| --- |
| license: apache-2.0 |
| library_name: pytorch |
| tags: |
| - decision-model |
| - multimodal |
| --- |
| |
| # WeMM-JEV |
|
|
| WeMM-JEV is a 27B multimodal decision model based on Qwen3.8-27B. |
| It supports choice, noul (yes/no) and score questions over text and multiple images, returning probabilities in a single forward pass per question. |
|
|
| - **Choice:** select among supplied options. |
| - **Noul:** estimate the probability of a yes/no proposition. |
| - **Score:** return a probability distribution over ordered levels. |
|
|
| ## Public Decision Index 0.3 |
|
|
| | Panel | Public index | |
| | --- | ---: | |
| | Text | 69.37 | |
| | Vision | 77.97 | |
|
|
| [Detailed evaluation results](https://huggingface.co/datasets/Rover00/WeMM-JEV-Results) |
|
|
| ## Usage |
|
|
| Install the dependencies in [inference/README.md](inference/README.md), then run |
| from your local model directory: |
|
|
| ```bash |
| cd /path/to/WeMM-JEV |
| python - <<'PY' |
| from inference import D3Engine |
| |
| engine = D3Engine(model=".", device="cuda:0") |
| response, _ = engine(state="The light is red.", questions={ |
| "color": {"type": "choice", "instructions": "What color is the light?", |
| "criteria": {"A": "Red", "B": "Blue"}} |
| }) |
| print(response) |
| engine.close() |
| PY |
| ``` |
|
|
| See [text and image examples](inference/README.md#python), |
| the [HTTP server](inference/README.md#server) and |
| [Decision Index integration](inference/README.md#decision-index). |
|
|
