const taskData = {
    datasets: [
        {
            description: "Instructions composed of image and text.",
            id: "liuhaotian/LLaVA-Instruct-150K",
        },
        {
            description: "Collection of image-text pairs on scientific topics.",
            id: "DAMO-NLP-SG/multimodal_textbook",
        },
        {
            description: "A collection of datasets made for model fine-tuning.",
            id: "HuggingFaceM4/the_cauldron",
        },
        {
            description: "Screenshots of websites with their HTML/CSS codes.",
            id: "HuggingFaceM4/WebSight",
        },
    ],
    demo: {
        inputs: [
            {
                filename: "image-text-to-text-input.png",
                type: "img",
            },
            {
                label: "Text Prompt",
                content: "Describe the position of the bee in detail.",
                type: "text",
            },
        ],
        outputs: [
            {
                label: "Answer",
                content: "The bee is sitting on a pink flower, surrounded by other flowers. The bee is positioned in the center of the flower, with its head and front legs sticking out.",
                type: "text",
            },
        ],
    },
    metrics: [],
    models: [
        {
            description: "Small and efficient yet powerful vision language model.",
            id: "HuggingFaceTB/SmolVLM-Instruct",
        },
        {
            description: "Cutting-edge reasoning vision language model.",
            id: "zai-org/GLM-4.5V",
        },
        {
            description: "Cutting-edge small vision language model to convert documents to text.",
            id: "rednote-hilab/dots.ocr",
        },
        {
            description: "Small yet powerful model.",
            id: "Qwen/Qwen2.5-VL-3B-Instruct",
        },
        {
            description: "Image-text-to-text model with agentic capabilities.",
            id: "microsoft/Magma-8B",
        },
    ],
    spaces: [
        {
            description: "Leaderboard to evaluate vision language models.",
            id: "opencompass/open_vlm_leaderboard",
        },
        {
            description: "An application that compares object detection capabilities of different vision language models.",
            id: "sergiopaniego/vlm_object_understanding",
        },
        {
            description: "An application to compare different OCR models.",
            id: "prithivMLmods/Multimodal-OCR",
        },
    ],
    summary: "Image-text-to-text models take in an image and text prompt and output text. These models are also called vision-language models, or VLMs. The difference from image-to-text models is that these models take an additional text input, not restricting the model to certain use cases like image captioning, and may also be trained to accept a conversation as input.",
    widgetModels: ["zai-org/GLM-4.5V"],
    youtubeId: "IoGaGfU1CIg",
};
export default taskData;
