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

openbmb_MiniCPM-Llama3-V-2_5.json

@@ -0,0 +1,109 @@
+{
+    "bomFormat": "CycloneDX",
+    "specVersion": "1.6",
+    "serialNumber": "urn:uuid:1d723570-c4c0-4b5d-b1a1-aac2d99d4b2d",
+    "version": 1,
+    "metadata": {
+        "timestamp": "2025-06-05T09:41:47.231012+00:00",
+        "component": {
+            "type": "machine-learning-model",
+            "bom-ref": "openbmb/MiniCPM-Llama3-V-2_5-0f9a184d-7b24-5845-b52e-cbdc16daa953",
+            "name": "openbmb/MiniCPM-Llama3-V-2_5",
+            "externalReferences": [
+                {
+                    "url": "https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5",
+                    "type": "documentation"
+                }
+            ],
+            "modelCard": {
+                "modelParameters": {
+                    "task": "image-text-to-text",
+                    "architectureFamily": "minicpmv",
+                    "modelArchitecture": "MiniCPMV",
+                    "datasets": [
+                        {
+                            "ref": "openbmb/RLAIF-V-Dataset-d1ff380b-52a0-586f-946e-773a0ef8556f"
+                        }
+                    ]
+                },
+                "properties": [
+                    {
+                        "name": "library_name",
+                        "value": "transformers"
+                    }
+                ]
+            },
+            "authors": [
+                {
+                    "name": "openbmb"
+                }
+            ],
+            "description": "**MiniCPM-Llama3-V 2.5** is the latest model in the MiniCPM-V series. The model is built on SigLip-400M and Llama3-8B-Instruct with a total of 8B parameters. It exhibits a significant performance improvement over MiniCPM-V 2.0. Notable features of MiniCPM-Llama3-V 2.5 include:- \ud83d\udd25 **Leading Performance.**MiniCPM-Llama3-V 2.5 has achieved an average score of 65.1 on OpenCompass, a comprehensive evaluation over 11 popular benchmarks. **With only 8B parameters, it surpasses widely used proprietary models like GPT-4V-1106, Gemini Pro, Claude 3 and Qwen-VL-Max** and greatly outperforms other Llama 3-based MLLMs.- \ud83d\udcaa **Strong OCR Capabilities.**MiniCPM-Llama3-V 2.5 can process images with any aspect ratio and up to 1.8 million pixels (e.g., 1344x1344), achieving an **700+ score on OCRBench, surpassing proprietary models such as GPT-4o, GPT-4V-0409, Qwen-VL-Max and Gemini Pro**. Based on recent user feedback, MiniCPM-Llama3-V 2.5 has now enhanced full-text OCR extraction, table-to-markdown conversion, and other high-utility capabilities, and has further strengthened its instruction-following and complex reasoning abilities, enhancing multimodal interaction experiences.- \ud83c\udfc6 **Trustworthy Behavior.**Leveraging the latest [RLAIF-V](https://github.com/RLHF-V/RLAIF-V/) method (the newest technology in the [RLHF-V](https://github.com/RLHF-V) [CVPR'24] series), MiniCPM-Llama3-V 2.5 exhibits more trustworthy behavior. It achieves **10.3%** hallucination rate on Object HalBench, lower than GPT-4V-1106 (13.6%), achieving the best-level performance within the open-source community. [Data released](https://huggingface.co/datasets/openbmb/RLAIF-V-Dataset).- \ud83c\udf0f **Multilingual Support.**Thanks to the strong multilingual capabilities of Llama 3 and the cross-lingual generalization technique from [VisCPM](https://github.com/OpenBMB/VisCPM), MiniCPM-Llama3-V 2.5 extends its bilingual (Chinese-English) multimodal capabilities to **over 30 languages including German, French, Spanish, Italian, Korean, Japanese etc.** [All Supported Languages](./assets/minicpm-llama-v-2-5_languages.md).- \ud83d\ude80 **Efficient Deployment.**MiniCPM-Llama3-V 2.5 systematically employs **model quantization, CPU optimizations, NPU optimizations and compilation optimizations**, achieving high-efficiency deployment on edge devices. For mobile phones with Qualcomm chips, we have integrated the NPU acceleration framework QNN into llama.cpp for the first time. After systematic optimization, MiniCPM-Llama3-V 2.5 has realized a **150-fold acceleration in multimodal large model end-side image encoding** and a **3-fold increase in language decoding speed**.