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

openchat_openchat-3.5-1210.json

@@ -0,0 +1,480 @@
+{
+    "bomFormat": "CycloneDX",
+    "specVersion": "1.6",
+    "serialNumber": "urn:uuid:3a9979cc-51e6-4439-b6a4-010842855745",
+    "version": 1,
+    "metadata": {
+        "timestamp": "2025-06-05T09:38:09.000189+00:00",
+        "component": {
+            "type": "machine-learning-model",
+            "bom-ref": "openchat/openchat-3.5-1210-fca84ccc-d530-50f8-990c-1ea1cd4217e2",
+            "name": "openchat/openchat-3.5-1210",
+            "externalReferences": [
+                {
+                    "url": "https://huggingface.co/openchat/openchat-3.5-1210",
+                    "type": "documentation"
+                }
+            ],
+            "modelCard": {
+                "modelParameters": {
+                    "task": "text-generation",
+                    "architectureFamily": "mistral",
+                    "modelArchitecture": "MistralForCausalLM",
+                    "datasets": [
+                        {
+                            "ref": "openchat/openchat_sharegpt4_dataset-79a9a029-636a-5348-a67b-03818aca7f78"
+                        },
+                        {
+                            "ref": "kaist-ai/Feedback-Collection-32aaa1f9-555e-5d7b-bc75-06bcd7f864bf"
+                        },
+                        {
+                            "ref": "imone/OpenOrca_FLAN-56466c9b-33bb-5f23-ab7c-388f28f07644"
+                        },
+                        {
+                            "ref": "LDJnr/Capybara-b2bf7143-e608-5efa-808f-227e2689633b"
+                        },
+                        {
+                            "ref": "tiedong/goat-38642401-79d7-541d-a92f-d3d7acdb8db8"
+                        },
+                        {
+                            "ref": "glaiveai/glaive-code-assistant-4cf9eb44-c4b0-5c31-b48c-ce95c2926a6d"
+                        },
+                        {
+                            "ref": "meta-math/MetaMathQA-c6cf810a-8b06-5552-a876-53681c5fe9a1"
+                        },
+                        {
+                            "ref": "OpenAssistant/oasst_top1_2023-08-25-10cff0b1-2289-5b35-8ea8-59253cea318a"
+                        },
+                        {
+                            "ref": "TIGER-Lab/MathInstruct-9d9c997d-f6c1-5029-96fd-6003c4f0ec06"
+                        }
+                    ]
+                },
+                "properties": [
+                    {
+                        "name": "library_name",
+                        "value": "transformers"
+                    },
+                    {
+                        "name": "base_model",
+                        "value": "mistralai/Mistral-7B-v0.1"
+                    }
+                ]
+            },
+            "authors": [
+                {
+                    "name": "openchat"
+                }
+            ],
+            "licenses": [
+                {
+                    "license": {
+                        "id": "Apache-2.0",
+                        "url": "https://spdx.org/licenses/Apache-2.0.html"
+                    }
+                }
+            ],
+            "tags": [
+                "transformers",
+                "safetensors",
+                "mistral",
+                "text-generation",
+                "openchat",
+                "C-RLFT",
+                "conversational",
+                "dataset:openchat/openchat_sharegpt4_dataset",
+                "dataset:kaist-ai/Feedback-Collection",
+                "dataset:imone/OpenOrca_FLAN",
+                "dataset:LDJnr/Capybara",
+                "dataset:tiedong/goat",
+                "dataset:glaiveai/glaive-code-assistant",
+                "dataset:meta-math/MetaMathQA",
+                "dataset:OpenAssistant/oasst_top1_2023-08-25",
+                "dataset:TIGER-Lab/MathInstruct",
+                "arxiv:2309.11235",
+                "arxiv:2303.08774",
+                "base_model:mistralai/Mistral-7B-v0.1",
+                "base_model:finetune:mistralai/Mistral-7B-v0.1",
+                "license:apache-2.0",
+                "autotrain_compatible",
+                "text-generation-inference",
+                "endpoints_compatible",
+                "region:us"
+            ]
+        }
+    },
+    "components": [
+        {
+            "type": "data",
+            "bom-ref": "openchat/openchat_sharegpt4_dataset-79a9a029-636a-5348-a67b-03818aca7f78",
+            "name": "openchat/openchat_sharegpt4_dataset",
