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tianxinwei
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Model Details
Model Provider
tianxinwei
Model Tree
Qwen/Qwen3.8-27B
Input Modalities
Output Modalities
Supported Functionality
Use this checkpoint for bounded classification, routing, ranking, tool choice, agent actions, and confidence-aware decisions. It accepts choice, noul, and ordinal score questions through the JevAny API. It is not a chat or chain-of-thought model.
The adapter and pointer head were trained on 107,278 records spanning preferences, agent and tool decisions, hard and many-choice reasoning, classification, policy, and native image and video evidence. A separate calibration partition sets the inference temperature. The base weights remain frozen.
| Evaluation | Result |
|---|---|
| v2 development accuracy | 90.34% |
| v2 development NLL | 0.265 |
| transfer-v9 accuracy | 82.41% |
| MMLU-Pro accuracy | 73.0% |
| AI2D accuracy | 86.0% |
| MMMU accuracy | 68.0% |
Development metrics exclude a 100-question single-class VideoFeedback slice. It exercises the native video path but is not a meaningful capability benchmark. AI2D and MMMU use native images through the backbone's vision path. Full measurements and the Jev comparison are in the repository release results.
The released path accepts text, JSON-renderable state, native images, and native video. Multimodal requests currently contain one isolated question and use a bounded visual token budget. HTTP media is operator opt-in through a controlled local root; network media URLs are rejected. The trained text context envelope is 2,048 packed tokens. Calibration can shift under new data, so validate thresholds on a deployment-specific calibration set. The adapter requires the separately distributed Qwen base weights and JevAny runtime.