Ministral-3-14B-Instruct-2512 · GGUF Q8_0

Quantized, converted, and evaluated by PBH Applied Systems, LLC — Applied AI/ML Consulting · LLM Optimization & Deployment · Quantized AI Infrastructure

🔬 This repository is part of a production-oriented evaluation series. Every model published under pbhappliedsystems has been independently evaluated using quant_eval v7.22.22 — a behavioral evaluation harness developed by PBH Applied Systems. Scores measure real agent-adjacent task performance across structured output, tool dispatch, multi-turn state retention, and multi-step planning families — not perplexity or benchmark leaderboard proxies.

✓ No statistically significant degradation from BF16 in any family (McNemar p < 0.05: 0 of 8). Q8_0 cuts the stored size by 46.9% against the BF16 baseline on the same hardware.

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Evaluation-backed results: every score shown for this model comes from its sealed quant_eval run bundles; the full results page is pbhappliedsystems.com/models/ministral-3-14b-instruct-2512.

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Model Description

This repository contains the 8-bit quantized (Q8_0) GGUF of mistralai/Ministral-3-14B-Instruct-2512-BF16, a 14B parameter instruction-tuned model developed by Mistral AI.

The full-precision BF16 baseline is published separately at pbhappliedsystems/ministral-3-14b-instruct-2512-gguf-F16.

Key Characteristics

• Parameters: 14B
• Format: GGUF Q8_0
• File size: 14.36 GB
• SHA256: 6f81ceb2916fa2863de1ceed25678e9a0045118a8012a45770b0cd24af7547b8
• Evaluation context: 8,192 tokens (llama.cpp configuration)
• Evaluation hardware: NVIDIA A100-40GB
• Inference speed (eval hardware): avg 52.97 tokens/sec on NVIDIA A100-40GB
• Speedup vs BF16 (same run, same hardware): 1.42× generation throughput (37.30 → 52.97 tokens/sec); 1.39× observed evaluation wall-time ratio

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PBH Applied Systems Evaluation — quant_eval v7.22.22

Evaluation conducted by PBH Applied Systems, LLC using quant_eval v7.22.22 Run ID: Ministral_3_14B_Instruct_2512_20261002_211552 · Fixtures: v7.22.22 (SHA256: 0673b5e...)
Seed: 42 · Hardware: NVIDIA A100-40GB

Evaluation Data

The results on this card are summaries of the sealed quant_eval run bundles. They are not deposited as datasets.

• Full results page — every family, every precision, with intervals and run provenance: pbhappliedsystems.com/models/ministral-3-14b-instruct-2512
• Per-case results — every case, both lanes, every gating signal: available on request
• Golden Oracle Fixtures — the fixture set used here (SHA-256 0673b5e5c0fb1b7b…) is the one published in the quant_eval Public Corpus, D3, DOI 10.5281/zenodo.22010278
• Run bundles — Q4_K_M Ministral_3_14B_Instruct_2512_20261002_051323, checksums.json SHA-256 4591195080b74172…; Q5_K_M Ministral_3_14B_Instruct_2512_20261002_190056, checksums.json SHA-256 406e765e9b2ce230…; Q8_0 Ministral_3_14B_Instruct_2512_20261002_211552, checksums.json SHA-256 9cfc827095e43cd3…

Evaluation Contract: canonical_agentic_contract_v7.22.18:evaluation

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Comparability & Fixture Methodology

Identical Fixtures Across All Three Runs

Baseline reproducibility: The BF16 baseline was executed in all 3 runs. All eight family pass rates are identical across the runs, and at case level 0 of 1,600 cases differ in any pairwise comparison. Every quantized delta is measured against the BF16 lane of its own run.

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Per-Family Paired Comparison — BF16 Baseline vs. Q8_0

All rates include 95% Wilson score confidence intervals. McNemar p-values test statistical significance of paired differences. N = 200 cases per family.

Source: sealed quant_eval run bundles; full tables at pbhappliedsystems.com/models/ministral-3-14b-instruct-2512

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Key Findings

Finding 1: No family changes significantly

Implication: Q8_0 shows no statistically significant quantization effect in any of the eight families.

Finding 2: Largest movement without significance — mixed_brief_json (+0.030)

The 95% interval [−0.031, 0.090] includes zero and McNemar p = 0.1796: 4 cases pass only at BF16, 10 only at Q8_0. Within measurement noise at n = 200.

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Efficiency & Footprint

Source: sealed quant_eval run bundle (efficiency and throughput tables)

Hardware: both lanes executed on NVIDIA A100-40GB; ratio is a precision comparison.

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Quantization Variants Comparison

See how this model performs across all three quantized variants. All three were evaluated on identical fixtures against the same BF16 baseline.

How to read this table: Speed is the observed evaluation wall-time ratio (BF16 ÷ quantized) on matched A100-40GB hardware — not a controlled throughput benchmark. Stateful/Toolcall-only/Hybrid are pass rates.

