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

Runs NVIDIA-Medtech NV-Reason-CXR-3B for chest X-ray image interpretation through either the documented local Hugging Face Transformers inference path or the public Hugging Face Space API. The wrapper does not reimplement the model, image preprocessing, or decoding.

Exact Runnable Surface

For command-shape smoke tests and JSON fixtures, use this repo-root wrapper path exactly:

python skills/nv-reason-cxr/scripts/run_nv_reason_cxr.py PATH_TO_CXR_OR_FIXTURE --mock --out-dir OUT_DIR

For local live image inference, omit --mock only when the user asks for live model inference. Local is the default backend:

python skills/nv-reason-cxr/scripts/run_nv_reason_cxr.py PATH_TO_CXR_OR_FIXTURE \
  --prompt "Find abnormalities and support devices." \
  --backend local

For public API inference without local model packages, use:

python skills/nv-reason-cxr/scripts/run_nv_reason_cxr.py PATH_TO_CXR_OR_FIXTURE \
  --prompt "Find abnormalities and support devices." \
  --backend hf-space-api

Do not invent Medical AI Skills run, eval_engine/run.py, infer.py, or python -m nv_reason_cxr commands for ordinary user runs.

Preconditions

For --backend local, install the inference dependencies in the environment that will run the skill:

pip install torch==2.7.1 torchvision==0.22.1 transformers==4.56.1 Pillow

The model weights and remote model code are loaded from nvidia/NV-Reason-CXR-3B revision 056bd0383b35226554da9dc5866e095df174ae19 through Transformers. They may download to the Hugging Face cache on first use. Set TRANSFORMERS_OFFLINE=1 or pass --local-files-only only after the weights are already cached.

CUDA is expected for practical inference. CPU execution may work for small tests but is slow and must be requested explicitly.

For --backend hf-space-api, no local PyTorch, Transformers, CUDA, model cache, or Hugging Face token is required. The backend sends the image and prompt to the public nvidia/nv-reason-cxr Hugging Face Space.

Check the local environment before downloading weights or running inference:

python skills/nv-reason-cxr/scripts/run_nv_reason_cxr.py --check-setup

The setup report checks importable dependencies, CUDA visibility, Hugging Face cache state, and the recommended next step.

Operational environment variables:

Prompt Routing

Choose both the model prompt and the user-facing output mode before running the wrapper. Routing order matters: exact model-prompt requests use pass-through/raw-only mode first; otherwise report-generation requests take precedence over general analysis and specific-question routing.

Use pass-through/raw-only mode only when the user explicitly asks to send an exact prompt to the model, such as "call the model with this prompt exactly: ...". Pass only that exact model prompt as --prompt.

Use abnormality-analysis mode when the user asks to analyze, examine, or find abnormalities in a chest X-ray. Treat local image paths, uploaded filenames, backend choices such as "use API" or "use local", output delivery instructions, and other agent orchestration text as wrapper instructions, not model prompt content. Do not include local filesystem paths, backend names, or "use API" in --prompt unless the user explicitly asks to send that exact text to the model. For ordinary abnormality-finding requests, use the documented prompt, usually --prompt "Find abnormalities and support devices.", with the requested backend.

Use report-generation/two-call mode if the user asks to write, create, or generate a structured report, chest X-ray report, radiology report, or report. If sufficient raw model context for the same image is already available, especially output from Find abnormalities and support devices., skip the context-gathering call. Otherwise first run the wrapper with --prompt "Examine the chest X-ray." to gather context, but do not show that first call. Then run the wrapper again with a multi-turn transcript prompt:

User: Find abnormalities and support devices.

Assistant:
<raw model context>

User: Write a structured report.

Treat the second call as the completed run.

Use default-prompt/context-answer mode when the user asks a specific question about a finding, such as presence, count, location, or characterization, or mixes general analysis with specific questions. Run the wrapper with --prompt "Find abnormalities and support devices." before answering the original question in plain text prefixed exactly with Answer:. Base the answer only on the raw model output context and the image.

Follow-up Handling

For follow-up questions about an image already analyzed in the conversation, reuse prior raw model context when it is sufficient. For report follow-ups, use report-generation/two-call mode and skip directly to the second model call if there is sufficient context. If prior context is insufficient and the same image path or image bytes are available, call the wrapper again using the prompt routing rules above. If the image is no longer available, ask the user to reattach it.

For long multi-turn prompts that include prior raw model output, prefer a quoted Bash here-doc variable so XML-like tags, apostrophes, quotes, and newlines are preserved:

IFS= read -r -d '' prompt <<'PROMPT'
User: Examine the chest X-ray.

