NV Reason CXR 3B EU AI Act Compliance Profile

NV-Reason-CXR-3B Overview

Description:

NV-Reason-CXR-3B is a specialized vision-language model designed for medical reasoning and interpretation of chest X-ray images, with detailed explanations. The model combines visual understanding with medical reasoning capabilities, enabling healthcare professionals to access comprehensive analyses and engage in follow-up discussions about radiological findings. NV-Reason-CXR-3B provides step-by-step reasoning that mirrors clinical thinking patterns, making it valuable for educational and research applications in medical imaging.

This model is for research and development only.

💻 [Github code]
🩻 [Web Demo]

Quick start

import torch
from transformers import AutoModelForImageTextToText, AutoProcessor
from PIL import Image


# Load the model 
model_name = "nvidia/NV-Reason-CXR-3B"
model = AutoModelForImageTextToText.from_pretrained(
    model_name,
    torch_dtype=torch.float16,
).eval().to("cuda")

processor = AutoProcessor.from_pretrained(model_name)

# Load chest x-ray image
image = Image.open("chest_xray.png")

# Prepare input with clinical context
messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "image": image,
            },
            {
                "type": "text",
                "text": "Find abnormalities and support devices."
            }
        ]
    }
]


# Create prompt using chat template
text = processor.apply_chat_template(messages, add_generation_prompt=True)

# Process inputs
inputs = processor(text=text, images=[image], return_tensors="pt")
inputs = inputs.to(model.device)

# Generate 
generated_ids = model.generate(**inputs,  max_new_tokens=2048)

# Trim and decode
trimmed_generated_ids = [
    out_ids[len(in_ids):]
    for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
generated_te

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