+            "data": [
+                {
+                    "type": "dataset",
+                    "bom-ref": "openchat/openchat_sharegpt4_dataset-79a9a029-636a-5348-a67b-03818aca7f78",
+                    "name": "openchat/openchat_sharegpt4_dataset",
+                    "contents": {
+                        "url": "https://huggingface.co/datasets/openchat/openchat_sharegpt4_dataset",
+                        "properties": [
+                            {
+                                "name": "task_categories",
+                                "value": "conversational, text-generation"
+                            },
+                            {
+                                "name": "language",
+                                "value": "en"
+                            },
+                            {
+                                "name": "size_categories",
+                                "value": "1K<n<10K"
+                            },
+                            {
+                                "name": "pretty_name",
+                                "value": "OpenChat"
+                            }
+                        ]
+                    },
+                    "governance": {
+                        "owners": [
+                            {
+                                "organization": {
+                                    "name": "openchat",
+                                    "url": "https://huggingface.co/openchat"
+                                }
+                            }
+                        ]
+                    },
+                    "description": "This repository contains cleaned and filtered ShareGPT GPT-4 data used to train OpenChat. Details can be found in the OpenChat repository.\n"
+                }
+            ]
+        },
+        {
+            "type": "data",
+            "bom-ref": "kaist-ai/Feedback-Collection-32aaa1f9-555e-5d7b-bc75-06bcd7f864bf",
+            "name": "kaist-ai/Feedback-Collection",
+            "data": [
+                {
+                    "type": "dataset",
+                    "bom-ref": "kaist-ai/Feedback-Collection-32aaa1f9-555e-5d7b-bc75-06bcd7f864bf",
+                    "name": "kaist-ai/Feedback-Collection",
+                    "contents": {
+                        "url": "https://huggingface.co/datasets/kaist-ai/Feedback-Collection",
+                        "properties": [
+                            {
+                                "name": "task_categories",
+                                "value": "text-generation, text-classification"
+                            },
+                            {
+                                "name": "language",
+                                "value": "en"
+                            },
+                            {
+                                "name": "size_categories",
+                                "value": "10K<n<100K"
+                            },
+                            {
+                                "name": "configs",
+                                "value": "Name of the dataset subset: default {\"split\": \"train\", \"path\": \"new_feedback_collection.json\"}"
+                            },
+                            {
+                                "name": "license",
+                                "value": "cc-by-4.0"
+                            }
+                        ]
+                    },
+                    "governance": {
+                        "owners": [
+                            {
+                                "organization": {
+                                    "name": "prometheus-eval",
+                                    "url": "https://huggingface.co/prometheus-eval"
+                                }
+                            }
+                        ]
+                    },
+                    "description": "\n\t\n\t\t\n\t\tDataset Card\n\t\n\n\n\t\n\t\t\n\t\tDataset Summary\n\t\n\nThe Feedback Collection is a dataset designed to induce fine-grained evaluation capabilities into language models.\\\n\nRecently, proprietary LLMs (e.g., GPT-4) have been used to evaluate long-form responses. In our experiments, we found that open-source LMs are not capable of evaluating long-form responses, showing low correlation with both human evaluators and GPT-4.\\\nIn our paper, we found that by (1) fine-tuning feedback generated by GPT-4\u2026 See the full description on the dataset page: https://huggingface.co/datasets/prometheus-eval/Feedback-Collection."
