{
    "bomFormat": "CycloneDX",
    "specVersion": "1.6",
    "serialNumber": "urn:uuid:0fd73414-9e65-47c5-8598-9745c0e73210",
    "version": 1,
    "metadata": {
        "timestamp": "2025-06-05T09:41:23.012895+00:00",
        "component": {
            "type": "machine-learning-model",
            "bom-ref": "OpenGVLab/InternVL-Chat-V1-5-c62ebeb8-2b6a-572f-945a-ebc349c329f4",
            "name": "OpenGVLab/InternVL-Chat-V1-5",
            "externalReferences": [
                {
                    "url": "https://huggingface.co/OpenGVLab/InternVL-Chat-V1-5",
                    "type": "documentation"
                }
            ],
            "modelCard": {
                "modelParameters": {
                    "task": "image-text-to-text",
                    "architectureFamily": "internvl_chat",
                    "modelArchitecture": "InternVLChatModel"
                },
                "properties": [
                    {
                        "name": "library_name",
                        "value": "transformers"
                    },
                    {
                        "name": "base_model",
                        "value": "OpenGVLab/InternViT-6B-448px-V1-5, internlm/internlm2-chat-20b"
                    },
                    {
                        "name": "base_model_relation",
                        "value": "merge"
                    }
                ]
            },
            "authors": [
                {
                    "name": "OpenGVLab"
                }
            ],
            "licenses": [
                {
                    "license": {
                        "id": "MIT",
                        "url": "https://spdx.org/licenses/MIT.html"
                    }
                }
            ],
            "description": "<p align=\"center\"><img src=\"/static-proxy?url=https%3A%2F%2Fcdn-uploads.huggingface.co%2Fproduction%2Fuploads%2F64119264f0f81eb569e0d569%2FD60YzQBIzvoCvLRp2gZ0A.jpeg%5C" alt=\"Image Description\" width=\"300\" height=\"300\"></p>> _Two interns holding hands, symbolizing the integration of InternViT and InternLM._We introduce InternVL 1.5, an open-source multimodal large language model (MLLM) to bridge the capability gap between open-source and proprietary commercial models in multimodal understanding.We introduce three simple designs:1. **Strong Vision Encoder:** we explored a continuous learning strategy for the large-scale vision foundation model---InternViT-6B, boosting its visual understanding capabilities, and making it can be transferred and reused in different LLMs.2. **Dynamic High-Resolution:** we divide images into tiles ranging from 1 to 40 of 448 \u00d7 448 pixels according to the aspect ratio and resolution of the input images, which supports up to 4K resolution input during inference.3. **High-Quality Bilingual Dataset:** we carefully collected a high-quality bilingual dataset that covers common scenes, document images, and annotated them with English and Chinese question-answer pairs, significantly enhancing performance in OCR- and Chinese-related tasks.",
            "tags": [
                "transformers",
                "tensorboard",
                "safetensors",
                "internvl_chat",
                "feature-extraction",
                "internvl",
                "custom_code",
                "image-text-to-text",
                "conversational",
                "multilingual",
                "arxiv:2312.14238",
                "arxiv:2404.16821",
                "arxiv:2410.16261",
                "arxiv:2412.05271",
                "base_model:OpenGVLab/InternViT-6B-448px-V1-5",
                "base_model:merge:OpenGVLab/InternViT-6B-448px-V1-5",
                "base_model:internlm/internlm2-chat-20b",
                "base_model:merge:internlm/internlm2-chat-20b",
                "license:mit",
                "region:us"
            ]
        }
    }
}