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

ByteDance-Seed_UI-TARS-7B-SFT.json

@@ -0,0 +1,62 @@
+{
+    "bomFormat": "CycloneDX",
+    "specVersion": "1.6",
+    "serialNumber": "urn:uuid:3055ead6-57c6-4f53-b237-31c297a9a0db",
+    "version": 1,
+    "metadata": {
+        "timestamp": "2025-06-05T09:41:54.385491+00:00",
+        "component": {
+            "type": "machine-learning-model",
+            "bom-ref": "ByteDance-Seed/UI-TARS-7B-SFT-2563bff1-2155-555e-9ebd-c56f1c3410c5",
+            "name": "ByteDance-Seed/UI-TARS-7B-SFT",
+            "externalReferences": [
+                {
+                    "url": "https://huggingface.co/ByteDance-Seed/UI-TARS-7B-SFT",
+                    "type": "documentation"
+                }
+            ],
+            "modelCard": {
+                "modelParameters": {
+                    "task": "image-text-to-text",
+                    "architectureFamily": "qwen2_vl",
+                    "modelArchitecture": "Qwen2VLForConditionalGeneration"
+                },
+                "properties": [
+                    {
+                        "name": "library_name",
+                        "value": "transformers"
+                    }
+                ]
+            },
+            "authors": [
+                {
+                    "name": "ByteDance-Seed"
+                }
+            ],
+            "licenses": [
+                {
+                    "license": {
+                        "id": "Apache-2.0",
+                        "url": "https://spdx.org/licenses/Apache-2.0.html"
+                    }
+                }
+            ],
+            "description": "UI-TARS is a next-generation native GUI agent model designed to interact seamlessly with graphical user interfaces (GUIs) using human-like perception, reasoning, and action capabilities. Unlike traditional modular frameworks, UI-TARS integrates all key components\u2014perception, reasoning, grounding, and memory\u2014within a single vision-language model (VLM), enabling end-to-end task automation without predefined workflows or manual rules.<!-- ![Local Image](figures/UI-TARS.png) --><p align=\"center\"><img src=\"https://github.com/bytedance/UI-TARS/blob/main/figures/UI-TARS-vs-Previous-SOTA.png?raw=true\" width=\"90%\"/><p><p align=\"center\"><img src=\"https://github.com/bytedance/UI-TARS/blob/main/figures/UI-TARS.png?raw=true\" width=\"90%\"/><p><!-- ![Local Image](figures/UI-TARS-vs-Previous-SOTA.png) -->This repository contains the model for the paper [UI-TARS: Pioneering Automated GUI Interaction with Native Agents](https://huggingface.co/papers/2501.12326).Code: https://github.com/bytedance/UI-TARS",
+            "tags": [
+                "transformers",
+                "safetensors",
+                "qwen2_vl",
+                "image-text-to-text",
+                "multimodal",
+                "gui",
+                "conversational",
+                "en",
+                "arxiv:2501.12326",
+                "license:apache-2.0",
+                "text-generation-inference",
+                "endpoints_compatible",
+                "region:us"
+            ]
+        }
+    }
+}