+                }
+            ]
+        },
+        {
+            "type": "data",
+            "bom-ref": "imone/OpenOrca_FLAN-56466c9b-33bb-5f23-ab7c-388f28f07644",
+            "name": "imone/OpenOrca_FLAN",
+            "data": [
+                {
+                    "type": "dataset",
+                    "bom-ref": "imone/OpenOrca_FLAN-56466c9b-33bb-5f23-ab7c-388f28f07644",
+                    "name": "imone/OpenOrca_FLAN",
+                    "contents": {
+                        "url": "https://huggingface.co/datasets/imone/OpenOrca_FLAN",
+                        "properties": [
+                            {
+                                "name": "license",
+                                "value": "mit"
+                            }
+                        ]
+                    },
+                    "governance": {
+                        "owners": [
+                            {
+                                "organization": {
+                                    "name": "imone",
+                                    "url": "https://huggingface.co/imone"
+                                }
+                            }
+                        ]
+                    },
+                    "description": "This is the OpenOrca GPT4 subset with the original FLAN answers. Each even row (indexed starting from 0) contains the OpenOrca GPT4 answer, while each odd row contains the corresponding FLAN answer.\n"
+                }
+            ]
+        },
+        {
+            "type": "data",
+            "bom-ref": "LDJnr/Capybara-b2bf7143-e608-5efa-808f-227e2689633b",
+            "name": "LDJnr/Capybara",
+            "data": [
+                {
+                    "type": "dataset",
+                    "bom-ref": "LDJnr/Capybara-b2bf7143-e608-5efa-808f-227e2689633b",
+                    "name": "LDJnr/Capybara",
+                    "contents": {
+                        "url": "https://huggingface.co/datasets/LDJnr/Capybara",
+                        "properties": [
+                            {
+                                "name": "task_categories",
+                                "value": "conversational, question-answering, text-generation"
+                            },
+                            {
+                                "name": "language",
+                                "value": "en"
+                            },
+                            {
+                                "name": "size_categories",
+                                "value": "10K<n<100K"
+                            },
+                            {
+                                "name": "pretty_name",
+                                "value": "LessWrong-Amplify-Instruct"
+                            },
+                            {
+                                "name": "license",
+                                "value": "apache-2.0"
+                            }
+                        ]
+                    },
+                    "governance": {
+                        "owners": [
+                            {
+                                "organization": {
+                                    "name": "LDJnr",
+                                    "url": "https://huggingface.co/LDJnr"
+                                }
+                            }
+                        ]
+                    },
+                    "description": "\n\t\n\t\t\n\t\tThis is the Official Capybara dataset. Over 10,000 multi-turn examples.\n\t\n\nCapybara is the culmination of insights derived from synthesis techniques like Evol-instruct (used for WizardLM), Alpaca, Orca, Vicuna, Lamini, FLASK and others.\nThe single-turn seeds used to initiate the Amplify-Instruct synthesis of conversations are mostly based on datasets that i've personally vetted extensively, and are often highly regarded for their diversity and demonstration of logical robustness and\u2026 See the full description on the dataset page: https://huggingface.co/datasets/LDJnr/Capybara."
+                }
+            ]
+        },
+        {
+            "type": "data",
+            "bom-ref": "tiedong/goat-38642401-79d7-541d-a92f-d3d7acdb8db8",
+            "name": "tiedong/goat",
+            "data": [
+                {
+                    "type": "dataset",
+                    "bom-ref": "tiedong/goat-38642401-79d7-541d-a92f-d3d7acdb8db8",
+                    "name": "tiedong/goat",
+                    "contents": {
+                        "url": "https://huggingface.co/datasets/tiedong/goat",
+                        "properties": [
+                            {
+                                "name": "task_categories",
+                                "value": "question-answering"
+                            },
+                            {
+                                "name": "language",
+                                "value": "en"
+                            },
+                            {
+                                "name": "size_categories",
+                                "value": "1M<n<10M"
+                            },
+                            {
+                                "name": "license",
+                                "value": "apache-2.0"
+                            }
+                        ]
+                    },
+                    "governance": {
+                        "owners": [
+                            {
+                                "organization": {
+                                    "name": "tiedong",
+                                    "url": "https://huggingface.co/tiedong"
+                                }
+                            }
+                        ]
+                    },
+                    "description": "\n\t\n\t\t\n\t\tDataset Card for Dataset Name\n\t\n\n\n\t\n\t\t\n\t\tDataset Summary\n\t\n\nThe dataset.json file contains ~1.7 million synthetic data for arithmetic tasks, generated by dataset.ipynb.\n\n\t\n\t\t\n\t\tSupported Tasks and Leaderboards\n\t\n\n[More Information Needed]\n\n\t\n\t\t\n\t\tLanguages\n\t\n\n[More Information Needed]\n\n\t\n\t\t\n\t\tDataset Structure\n\t\n\n\n\t\n\t\t\n\t\tData Instances\n\t\n\n[More Information Needed]\n\n\t\n\t\t\n\t\tData Fields\n\t\n\n[More Information Needed]\n\n\t\n\t\t\n\t\tData Splits\n\t\n\n[More Information Needed]\n\n\t\n\t\t\n\t\tDataset Creation\u2026 See the full description on the dataset page: https://huggingface.co/datasets/tiedong/goat."
