{
    "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_hf_20250321.json",
    "version": "1.1.0",
    "changelog": {
        "1.1.0": "enhanced metadata with improved descriptions, task specification, and intended use documentation",
        "1.0.0": "initial release of VILA_M3_3B model"
    },
    "monai_version": "1.4.0",
    "pytorch_version": "2.4.0",
    "numpy_version": "1.24.4",
    "required_packages_version": {
        "torch": "2.4.0",
        "huggingface_hub": "0.24.2",
        "transformers": "4.43.3"
    },
    "name": "Llama3-VILA-M3-3B",
    "task": "Medical Visual Language Understanding and Generation",
    "description": "VILA-M3 is a medical visual language model built on Llama 3 and VILA architecture. This 3B parameter model performs medical image analysis including segmentation, classification, visual question answering, and report generation across multiple imaging modalities.",
    "authors": "Vishwesh Nath, Wenqi Li, Dong Yang, Andriy Myronenko, et al. from NVIDIA, SingHealth and NIH",
    "copyright": "NVIDIA",
    "data_source": "MONAI and specialized medical datasets",
    "data_type": "Medical images and text",
    "image_classes": "Various medical imaging modalities",
    "huggingface_model_id": "MONAI/Llama3-VILA-M3-3B",
    "huggingface_url": "https://huggingface.co/MONAI/Llama3-VILA-M3-3B",
    "intended_use": "Research in medical vision-language tasks",
    "references": [
        "Nath, Vishwesh, et al. 'VILA_M3: Enhancing Vision-Language Models with Medical Expert Knowledge', arXiv preprint arXiv:2411.12915 (2025)."
    ]
}