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

rinna_japanese-gpt-neox-3.6b-instruction-sft.json

@@ -0,0 +1,152 @@
+{
+    "bomFormat": "CycloneDX",
+    "specVersion": "1.6",
+    "serialNumber": "urn:uuid:8fbc208b-8a4d-4877-a1c1-5d07a22f5cc0",
+    "version": 1,
+    "metadata": {
+        "timestamp": "2025-06-05T09:41:50.547933+00:00",
+        "component": {
+            "type": "machine-learning-model",
+            "bom-ref": "rinna/japanese-gpt-neox-3.6b-instruction-sft-e7a06d20-f7da-5712-b713-897284b52f74",
+            "name": "rinna/japanese-gpt-neox-3.6b-instruction-sft",
+            "externalReferences": [
+                {
+                    "url": "https://huggingface.co/rinna/japanese-gpt-neox-3.6b-instruction-sft",
+                    "type": "documentation"
+                }
+            ],
+            "modelCard": {
+                "modelParameters": {
+                    "task": "text-generation",
+                    "architectureFamily": "gpt_neox",
+                    "modelArchitecture": "GPTNeoXForCausalLM",
+                    "datasets": [
+                        {
+                            "ref": "Anthropic/hh-rlhf-5da2bc0b-1d42-54c8-8b53-3770b07b011a"
+                        },
+                        {
+                            "ref": "stanfordnlp/SHP-f9be653c-ec3d-5323-8491-cc46c65f1623"
+                        }
+                    ]
+                },
+                "properties": [
+                    {
+                        "name": "library_name",
+                        "value": "transformers"
+                    },
+                    {
+                        "name": "base_model",
+                        "value": "rinna/japanese-gpt-neox-3.6b"
+                    }
+                ]
+            },
+            "authors": [
+                {
+                    "name": "rinna"
+                }
+            ],
+            "licenses": [
+                {
+                    "license": {
+                        "id": "MIT",
+                        "url": "https://spdx.org/licenses/MIT.html"
+                    }
+                }
+            ],
+            "description": "This repository provides a Japanese GPT-NeoX model of 3.6 billion parameters. The model is based on [`rinna/japanese-gpt-neox-3.6b`](https://huggingface.co/rinna/japanese-gpt-neox-3.6b) and has been finetuned to serve as an instruction-following conversational agent.* **Model architecture**A 36-layer, 2816-hidden-size transformer-based language model.* **Finetuning**The finetuning data is the subset of the following datasets and has been translated into Japanese.* [Anthropic HH RLHF data](https://huggingface.co/datasets/Anthropic/hh-rlhf)* [FLAN Instruction Tuning data](https://github.com/google-research/FLAN)* [Stanford Human Preferences Dataset](https://huggingface.co/datasets/stanfordnlp/SHP)The data will **not** be released.* **Model Series**| Variant | Link || :-- | :--|| 3.6B PPO | https://huggingface.co/rinna/japanese-gpt-neox-3.6b-instruction-ppo || 3.6B SFT-v2 | https://huggingface.co/rinna/japanese-gpt-neox-3.6b-instruction-sft-v2 || 3.6B SFT | https://huggingface.co/rinna/japanese-gpt-neox-3.6b-instruction-sft || 3.6B pretrained | https://huggingface.co/rinna/japanese-gpt-neox-3.6b |* **Contributors**[Tianyu Zhao](https://huggingface.co/tianyuz) and [Kei Sawada](https://huggingface.co/keisawada)* **Release date**March 17, 2023",
+            "tags": [
+                "transformers",
+                "pytorch",
+                "safetensors",
+                "gpt_neox",
+                "text-generation",
+                "lm",
+                "nlp",
+                "ja",
+                "dataset:Anthropic/hh-rlhf",
+                "dataset:stanfordnlp/SHP",
+                "arxiv:2404.01657",
+                "base_model:rinna/japanese-gpt-neox-3.6b",
+                "base_model:finetune:rinna/japanese-gpt-neox-3.6b",
+                "license:mit",
+                "autotrain_compatible",
+                "text-generation-inference",
+                "region:us"
+            ]
+        }
+    },
+    "components": [
+        {
+            "type": "data",
+            "bom-ref": "Anthropic/hh-rlhf-5da2bc0b-1d42-54c8-8b53-3770b07b011a",
+            "name": "Anthropic/hh-rlhf",
+            "data": [
+                {
+                    "type": "dataset",
+                    "bom-ref": "Anthropic/hh-rlhf-5da2bc0b-1d42-54c8-8b53-3770b07b011a",
+                    "name": "Anthropic/hh-rlhf",
+                    "contents": {
+                        "url": "https://huggingface.co/datasets/Anthropic/hh-rlhf",
+                        "properties": [
+                            {
+                                "name": "license",
+                                "value": "mit"
+                            }
+                        ]
+                    },
+                    "governance": {
+                        "owners": [
+                            {
+                                "organization": {
+                                    "name": "Anthropic",
+                                    "url": "https://huggingface.co/Anthropic"
+                                }
+                            }
+                        ]
+                    },
+                    "description": "\n\t\n\t\t\n\t\tDataset Card for HH-RLHF\n\t\n\n\n\t\n\t\t\n\t\tDataset Summary\n\t\n\nThis repository provides access to two different kinds of data:\n\nHuman preference data about helpfulness and harmlessness from Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback. These data are meant to train preference (or reward) models for subsequent RLHF training. These data are not meant for supervised training of dialogue agents. Training dialogue agents on these data is likely to lead\u2026 See the full description on the dataset page: https://huggingface.co/datasets/Anthropic/hh-rlhf."
+                }
+            ]
+        },
+        {
+            "type": "data",
+            "bom-ref": "stanfordnlp/SHP-f9be653c-ec3d-5323-8491-cc46c65f1623",
+            "name": "stanfordnlp/SHP",
+            "data": [
+                {
+                    "type": "dataset",
+                    "bom-ref": "stanfordnlp/SHP-f9be653c-ec3d-5323-8491-cc46c65f1623",
+                    "name": "stanfordnlp/SHP",
+                    "contents": {
+                        "url": "https://huggingface.co/datasets/stanfordnlp/SHP",
+                        "properties": [
+                            {
+                                "name": "task_categories",
+                                "value": "text-generation, question-answering"
+                            },
+                            {
+                                "name": "language",
+                                "value": "en"
+                            },
+                            {
+                                "name": "size_categories",
+                                "value": "100K<n<1M"
+                            }
+                        ]
+                    },
+                    "governance": {
+                        "owners": [
+                            {
+                                "organization": {
+                                    "name": "stanfordnlp",
+                                    "url": "https://huggingface.co/stanfordnlp"
+                                }
+                            }
+                        ]
+                    },
+                    "description": "\n\t\n\t\t\n\t\t\ud83d\udea2  Stanford Human Preferences Dataset (SHP)\n\t\n\nIf you mention this dataset in a paper, please cite the paper: Understanding Dataset Difficulty with V-Usable Information (ICML 2022).\n\n\t\n\t\t\n\t\tSummary\n\t\n\nSHP is a dataset of 385K collective human preferences over responses to questions/instructions in 18 different subject areas, from cooking to legal advice.\nThe preferences are meant to reflect the helpfulness of one response over another, and are intended to be used for training RLHF\u2026 See the full description on the dataset page: https://huggingface.co/datasets/stanfordnlp/SHP."
+                }
+            ]
+        }
+    ]
+}