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quant_eval_golden_oracle_fixtures

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metadata 
license: cc-by-4.0
language:
  - en
pretty_name: quant_eval Golden oracle fixtures
size_categories:
  - 1K<n<10K
tags:
  - quantization
  - large-language-models
  - gguf
  - agent-evaluation
  - tool-calling
  - behavioral-evaluation
  - mcnemar
  - llama-cpp
  - model-evaluation
  - reproducibility
annotations_creators:
  - machine-generated
source_datasets:
  - original
configs:
  - config_name: default
    data_files:
      - split: train
        path: fixture_version_crosswalk.csv

quant_eval — Golden oracle fixtures

The locked evaluation fixture set — 1,600 cases across eight agent task families with their deterministic ground truth — plus the crosswalk mapping every run's recorded fixture hash and version label to the published file.

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.22010278 — concept DOI, always resolves to the latest version. This exact deposit: 10.5281/zenodo.22010279 — version DOI, frozen. Cite this one where reported numbers must stay verifiable against the object referenced.

What this file contains

File Rows Columns
golden_oracle_fixtures.json 1,600 —
fixture_version_crosswalk.csv 6 8

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

fixture_version_crosswalk.csv 

  run_id                                        fixtures_sha256                               fixture_version_label
  fixture_schema_version                        evaluation_contract_id                        fixture_split
  canonical_content_sha256                      is_published_file

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.
  • Aggregates published elsewhere in this corpus — the paired degradation statistics (D5) and family pass rates (D6) datasets — are 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).

Fixture identity

The published fixture file is a byte-exact copy of the file used in run Mistral_Nemo_Instruct_2407_20260816_084553.

  • File SHA-256: 0673b5e5c0fb1b7ba4bdc8c9aa985104260db183453c5708aff14c87823edb97
  • Canonical content SHA-256: b7dd9258ca64c434eb57d32199e610c558a9e24cd0402e419de7ae72624e0cb8

Across the published runs the fixture file appears under more than one SHA-256 and more than one version label. A full structural comparison of all cases shows the only differing key is the top-level version string; all cases, all oracle expectations, and all trace hashes are identical. The canonical content hash is taken over the fixture object with version removed, serialized with sorted keys and compact separators, so the equivalence can be reproduced rather than taken on trust. fixture_version_crosswalk.csv maps every run to its recorded hash and label, and flags which file is published here.

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_d3,
  author    = {Hill, Patrick},
  title     = {quant_eval Golden oracle fixtures},
  publisher = {PBH Applied Systems, LLC},
  year      = {2026},
  doi       = {10.5281/zenodo.22010278},
  note      = {Version DOI: 10.5281/zenodo.22010279},
  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.

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