Downloadpplx-decider-v1-27b is Perplexity AI's open-weight decision model, fine-tuned from Qwen3.8-27B to perform structured classification and probabilistic decision-making across text and image inputs. Unlike conventional generative language models, it focuses on selecting answers from predefined choices or estimating the probability of binary outcomes. The model supports Choice decisions for multiclass classification and Noul decisions for yes/no judgments, returning calibrated probabilities that help applications evaluate confidence. It is designed for intent detection, request routing, factual verification, content moderation, document analysis, and automated decision workflows. Across 11 classification and reasoning benchmarks, it achieved 85.71% average accuracy, compared with 74.76% for its base model. Released under Apache 2.0, it supports local Python inference on CUDA GPUs with approximately 49 GiB of memory for model weights, plus additional working memory.
Features
- 27B-class model fine-tuned from Qwen3.8-27B
- Structured multiclass classification through Choice decisions
- Binary probability estimation through Noul decisions
- Calibrated probability outputs for confidence assessment
- Supports text and image-based classification
- 85.71% average accuracy across 11 evaluation benchmarks
- Local CUDA inference with approximately 49 GiB for model weights
- Apache 2.0 license for commercial and research applications
Project Samples