+                }
+            ]
+        },
+        {
+            "type": "data",
+            "bom-ref": "glaiveai/glaive-code-assistant-4cf9eb44-c4b0-5c31-b48c-ce95c2926a6d",
+            "name": "glaiveai/glaive-code-assistant",
+            "data": [
+                {
+                    "type": "dataset",
+                    "bom-ref": "glaiveai/glaive-code-assistant-4cf9eb44-c4b0-5c31-b48c-ce95c2926a6d",
+                    "name": "glaiveai/glaive-code-assistant",
+                    "contents": {
+                        "url": "https://huggingface.co/datasets/glaiveai/glaive-code-assistant",
+                        "properties": [
+                            {
+                                "name": "size_categories",
+                                "value": "100K<n<1M"
+                            },
+                            {
+                                "name": "license",
+                                "value": "apache-2.0"
+                            }
+                        ]
+                    },
+                    "governance": {
+                        "owners": [
+                            {
+                                "organization": {
+                                    "name": "glaiveai",
+                                    "url": "https://huggingface.co/glaiveai"
+                                }
+                            }
+                        ]
+                    },
+                    "description": "\n\t\n\t\t\n\t\tGlaive-code-assistant\n\t\n\nGlaive-code-assistant is a dataset of ~140k code problems and solutions generated using Glaive\u2019s synthetic data generation platform.\nThe data is intended to be used to make models act as code assistants, and so the data is structured in a QA format where the questions are worded similar to how real users will ask code related questions.\nThe data has ~60% python samples.\nTo report any problems or suggestions in the data, join the Glaive discord\n"
+                }
+            ]
+        },
+        {
+            "type": "data",
+            "bom-ref": "meta-math/MetaMathQA-c6cf810a-8b06-5552-a876-53681c5fe9a1",
+            "name": "meta-math/MetaMathQA",
+            "data": [
+                {
+                    "type": "dataset",
+                    "bom-ref": "meta-math/MetaMathQA-c6cf810a-8b06-5552-a876-53681c5fe9a1",
+                    "name": "meta-math/MetaMathQA",
+                    "contents": {
+                        "url": "https://huggingface.co/datasets/meta-math/MetaMathQA",
+                        "properties": [
+                            {
+                                "name": "license",
+                                "value": "mit"
+                            }
+                        ]
+                    },
+                    "governance": {
+                        "owners": [
+                            {
+                                "organization": {
+                                    "name": "meta-math",
+                                    "url": "https://huggingface.co/meta-math"
+                                }
+                            }
+                        ]
+                    },
+                    "description": "View the project page:\nhttps://meta-math.github.io/\nsee our paper at https://arxiv.org/abs/2309.12284\n\n\t\n\t\t\n\t\tNote\n\t\n\nAll MetaMathQA data are augmented from the training sets of GSM8K and MATH. \nNone of the augmented data is from the testing set.\nYou can check the original_question in meta-math/MetaMathQA, each item is from the GSM8K or MATH train set.\n\n\t\n\t\t\n\t\tModel Details\n\t\n\nMetaMath-Mistral-7B is fully fine-tuned on the MetaMathQA datasets and based on the powerful Mistral-7B model. It is\u2026 See the full description on the dataset page: https://huggingface.co/datasets/meta-math/MetaMathQA."
