add AIBOM

Dear model owner(s),
We are a group of researchers investigating the usefulness of sharing AIBOMs (Artificial Intelligence Bill of Materials) to document AI models – AIBOMs are machine-readable structured lists of components (e.g., datasets and models) used to enhance transparency in AI-model supply chains.

To pursue the above-mentioned objective, we identified popular models on HuggingFace and, based on your model card (and some configuration information available in HuggingFace), we generated your AIBOM according to the CyclonDX (v1.6) standard (see https://cyclonedx.org/docs/1.6/json/). AIBOMs are generated as JSON files by using the following open-source supporting tool: https://github.com/MSR4SBOM/ALOHA (technical details are available in the research paper: https://github.com/MSR4SBOM/ALOHA/blob/main/ALOHA.pdf).

The JSON file in this pull request is your AIBOM (see https://github.com/MSR4SBOM/ALOHA/blob/main/documentation.json for details on its structure).

Clearly, the submitted AIBOM matches the current model information, yet it can be easily regenerated when the model evolves, using the aforementioned AIBOM generator tool.

We open this pull request containing an AIBOM of your AI model, and hope it will be considered. We would also like to hear your opinion on the usefulness (or not) of AIBOM by answering a 3-minute anonymous survey: https://forms.gle/WGffSQD5dLoWttEe7.

Thanks in advance, and regards,
Riccardo D’Avino, Fatima Ahmed, Sabato Nocera, Simone Romano, Giuseppe Scanniello (University of Salerno, Italy),
Massimiliano Di Penta (University of Sannio, Italy),
The MSR4SBOM team

OpenGVLab_InternVL-Chat-V1-5.json

@@ -0,0 +1,77 @@
+{
+    "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=\"https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/D60YzQBIzvoCvLRp2gZ0A.jpeg\" 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"
+            ]
+        }
+    }
+}