Model overview
**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.
Key features
特点摘要待依据来源补齐，不凭模型名称推断能力。
Limitations & considerations
Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.
Source prices & cost analysis
Prices vary by channel, region and configuration. Channel quotes are listed independently and are not treated as a single official provider price.
| Source / channel | Input | Output | Billing unit | Collected at |
|---|---|---|---|---|
| No verified prices yet. Check the source channel. | ||||
Estimate monthly base token costs
Estimates exclude cache discounts, tiered pricing, multimodal surcharges, web search, taxes and additional reasoning output.
Public benchmarks & run configuration
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。
View full indexed rankings →Frequently asked questions
Do open weights permit free commercial use?
Not necessarily. Check the version license, commercial conditions and usage policies. Self-hosting incurs compute and maintenance costs.
Are these real-time billing prices?
These are timestamped source snapshots, not live billing prices. Check the channel for current versions, regions, discounts and billing methods.
Sources & traceability
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。