nv-reason-cxr

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Translated
2 scripts

Used for command-shape or live NV-Reason-CXR chest X-ray reasoning smoke tests. Not for diagnosis or clinical reporting.

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Sourcenvidia/skills
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NPX Install

npx skill4agent add nvidia/skills nv-reason-cxr

Tags

Translated version includes tags in frontmatter
nv-reason-cxrchest-xray-aimedical-aihugging-face-transformerssmoke-testing

SKILL.md Content

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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; keep outputs under a caller-provided run directory.
  • 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.

Available Scripts

ScriptPurposeArguments
scripts/run_nv_reason_cxr.py
Primary entrypoint declared by skill_manifest.yaml.
PATH_TO_CXR_OR_FIXTURE --out-dir OUT_DIR [--mock] [--check-setup]

Prerequisites

  • Runtime requirements: GPU/CUDA when declared by the manifest; Python packages listed in 
    runtime.side_effects.pip_packages
    .
  • Side effects: writes JSON outputs under the caller's 
    --out-dir
    , may cache model assets under 
    ~/.cache/huggingface/
    , and may contact 
    https://huggingface.co
    or 
    https://github.com
    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.
  • Not for clinical deployment, clinical interpretation, autonomous diagnosis, treatment decisions.

Troubleshooting

ErrorCauseFix
Missing dependency or import errorRuntime package drift from 
skill_manifest.yaml
.		Install the packages declared in the manifest or use the documented setup command.
Empty or schema-invalid outputWrong input path, unsupported modality, or upstream failure.Re-run with a known fixture and inspect the wrapper JSON plus stderr.
Validation gate failureOutput violated a declared engineering invariant.Keep the failed evidence pack and use the gate message to repair inputs or wrapper code.
Runs NVIDIA-Medtech 
NV-Reason-CXR-3B
 for chest X-ray image interpretation through the documented Hugging Face Transformers inference path. 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:
bash
python skills/nv-reason-cxr/scripts/run_nv_reason_cxr.py PATH_TO_CXR_OR_FIXTURE --mock --out-dir OUT_DIR
For live image inference, omit 
--mock
only when the user asks for live model inference. 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

Install the inference dependencies in the environment that will run the skill:
bash
pip install torch==2.7.1 torchvision==0.22.1 transformers==4.56.1 Pillow
The model weights are loaded from 
nvidia/NV-Reason-CXR-3B
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.
Check the local environment before downloading weights or running inference:
bash
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:
VariableWhen to use
MOCK_NV_REASON_CXR
Set to 
1
for deterministic command-shape smoke tests without model inference.
NV_REASON_CXR_MODEL
Override the Hugging Face model id only for compatibility probes.
HF_HOME
Point at a pre-populated Hugging Face cache.
HF_TOKEN
Authenticate model downloads when required by the local environment.
TRANSFORMERS_OFFLINE
Set to 
1
only after weights are already cached.
HF_HUB_OFFLINE
Set to 
1
only after Hugging Face assets are already cached.

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:
bash
python skills/nv-reason-cxr/scripts/run_nv_reason_cxr.py PATH_TO_CXR.png \
  --prompt "Find abnormalities and support devices." \
  --out-dir runs/nv_reason_cxr_case
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, including 
output.response_text
, to verify the run. The direct runnable surface is:
bash
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 
output.response_text
. Use 
--mock
only for command-shape smoke tests or fixtures that request mock mode; omit 
--mock
for live model inference.
For JPEG input:
bash
python skills/nv-reason-cxr/scripts/run_nv_reason_cxr.py PATH_TO_CXR.jpg \
  --prompt "Describe the chest X-ray findings." \
  --out-dir runs/nv_reason_cxr_case
Flags:
  • --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.
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. It 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.

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:
bash
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.