Assistant:
<raw model context>

User: Write a structured report.
PROMPT

python skills/nv-reason-cxr/scripts/run_nv_reason_cxr.py PATH_TO_CXR.png \
  --prompt "$prompt" \
  --backend hf-space-api

Use IFS= read -r -d '' prompt <<'PROMPT', not command substitution, for long pasted transcripts.

License

The upstream repository code is Apache-2.0. The model weights are released under the NVIDIA OneWay Noncommercial License Agreement. Users are responsible for complying with the model-weight terms before live inference.

Usage

From Medical AI Skills repo root:

python skills/nv-reason-cxr/scripts/run_nv_reason_cxr.py PATH_TO_CXR.png \
  --prompt "Find abnormalities and support devices." \
  --backend local

For public API inference without installing model packages locally:

python skills/nv-reason-cxr/scripts/run_nv_reason_cxr.py PATH_TO_CXR.png \
  --prompt "Find abnormalities and support devices." \
  --backend hf-space-api

For user requests that include local path or backend instructions, keep those instructions out of the model prompt:

User request: find abnormalities in ~/Desktop/363.jpg (use API)

python skills/nv-reason-cxr/scripts/run_nv_reason_cxr.py ~/Desktop/363.jpg \
  --prompt "Find abnormalities and support devices." \
  --backend hf-space-api

Use the wrapper script directly for agent-generated commands. Do not replace it with eval_engine/run.py unless the user explicitly asks to run the eval harness. Do not redirect stdout with > in generated commands: callers and the eval harness read the wrapper's stdout JSON to verify the run. The direct runnable surface is:

python skills/nv-reason-cxr/scripts/run_nv_reason_cxr.py PATH_TO_CXR_OR_FIXTURE \
  --mock \
  --out-dir runs/nv_reason_cxr_case

PATH_TO_CXR_OR_FIXTURE may be a PNG/JPEG image or a JSON fixture. If the user provides a JSON request such as runs/.../synthetic_cxr_input.json, pass that exact JSON path as the first argument. The script will load generated://synthetic_chest_xray fixtures, create the temporary PNG under the output directory, and emit JSON with the model response. Use --mock only for command-shape smoke tests or fixtures that request mock mode; omit --mock for live model inference.

For JPEG input:

python skills/nv-reason-cxr/scripts/run_nv_reason_cxr.py PATH_TO_CXR.jpg \
  --prompt "Describe the chest X-ray findings." \
  --backend local

Flags:

• --backend local|hf-space-api — inference backend, default local.

• --model-id — Hugging Face model id, default nvidia/NV-Reason-CXR-3B.

• --device auto|cuda|cpu — default auto, using CUDA when available.

• --allow-cpu — required for live CPU inference; CPU runs can be very slow.

• --torch-dtype auto|float16|bfloat16|float32 — default auto, using bfloat16 on CUDA and float32 on CPU, matching the published BF16 model.

• --max-new-tokens — generation cap, default 2048.

• --local-files-only — use only locally cached Hugging Face assets.

• --mock — deterministic dry-run response for CI and wiring checks.

• --prompt-preset findings|comprehensive|educational|structured — optional known-good prompt presets from the model card/demo behavior.

• --out-dir — optional artifact directory. Required for generated JSON fixtures; the eval harness passes it explicitly.

The tested local live path uses:

• AutoModelForImageTextToText.from_pretrained(..., dtype=torch.bfloat16).eval().to("cuda")

• AutoProcessor.from_pretrained(..., use_fast=True)

• PNG/JPEG image input plus one text prompt

• max_new_tokens=2048 by default

The script emits JSON on stdout and writes no clinical report files. Direct PNG/JPEG runs do not create a default output directory. Generated JSON fixtures require --out-dir for the temporary synthetic image. The result JSON records input image metadata, prompt, model id, runtime mode, response text, and known limitations. If runtime.truncated_by_max_new_tokens is true, rerun with a higher --max-new-tokens value.

Reporting reminder: for both local and hf-space-api backends, follow the completed-run rule in Instructions.

The hf-space-api backend calls the fixed public Hugging Face Space at https://nvidia-nv-reason-cxr.hf.space with a 300 second HTTP timeout.

Fixture Smoke Test

The committed fixture uses a generated synthetic PNG and mock mode so the eval harness can verify the wrapper without downloading weights:

python eval_engine/run.py skills/nv-reason-cxr \
  --fixture skills/nv-reason-cxr/fixtures/synthetic_cxr_input.json \
  --out runs/nv_reason_cxr_smoke

Limits

This is research and engineering tooling only. It is not validated for clinical diagnosis, treatment decisions, triage, patient-facing reporting, or regulatory use. Model outputs can hallucinate, miss subtle findings, or overstate uncertainty. A qualified professional must review any use in a medical workflow.