See the full cards:

• BF16
• Q4_K_M
• Q5_K_M

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Artifact Provenance

Produced from mistralai/Ministral-3-14B-Instruct-2512-BF16 at revision 3cea74c1ebaf5ce5f5a2553de470e2ceab825142 (exact) using a custom-built llama.cpp conversion and quantization pipeline developed by PBH Applied Systems.

Artifact verification: the SHA-256 is recorded in the run manifest and checked by the evaluation runtime when the file is loaded. Download the GGUF and verify:

sha256sum ministral-3-14b-instruct-2512-gguf-Q8-0.gguf
# Should output: 6f81ceb2916fa2863de1ceed25678e9a0045118a8012a45770b0cd24af7547b8

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Evaluation Methodology

quant_eval v7.22.22 is a behavioral evaluation harness developed by PBH Applied Systems. Each run evaluates the full-precision (BF16) model and one quantized variant against the same fixture set, enabling direct measurement of behavioral degradation. Method: Whitepaper 1 (the harness) and Whitepaper 2 (the public corpus that validates it).

Fixture set: v7.22.22 evaluation split

• SHA256: 0673b5e5c0fb1b7ba4bdc8c9aa985104260db183453c5708aff14c87823edb97
• Total unique cases: 1,600 (200 per family × 8 families)

Evaluation hardware: NVIDIA A100-40GB
Runner: quantized_modal_llama_cpp (llama.cpp via Python binding on Modal)
Decoding: temperature 0.15, seed 42, top_p 0.95, top_k 40
Context: 8,192-token context window (llama.cpp configuration)
Reproducibility: sealed run bundles (checksums above); per-case results on request

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Deployment Recommendations

✅ Deploy Q8_0 when:

• You want BF16-level behavior at 14.36 GB. No family changed significantly.
• Latency matters. 1.39× observed wall-time ratio against BF16 on the same hardware.

⚠️ Avoid Q8_0 if:

• You want the smallest build without a significant change. Q5_K_M is 9.62 GB and also showed none.
• You need the reference baseline itself (audit or reproduction): use BF16.

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About PBH Applied Systems

PBH Applied Systems, LLC is an Oklahoma City–based applied machine learning and AI systems company specializing in production-grade model evaluation, quantization pipelines, agentic AI infrastructure, and scalable AI-driven application development. The organization emphasizes engineering rigor, reproducibility, and real-world deployment constraints — particularly in environments where performance, cost efficiency, and reliability must be balanced against available hardware and budget.

Founder & Principal AI/ML Systems Architect — Patrick Hill

Patrick Hill is the Founder & Principal AI/ML Systems Architect of PBH Applied Systems with 10+ years of experience delivering advanced analytics, predictive modeling, and decision-support solutions across high-stakes operational environments. Patrick holds a Master of Science in Software Engineering with concentrations in Artificial Intelligence and Machine Learning and a B.S. in Business Finance.

Technical expertise spans: Python, SQL, Linux, Pandas, NumPy, scikit-learn, PyTorch, TensorFlow/Keras, HuggingFace Transformers, GGUF, llama.cpp, BitsAndBytes, PEFT, QLoRA, Flask APIs, Docker, CI/CD, Jupyter, Databricks, and quantization strategies.

Published Author: Patrick is the author of Applied Machine Learning: Concepts, Tools, and Case Studies — a 1,200+ page practitioner-oriented textbook adopted as required reading for CSC 373 – Machine Learning at the University of Advancing Technology.

Core Service Areas: LLM optimization & deployment · AI evaluation frameworks · Agentic AI infrastructure · Scalable AI application development · ML pipeline design & analytics · Model & agent cataloging.

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🔬 About quant_eval & This Evaluation Series

quant_eval is a behavioral evaluation harness developed by PBH Applied Systems, LLC. It measures real agent-adjacent task performance across structured output, tool dispatch, multi-turn state retention, and multi-step planning — not perplexity or leaderboard proxies. Every model published under pbhappliedsystems has been independently evaluated using quant_eval before being recommended for any production role.

See it in action: Live AI Agent Demo →

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License

This GGUF repository inherits the license of the base model: Apache-2.0 — mistralai/Ministral-3-14B-Instruct-2512-BF16

The quant_eval evaluation harness, fuzz prompt builder, and scoring implementation are proprietary to PBH Applied Systems, LLC and are not included in this repository. The golden oracle fixture set used in this evaluation is published under CC BY 4.0 as part of the quant_eval Public Corpus (D3, DOI 10.5281/zenodo.22010278).

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GGUF conversion, quantization, and behavioral evaluation performed by PBH Applied Systems, LLC · quant_eval v7.22.22 · Run ID: Ministral_3_14B_Instruct_2512_20261002_211552