rlcd-modernbert-151m

모델 소개

**OpenJev (Verdict)** is an open-source, post-trained foundational decision model built for structured software workflows, inspired by **TypeSafe AI's Jev** and **Reinforcement Learning for Calibrated Decisions (RLCD)**. It provides calibrated semantic judgments (discrete choices, ordinal scores, and binary probabilities) in a single forward pass without conversational text generation. - **GitHub Repository**: https://github.com/Heman10x-NGU/Verdict-open-jev - **Base Architecture**: ModernBERT-base (`knowledgator/gliclass-modern-base-v2.0`, 151,378,177 parameters) - **Logit Capacity**: 25 candidate slots (24 substantive options + 1 explicit abstention slot) 1. **Non-Autoregressive Single Pass**: Evaluates all candidate options simultaneously in a single forward pass (< 35ms latency) without token generation loops. 2. **Proper Scoring Calibration**: Trained with composite Cross-Entropy + Brier Score loss: $$\mathcal{L}_{\text{total}} = \mathcal{L}_{\text{CE}} + 1.0 \times \mathcal{L}_{\text{Brier}}$$ followed by post-hoc L-BFGS temperature scaling ($T = 1.0716$). 3. **Explicit Abstention Route**: Dedicated `__insufficient_evidence__` candidate slot ensures calibrated rejection on out-of-distribution or insufficient context queries. 4. **Edge and In-Browser WebGPU**: Runs locally in browsers via WebGPU/WASM and on servers via PyTorch/ONNX Runtime.

주요 특징

特点摘要待依据来源补齐，不凭模型名称推断能力。

한계 및 유의 사항

구체적 한계를 판단할 출처가 부족합니다. 실제 성능, 지연, 데이터 정책, 라이선스와 버전 수명을 확인하세요.

출처별 요금 및 비용 분석

요금은 채널, 지역, 설정에 따라 다릅니다. 각 채널 요금은 개별 표시되며 제공사의 통일 공식 요금이 아닙니다.

공개 평가 및 실행 설정

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

자주 묻는 질문

출처 및 추적 기록

许可证：apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。