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

LGAI-EXAONE_EXAONE-3.5-2.4B-Instruct.json

@@ -0,0 +1,63 @@
+{
+    "bomFormat": "CycloneDX",
+    "specVersion": "1.6",
+    "serialNumber": "urn:uuid:ddc387ff-082c-433d-a8ea-d00bb2ffeb15",
+    "version": 1,
+    "metadata": {
+        "timestamp": "2025-06-05T09:34:47.467785+00:00",
+        "component": {
+            "type": "machine-learning-model",
+            "bom-ref": "LGAI-EXAONE/EXAONE-3.5-2.4B-Instruct-95b3c8ab-8a5d-5c6b-98d6-f2b6ec72201a",
+            "name": "LGAI-EXAONE/EXAONE-3.5-2.4B-Instruct",
+            "externalReferences": [
+                {
+                    "url": "https://huggingface.co/LGAI-EXAONE/EXAONE-3.5-2.4B-Instruct",
+                    "type": "documentation"
+                }
+            ],
+            "modelCard": {
+                "modelParameters": {
+                    "task": "text-generation",
+                    "architectureFamily": "exaone",
+                    "modelArchitecture": "ExaoneForCausalLM"
+                },
+                "properties": [
+                    {
+                        "name": "library_name",
+                        "value": "transformers"
+                    }
+                ]
+            },
+            "authors": [
+                {
+                    "name": "LGAI-EXAONE"
+                }
+            ],
+            "licenses": [
+                {
+                    "license": {
+                        "name": "exaone",
+                        "url": "LICENSE"
+                    }
+                }
+            ],
+            "description": "We introduce EXAONE 3.5, a collection of instruction-tuned bilingual (English and Korean) generative models ranging from 2.4B to 32B parameters, developed and released by LG AI Research. EXAONE 3.5 language models include: 1) **2.4B model** optimized for deployment on small or resource-constrained devices, 2) **7.8B model** matching the size of its predecessor but offering improved performance, and 3) **32B model** delivering powerful performance. All models support long-context processing of up to 32K tokens. Each model demonstrates state-of-the-art performance in real-world use cases and long-context understanding, while remaining competitive in general domains compared to recently released models of similar sizes.For more details, please refer to our [technical report](https://arxiv.org/abs/2412.04862), [blog](https://www.lgresearch.ai/blog/view?seq=507) and [GitHub](https://github.com/LG-AI-EXAONE/EXAONE-3.5).This repository contains the instruction-tuned 2.4B language model with the following features:- Number of Parameters (without embeddings): 2.14B- Number of Layers: 30- Number of Attention Heads: GQA with 32 Q-heads and 8 KV-heads- Vocab Size: 102,400- Context Length: 32,768 tokens- Tie Word Embeddings: True (unlike 7.8B and 32B models)",
+            "tags": [
+                "transformers",
+                "safetensors",
+                "exaone",
+                "text-generation",
+                "lg-ai",
+                "exaone-3.5",
+                "conversational",
+                "custom_code",
+                "en",
+                "ko",
+                "arxiv:2412.04862",
+                "license:other",
+                "autotrain_compatible",
+                "region:us"
+            ]
+        }
+    }
+}