NV-Reason-CXR

NV-Reason-CXR

Purpose

• Used for command-shape or live NV-Reason-CXR chest X-ray reasoning smoke tests. Not for diagnosis or clinical reporting.
• Use the wrapper exactly as documented; do not replace the upstream entrypoint with a handwritten implementation.
• Manifest I/O: inputs are chest_xray_image_or_fixture; outputs are result_json.

Instructions

• Read skill_manifest.yaml before changing arguments, side effects, or validation gates.
• Run scripts/run_nv_reason_cxr.py through the documented command below; pass --out-dir only for generated fixtures or harness-managed artifact directories.
• If a host agent exposes run_script, use run_script("scripts/run_nv_reason_cxr.py", args=[...]); otherwise run the Bash/Python command shown below.
• Check the emitted JSON and paired verifier guidance before treating the run as evidence.
• When reporting a completed run, return the full wrapper JSON or at minimum the complete output.response_text exactly as emitted, including any model-generated <think>...</think> and <answer>...</answer> sections. Do not collapse the result to labels unless the user explicitly asks for a summary.

Available Scripts

Prerequisites

• Local backend requirements: GPU/CUDA when declared by the manifest; Python packages listed in runtime.side_effects.pip_packages.
• API backend requirements: public network access to the Hugging Face Space; no local PyTorch, Transformers, CUDA, model cache, or Hugging Face token.
• Side effects: emits result JSON on stdout; may write generated fixture artifacts under the caller's --out-dir; may cache model assets under ~/.cache/huggingface/ for local inference; and may contact https://huggingface.co, https://github.com, or https://*.hf.space outside --mock mode.
• Run commands from the repository root unless an existing section below says otherwise.

Limitations

• This is a thin wrapper. Image preprocessing, model inference, and decoding are delegated to Hugging Face Transformers and the NV-Reason-CXR-3B model.
• Output is not a diagnosis, clinical report, treatment recommendation, or triage decision. It is engineering evidence and must be reviewed by a qualified professional before any medical use.
• The model may hallucinate findings, miss subtle abnormalities, misread support devices, or produce overconfident prose.
• The committed fixture uses a generated synthetic PNG and deterministic mock response so CI can verify wrapper behavior without downloading model weights. Mock mode is not a substitute for model inference.
• The hf-space-api backend depends on public Hugging Face Space availability and API compatibility.
• Not for clinical deployment, clinical interpretation, autonomous diagnosis, treatment decisions.

Troubleshooting