add AIBOM

Dear laion,
We are a group of researchers investigating the usefulness of sharing AIBOMs (Artificial Intelligence Bill of Materials) to document AI models and to improve transparency in AI model supply chains. AIBOMs are machine-readable, structured inventories of components—such as datasets and models—used in the development of AI-powered systems.

We would like to emphasize that we have no financial or competing interests related to AIBOMs. Our sole interest is to advance the collective understanding of AIBOMs within both academia and industry. As part of this effort, we are contributing to randomly selected open and popular models on Hugging Face (like yours) and are happy to offer support to you and the maintainers of your model if needed.

Based on your model card (and some configuration information available in Hugging Face), we generated the AIBOM according to the CyclonDX (v1.6) standard (see https://cyclonedx.org/docs/1.6/json/). This AIBOM is generated as a JSON file 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). This tool is freely available online and can be downloaded and used at your own convenience. We are also happy to assist you directly if you need help generating or reviewing an AIBOM for your model.

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 generation tool.

We understand that initiatives like ours may raise questions, especially in open communities like Hugging Face. Therefore, we would like to further remark that our interest in AIBOMs is only to enhance the body of knowledge on AIBOMs and to make this easy and low-friction for maintainers of AI models and developers of AI-powered systems.

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

laion_CLIP-ViT-H-14-laion2B-s32B-b79K.json

@@ -0,0 +1,60 @@
+{
+    "bomFormat": "CycloneDX",
+    "specVersion": "1.6",
+    "serialNumber": "urn:uuid:d9fd9ecd-24d7-45fd-b99b-3387fdf9a4c8",
+    "version": 1,
+    "metadata": {
+        "timestamp": "2025-07-10T08:48:58.434970+00:00",
+        "component": {
+            "type": "machine-learning-model",
+            "bom-ref": "laion/CLIP-ViT-H-14-laion2B-s32B-b79K-1cc4816d-7985-5eff-adbc-1b0fca71fe35",
+            "name": "laion/CLIP-ViT-H-14-laion2B-s32B-b79K",
+            "externalReferences": [
+                {
+                    "url": "https://huggingface.co/laion/CLIP-ViT-H-14-laion2B-s32B-b79K",
+                    "type": "documentation"
+                }
+            ],
+            "modelCard": {
+                "modelParameters": {
+                    "task": "zero-shot-image-classification",
+                    "architectureFamily": "clip",
+                    "modelArchitecture": "CLIPModel"
+                },
+                "properties": [
+                    {
+                        "name": "library_name",
+                        "value": "open_clip"
+                    }
+                ],
+                "consideration": {
+                    "useCases": "As per the original [OpenAI CLIP model card](https://github.com/openai/CLIP/blob/d50d76daa670286dd6cacf3bcd80b5e4823fc8e1/model-card.md), this model is intended as a research output for research communities. We hope that this model will enable researchers to better understand and explore zero-shot, arbitrary image classification. We also hope it can be used for interdisciplinary studies of the potential impact of such model.The OpenAI CLIP paper includes a discussion of potential downstream impacts to provide an example for this sort of analysis. Additionally, the LAION-5B blog (https://laion.ai/blog/laion-5b/) and upcoming paper include additional discussion as it relates specifically to the training dataset."
+                }
+            },
+            "authors": [
+                {
+                    "name": "laion"
+                }
+            ],
+            "licenses": [
+                {
+                    "license": {
+                        "id": "MIT",
+                        "url": "https://spdx.org/licenses/MIT.html"
+                    }
+                }
+            ],
+            "description": "A CLIP ViT-H/14 model trained with the LAION-2B English subset of LAION-5B (https://laion.ai/blog/laion-5b/) using OpenCLIP (https://github.com/mlfoundations/open_clip).Model training done by Romain Beaumont on the [stability.ai](https://stability.ai/) cluster.",
+            "tags": [
+                "open_clip",
+                "pytorch",
+                "safetensors",
+                "clip",
+                "zero-shot-image-classification",
+                "arxiv:1910.04867",
+                "license:mit",
+                "region:us"
+            ]
+        }
+    }
+}