-  \ud83d\udcab  **Easy Usage.**MiniCPM-Llama3-V 2.5 can be easily used in various ways: (1) [llama.cpp](https://github.com/OpenBMB/llama.cpp/blob/minicpm-v2.5/examples/minicpmv/README.md) and [ollama](https://github.com/OpenBMB/ollama/tree/minicpm-v2.5/examples/minicpm-v2.5) support for efficient CPU inference on local devices, (2) [GGUF](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5-gguf) format quantized models in 16 sizes, (3) efficient [LoRA](https://github.com/OpenBMB/MiniCPM-V/tree/main/finetune#lora-finetuning) fine-tuning with only 2 V100 GPUs, (4) [streaming output](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5#usage), (5) quick local WebUI demo setup with [Gradio](https://github.com/OpenBMB/MiniCPM-V/blob/main/web_demo_2.5.py) and [Streamlit](https://github.com/OpenBMB/MiniCPM-V/blob/main/web_demo_streamlit-2_5.py), and (6) interactive demos on [HuggingFace Spaces](https://huggingface.co/spaces/openbmb/MiniCPM-Llama3-V-2_5).",
+            "tags": [
+                "transformers",
+                "safetensors",
+                "minicpmv",
+                "feature-extraction",
+                "minicpm-v",
+                "vision",
+                "ocr",
+                "custom_code",
+                "image-text-to-text",
+                "conversational",
+                "multilingual",
+                "dataset:openbmb/RLAIF-V-Dataset",
+                "region:us"
+            ]
+        }
+    },
+    "components": [
+        {
+            "type": "data",
+            "bom-ref": "openbmb/RLAIF-V-Dataset-d1ff380b-52a0-586f-946e-773a0ef8556f",
+            "name": "openbmb/RLAIF-V-Dataset",
+            "data": [
+                {
+                    "type": "dataset",
+                    "bom-ref": "openbmb/RLAIF-V-Dataset-d1ff380b-52a0-586f-946e-773a0ef8556f",
+                    "name": "openbmb/RLAIF-V-Dataset",
+                    "contents": {
+                        "url": "https://huggingface.co/datasets/openbmb/RLAIF-V-Dataset",
+                        "properties": [
+                            {
+                                "name": "task_categories",
+                                "value": "visual-question-answering"
+                            },
+                            {
+                                "name": "language",
+                                "value": "en"
+                            },
+                            {
+                                "name": "size_categories",
+                                "value": "10K<n<100K"
+                            },
+                            {
+                                "name": "pretty_name",
+                                "value": "RLAIF-V-Dataset"
+                            },
+                            {
+                                "name": "license",
+                                "value": "cc-by-nc-4.0"
+                            }
+                        ]
+                    },
+                    "governance": {
+                        "owners": [
+                            {
+                                "organization": {
+                                    "name": "openbmb",
+                                    "url": "https://huggingface.co/openbmb"
+                                }
+                            }
+                        ]
+                    },
+                    "description": "\n\t\n\t\t\n\t\tDataset Card for RLAIF-V-Dataset\n\t\n\nGitHub  | Paper\n\n\t\n\t\t\n\t\tNews:\n\t\n\n\n[2025.03.01] \ud83c\udf89 RLAIF-V is accepted by CVPR 2025! You can access the lastest version of the paper at here.\n[2024.05.28] \ud83d\udcc3 Our paper is accesible at arxiv now!\n[2024.05.20] \ud83d\udd25 Our data is used in MiniCPM-Llama3-V 2.5, which represents the first end-side MLLM achieving GPT-4V level performance!\n\n\n\t\n\t\t\n\t\n\t\n\t\tDataset Summary\n\t\n\nRLAIF-V-Dataset is a large-scale multimodal feedback dataset. The dataset provides\u2026 See the full description on the dataset page: https://huggingface.co/datasets/openbmb/RLAIF-V-Dataset."
+                }
+            ]
+        }
+    ]
+}