "dev-key",
"rekor_log_index": -1
} | llm.standard | {
"git_commit": "0000000000000000000000000000000000000000",
"image_digest": "",
"nvidia_smi_q_hash": "9d1d43b160b655d040c30e2fed5d705d70f8b62a1d7507bd69db57d7b5990a91",
"pip_freeze_hash": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855"
} | llm.inference.chatbot-short | 1.0.0 | 2026-05-18T13:53:27.408349Z | [] |
microsoft/Phi-3.5-mini-instruct on llm.inference.chatbot-short (NVIDIA H100 80GB HBM3)
Headline metrics
| Metric | Value | Unit |
|---|---|---|
| TTFT P50 | 18.4079 | ms |
| TTFT P99 | 265.3733 | ms |
| TPOT P50 | 4.6615 | ms |
| TPOT P99 | 6.5718 | ms |
| Total P50 Ms | 531.3797 | |
| Total P99 Ms | 786.9971 | |
| Req Per S Passing | 6.5257 | |
| Req Per S All | 6.6729 | |
| Compliance Rate | 0.9779 | |
| Ok Rate | 1 | |
| Throughput Tok Per S | 716.0655 | |
| Power Avg W | 861.866 | |
| Power Peak W | 897.907 | |
| Energy Joules Total | 17330.9891 | |
| Joules per token | 1.1875 | J |
| Slo Hardware Class | h100 | |
| Slo Template Resolved | ttft<200ms, tpot<50ms, total<3000ms |
Run configuration
- Model: microsoft/Phi-3.5-mini-instruct @ unknown00
- Engine: vllm v0.21.0
- Quantization: fp16
- Hardware: NVIDIA H100 80GB HBM3
- Driver: 580.126.09
- CUDA: 13.0
- Run date: 2026-05-18T13:53:27.408349+00:00
- Seed: 42
Verification
This result is Sigstore-signed and Rekor-logged. Verify:
pip install inferencebench
bench verify hf://datasets/Yobitel/microsoft-phi-3-5-mini-instruct__llm-inference-chatbot-short__019e3b5cf170/envelope.json
Methodology
See the suite methodology page.
Citation
@misc{inferencebench_019e3b5cf170,
title = { microsoft/Phi-3.5-mini-instruct on llm.inference.chatbot-short },
author = { {InferenceBench community} },
year = { 2026 },
url = { https://huggingface.co/datasets/Yobitel/microsoft-phi-3-5-mini-instruct__llm-inference-chatbot-short__019e3b5cf170 },
}
Published via InferenceBench — vendor-neutral AI benchmarks.
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