+                }
+            ]
+        },
+        {
+            "type": "data",
+            "bom-ref": "OpenAssistant/oasst_top1_2023-08-25-10cff0b1-2289-5b35-8ea8-59253cea318a",
+            "name": "OpenAssistant/oasst_top1_2023-08-25",
+            "data": [
+                {
+                    "type": "dataset",
+                    "bom-ref": "OpenAssistant/oasst_top1_2023-08-25-10cff0b1-2289-5b35-8ea8-59253cea318a",
+                    "name": "OpenAssistant/oasst_top1_2023-08-25",
+                    "contents": {
+                        "url": "https://huggingface.co/datasets/OpenAssistant/oasst_top1_2023-08-25",
+                        "properties": [
+                            {
+                                "name": "task_categories",
+                                "value": "conversational"
+                            },
+                            {
+                                "name": "size_categories",
+                                "value": "10K<n<100K"
+                            },
+                            {
+                                "name": "license",
+                                "value": "apache-2.0"
+                            }
+                        ]
+                    },
+                    "governance": {
+                        "owners": [
+                            {
+                                "organization": {
+                                    "name": "OpenAssistant",
+                                    "url": "https://huggingface.co/OpenAssistant"
+                                }
+                            }
+                        ]
+                    },
+                    "description": "\n\t\n\t\t\n\t\tOpenAssistant TOP-1 Conversation Threads\n\t\n\n\nGuanacco style export of the best conversation threads from the open-assistant.io database\nexported August 25, 2023\njsonl files with chatml formatted conversations\ntrain: 12,947 samples / valid: 680 samples\n\n"
+                }
+            ]
+        },
+        {
+            "type": "data",
+            "bom-ref": "TIGER-Lab/MathInstruct-9d9c997d-f6c1-5029-96fd-6003c4f0ec06",
+            "name": "TIGER-Lab/MathInstruct",
+            "data": [
+                {
+                    "type": "dataset",
+                    "bom-ref": "TIGER-Lab/MathInstruct-9d9c997d-f6c1-5029-96fd-6003c4f0ec06",
+                    "name": "TIGER-Lab/MathInstruct",
+                    "contents": {
+                        "url": "https://huggingface.co/datasets/TIGER-Lab/MathInstruct",
+                        "properties": [
+                            {
+                                "name": "task_categories",
+                                "value": "text-generation"
+                            },
+                            {
+                                "name": "language",
+                                "value": "en"
+                            },
+                            {
+                                "name": "size_categories",
+                                "value": "100K<n<1M"
+                            },
+                            {
+                                "name": "pretty_name",
+                                "value": "MathInstruct"
+                            },
+                            {
+                                "name": "license",
+                                "value": "mit"
+                            }
+                        ]
+                    },
+                    "governance": {
+                        "owners": [
+                            {
+                                "organization": {
+                                    "name": "TIGER-Lab",
+                                    "url": "https://huggingface.co/TIGER-Lab"
+                                }
+                            }
+                        ]
+                    },
+                    "description": "\n\t\n\t\t\n\t\t\ud83e\udda3 MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning\n\t\n\nMathInstruct is a meticulously curated instruction tuning dataset that is lightweight yet generalizable. MathInstruct is compiled from 13 math rationale datasets, six of which are newly curated by this work. It uniquely focuses on the hybrid use of chain-of-thought (CoT) and program-of-thought (PoT) rationales, and ensures extensive coverage of diverse mathematical fields. \nProject Page:\u2026 See the full description on the dataset page: https://huggingface.co/datasets/TIGER-Lab/MathInstruct."
+                }
+            ]
+        }
+    ]
+}