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    "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"
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            ],
            "modelCard": {
                "modelParameters": {
                    "task": "image-text-to-text",
                    "architectureFamily": "minicpmv",
                    "modelArchitecture": "MiniCPMV",
                    "datasets": [
                        {
                            "ref": "openbmb/RLAIF-V-Dataset-d1ff380b-52a0-586f-946e-773a0ef8556f"
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            ],
            "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"
            ]
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    },
    "components": [
        {
            "type": "data",
            "bom-ref": "openbmb/RLAIF-V-Dataset-d1ff380b-52a0-586f-946e-773a0ef8556f",
            "name": "openbmb/RLAIF-V-Dataset",
            "data": [
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                    "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"
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                            {
                                "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"
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                    "governance": {
                        "owners": [
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                                "organization": {
                                    "name": "openbmb",
                                    "url": "https://huggingface.co/openbmb"
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                    "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."
                }
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}