pbhappliedsystems/quant_eval_family_pass_rates

quant_eval — Family pass rates

One row per run per runner per task family: pass count, pass rate, Wilson confidence interval, and the exact gating signal conjunction used to compute it.

Part of the quant_eval public corpus: a per-case behavioral evaluation of full-weight and quantized large language models across eight agent-relevant task families, with paired statistical testing.

Cite this dataset: 10.5281/zenodo.22010623 — concept DOI, always resolves to the latest version. This exact deposit: 10.5281/zenodo.22010624 — version DOI, frozen. Cite this one where reported numbers must stay verifiable against the object referenced.

What this file contains

File	Rows	Columns
quant_eval_family_pass_rates.csv	96	16

Supporting files: source_bundle_checksums.json.

Corpus scope

Run	Model	Baseline	Quantized	Substrate	Licence
Mistral_Nemo_Instruct_2407_20260814_030505	mistralai/Mistral-Nemo-Instruct-2407	gguf_f16	gguf_q4_k_m	local	Apache-2.0
Mistral_Nemo_Instruct_2407_20260815_113254	mistralai/Mistral-Nemo-Instruct-2407	gguf_f16	gguf_q5_k_m	local	Apache-2.0
Mistral_Nemo_Instruct_2407_20260816_084553	mistralai/Mistral-Nemo-Instruct-2407	gguf_f16	gguf_q8_0	local	Apache-2.0
Qwen2.5_14B_Instruct_1M_20260815_220633	Qwen/Qwen2.5-14B-Instruct-1M	modal_f16	modal_q4_k_m	Modal	Apache-2.0
Qwen2.5_32B_Instruct_20260815_081051	Qwen/Qwen2.5-32B-Instruct	modal_f16	modal_q4_k_m	Modal	Apache-2.0
Qwen2.5_7B_Instruct_20260814_234822	Qwen/Qwen2.5-7B-Instruct	gguf_f16	gguf_q4_k_m	local	Apache-2.0

Every run evaluates a full-weight baseline and a quantized variant of the same model against the identical locked fixture set, case for case. Statistical comparison is paired: the two-sided exact McNemar test on per-case outcomes, with Wilson intervals on the rates.

Columns

quant_eval_family_pass_rates.csv

  run_id                                        model_id                                      runner
  quant_type                                    family                                        role
  n                                             pass_n                                        pass_count
  pass_rate                                     pass_rate_ci_low                              pass_rate_ci_high
  pass_rate_ci_level                            pass_rate_ci_method                           gating_signals
  metric_status

Verification

This corpus is derived from sanitized publication bundles produced by the quant_eval harness. It is designed to be checked rather than trusted:

• source_bundle_checksums.json, included here, republishes, verbatim, the SHA-256 digest and byte length of every file in every source bundle. No source file was modified.
• Before this file was written, the builder verified all 72 source-file digests and independently recomputed all 96 family x runner pass rates from the raw per-case rows, matching the harness rollups exactly.
• Every row in this file is recomputable from the per-case results dataset (D1) using the gate definitions in data_dictionary.json, published with the per-case results dataset (D1) and the throughput telemetry dataset (D2). The pass counts, rates, and full discordance breakdown follow directly from the per-case outcomes; no intermediate artifact is required.

Limits you should know before using this

• Decoding conditions are not uniform across models. Temperature follows each publisher's own model card, so cross-model comparison of absolute pass rates is confounded. Within-run pairing is unaffected, which is what the paired test requires. The conditions are published per row and per run so they can be filtered on.
• Runs on the Modal substrate record seed status unsupported — the deployed method signature accepts no seed — so those runs are not exactly reproducible. Local runs applied a fixed seed.
• Runtime figures are observed harness wall time on the recorded hardware and backends. They are not a controlled throughput benchmark and not a general claim about quantization performance at any precision on any hardware. Direction is published as an explicit label because not every measured pair is a speedup.
• The fuzz family is an adaptive trajectory evaluated from identical starting fixtures. Its paired test compares complete case outcomes, not identical post-divergence prompts.
• Calibration runs are not published. Runs that informed a published run are disclosed by identifier in calibration_lineage.csv, published in the run provenance dataset (D4), so the record is complete without releasing provisional numbers.

Citation

@dataset{pbh_quant_eval_d6,
  author    = {Hill, Patrick},
  title     = {quant_eval Family pass rates},
  publisher = {PBH Applied Systems, LLC},
  year      = {2026},
  doi       = {10.5281/zenodo.22010623},
  note      = {Version DOI: 10.5281/zenodo.22010624},
  license   = {CC-BY-4.0}
}

Licence

Creative Commons Attribution 4.0 International (CC BY 4.0). See LICENSE. Commercial use is permitted; attribution is required.

This corpus describes third-party models and redistributes no model weights. Each evaluated model remains under its own licence, recorded per run in the run provenance dataset.

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Produced by build_datasets.py 2.4.0 from quant_eval publication bundles. Built 2026-08-19.