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

Acknowledgment

Our data pipeline builds on Bespoke Nimble by Bespoke Labs. The data builder in source/ uses Nimble's converters to turn human-labelled public datasets into Choice and Noul decisions. It also uses Nimble's published benchmark subsets, pinned by record ID and checksum, to keep those benchmark records and related examples out of training. Nimble is downloaded at a pinned revision when the data is built and is not redistributed here. If you use this part of the pipeline, please also cite Nimble:

@misc{nimble2026,
  author = {{Bespoke Labs} and Sathiamoorthy, Maheswaran},
  title = {Nimble},
  year = {2026},
  howpublished = {\url{https://github.com/bespokelabsai/nimble}},
  note = {Model: https://huggingface.co/bespokelabs/Bespoke-Nimble-9B}
}