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

Qwen_Qwen3-4B.json

@@ -0,0 +1,67 @@
+{
+    "bomFormat": "CycloneDX",
+    "specVersion": "1.6",
+    "serialNumber": "urn:uuid:f24b7da0-02aa-40b9-97d8-8c76756a806e",
+    "version": 1,
+    "metadata": {
+        "timestamp": "2025-06-05T09:37:34.037173+00:00",
+        "component": {
+            "type": "machine-learning-model",
+            "bom-ref": "Qwen/Qwen3-4B-d504bcdf-8093-598c-937c-206e9c62b7df",
+            "name": "Qwen/Qwen3-4B",
+            "externalReferences": [
+                {
+                    "url": "https://huggingface.co/Qwen/Qwen3-4B",
+                    "type": "documentation"
+                }
+            ],
+            "modelCard": {
+                "modelParameters": {
+                    "task": "text-generation",
+                    "architectureFamily": "qwen3",
+                    "modelArchitecture": "Qwen3ForCausalLM"
+                },
+                "properties": [
+                    {
+                        "name": "library_name",
+                        "value": "transformers"
+                    },
+                    {
+                        "name": "base_model",
+                        "value": "Qwen/Qwen3-4B-Base"
+                    }
+                ]
+            },
+            "authors": [
+                {
+                    "name": "Qwen"
+                }
+            ],
+            "licenses": [
+                {
+                    "license": {
+                        "id": "Apache-2.0",
+                        "url": "https://spdx.org/licenses/Apache-2.0.html"
+                    }
+                }
+            ],
+            "description": "**Qwen3-4B** has the following features:- Type: Causal Language Models- Training Stage: Pretraining & Post-training- Number of Parameters: 4.0B- Number of Paramaters (Non-Embedding): 3.6B- Number of Layers: 36- Number of Attention Heads (GQA): 32 for Q and 8 for KV- Context Length: 32,768 natively and [131,072 tokens with YaRN](#processing-long-texts).For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our [blog](https://qwenlm.github.io/blog/qwen3/), [GitHub](https://github.com/QwenLM/Qwen3), and [Documentation](https://qwen.readthedocs.io/en/latest/).> [!TIP]> If you encounter significant endless repetitions, please refer to the [Best Practices](#best-practices) section for optimal sampling parameters, and set the ``presence_penalty`` to 1.5.",
+            "tags": [
+                "transformers",
+                "safetensors",
+                "qwen3",
+                "text-generation",
+                "conversational",
+                "arxiv:2309.00071",
+                "arxiv:2505.09388",
+                "base_model:Qwen/Qwen3-4B-Base",
+                "base_model:finetune:Qwen/Qwen3-4B-Base",
+                "license:apache-2.0",
+                "autotrain_compatible",
+                "text-generation-inference",
+                "endpoints_compatible",
+                "region:us"
+            ]
+        }
+    }
+}