pplx-decider-v1-27b

pplx-decider-v1-27b is a decision model fine-tuned from Qwen3.8-27B.

Accuracy across 11 benchmarks. The pplx-decider-v1-27b results were measured through the Perplexity API.

Benchmark	Jev	Qwen3.8-27B	pplx-decider-v1-27b
WinoGrande	90.70%	73.10%	83.30%
FinancialPhraseBank	76.98%	75.68%	84.18%
RAGTruth	77.27%	61.53%	88.80%
JudgeBench	78.57%	68.86%	78.29%
BBH	94.27%	72.80%	82.80%
JevBench public hard	73.27%	72.28%	70.30%
TabFact	89.80%	78.60%	90.60%
ContractNLI	77.45%	80.78%	80.78%
Circa	84.60%	87.00%	89.20%
Belebele	95.00%	93.20%	94.00%
TruthfulQA binary	92.00%	82.80%	85.40%
Overall	84.51%	74.76%	85.71%

Bold marks the best score in each row.

Usage

Python 3.12+ and a CUDA GPU with room for approximately 49 GiB of weights plus working memory.

Download and run the inference example with uv:

uvx --from huggingface-hub hf download perplexity-ai/pplx-decider-v1-27b inference.py --local-dir .
uv run inference.py

uv installs the dependencies; the script downloads the model from Hugging Face. In an environment with these dependencies installed, use Decider directly:

from inference import Decider

model = Decider.from_pretrained("perplexity-ai/pplx-decider-v1-27b")
result = model.predict(
    "My Stripe integration keeps failing. Please help ASAP.",
    {
        "type": "choice",
        "instructions": "Which team should handle this request?",
        "criteria": {
            "billing": "Charges and refunds",
            "technical_support": "Integration errors",
            "sales": "Questions about buying a product",
        },
    },
)
print(result)  # Selected choice and calibrated probabilities.

Use {"type": "noul", "instructions": "Does this message express urgency?"} for a yes/no probability. For images, pass images=["screenshot.png"] to predict, or run:

uv run inference.py --image screenshot.png