README
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
Similar repos
README
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