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

microsoft_wham.json

@@ -0,0 +1,78 @@
+{
+    "bomFormat": "CycloneDX",
+    "specVersion": "1.6",
+    "serialNumber": "urn:uuid:095669c7-1c4c-4d52-b0fa-e4d58743c8a8",
+    "version": 1,
+    "metadata": {
+        "timestamp": "2025-06-05T09:36:51.899012+00:00",
+        "component": {
+            "type": "machine-learning-model",
+            "bom-ref": "microsoft/wham-71a5e7b8-7423-56f5-b20f-63ea61b99a34",
+            "name": "microsoft/wham",
+            "externalReferences": [
+                {
+                    "url": "https://huggingface.co/microsoft/wham",
+                    "type": "documentation"
+                }
+            ],
+            "modelCard": {
+                "properties": [
+                    {
+                        "name": "library_name",
+                        "value": "wham"
+                    }
+                ],
+                "consideration": {
+                    "useCases": "This model and accompanying code are intended for academic research purposes only. WHAM has been trained on gameplay data from a single game, Bleeding Edge, and is intended to be used to generate plausible gameplay sequences resembling this game.The model is not intended to be used to generate imagery outside of the game Bleeding Edge. Generated images include watermark and provenance metadata. Do not remove the watermark or provenance metadata..WHAM can be used in multiple scenarios. The following list illustrates the types of tasks that WHAM can be used for:- World Model: Visuals are predicted, given a real starting state and action sequence.- Behaviour Policy: Given visuals, the model predicts the next controller action.- Full Generation: The model generates both the visuals and the controller actions a human player might take in the game."
+                },
+                "modelParameters": {
+                    "datasets": [
+                        {
+                            "ref": "microsoft/bleeding-edge-gameplay-sample-28eccfa4-1a5d-5597-8ab8-8591ca1e6cb9"
+                        }
+                    ]
+                }
+            },
+            "authors": [
+                {
+                    "name": "microsoft"
+                }
+            ],
+            "tags": [
+                "wham",
+                "microsoft",
+                "en",
+                "dataset:microsoft/bleeding-edge-gameplay-sample",
+                "region:us"
+            ]
+        }
+    },
+    "components": [
+        {
+            "type": "data",
+            "bom-ref": "microsoft/bleeding-edge-gameplay-sample-28eccfa4-1a5d-5597-8ab8-8591ca1e6cb9",
+            "name": "microsoft/bleeding-edge-gameplay-sample",
+            "data": [
+                {
+                    "type": "dataset",
+                    "bom-ref": "microsoft/bleeding-edge-gameplay-sample-28eccfa4-1a5d-5597-8ab8-8591ca1e6cb9",
+                    "name": "microsoft/bleeding-edge-gameplay-sample",
+                    "contents": {
+                        "url": "https://huggingface.co/datasets/microsoft/bleeding-edge-gameplay-sample"
+                    },
+                    "governance": {
+                        "owners": [
+                            {
+                                "organization": {
+                                    "name": "microsoft",
+                                    "url": "https://huggingface.co/microsoft"
+                                }
+                            }
+                        ]
+                    },
+                    "description": "This dataset contains 1024 60 second video clips of Bleeding Edge gameplay (75GB). The data has already been processed into the following format: 300x180 videos sampled at 10 fps.\n\n\t\n\t\t\n\t\tDataset Structure\n\t\n\n\n\t\n\t\t\n\t\tData Files\n\t\n\n\ntesting_dataset_part1.zip & testing_dataset_part2.zip \u2013 Contains all 1024 60 second trajectories used for our evaluation.\n\n4 examples from the dataset:\n\nFB[\u2026].npz \u2013 .npz file (described below)\nFB[\u2026].mp4 \u2013 60 seconds .mp4 video of the images from the .npz file.\u2026 See the full description on the dataset page: https://huggingface.co/datasets/microsoft/bleeding-edge-gameplay-sample."
+                }
+            ]
+        }
+    ]
+}