Image Inference Examples
See the Deployment guide for environment setup, image request formats, response fields, limits, and error handling.
| File | Purpose |
|---|---|
example.py |
Run image-based decisions locally |
example_request.json |
Sample question; replace it with your own single-question request |
http_example.py |
Encode a local image and send it to the HTTP API |
predictor.py |
Import wrapper for the installed neohorse_decision.vision.VisionDecisionEngine |
base_vision_provenance.json |
Record the source of the vision weights |
verification.json |
Record basic vision smoke tests and consistency checks |
LICENSE |
License for the vision components |
The language and vision weights are stored together in backbone/. The decision head is stored in pointer_head.safetensors at the model root. The vision weights come from Qwen3.5-4B and have not undergone additional vision training. The hashes of separate vision files in the provenance record are for traceability only; those files do not need to be loaded separately at runtime.
After installing the matching runtime package, set MODEL_DIR to the complete downloaded model directory. You can then run these commands from any working directory:
CUDA_VISIBLE_DEVICES=0 python "$MODEL_DIR/vision/example.py" \
--model-dir "$MODEL_DIR" --image /path/to/image.png \
--request "$MODEL_DIR/vision/example_request.json"
python "$MODEL_DIR/vision/http_example.py" --image /path/to/image.png \
--base-url http://127.0.0.1:8080 --endpoint systemone
Each image request supports one static image and one question, using Noul, Choice, or Score. The basic vision verification records are not a general visual capability evaluation.