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MODEL Listed

MiniCPM V

MiniCPM V is a compact yet powerful vision-language model designed for efficient multimodal processing. Unlike monolithic VLM architectures, it focuses on high-performance visual question answering (VQA) while maintaining a small enough footprint for deployment in resource-constrained environments. For developers, this means a lower barrier to entry for integrating image-to-text capabilities into applications without requiring massive GPU clusters. It excels at interpreting visual context and translating it into structured text, making it ideal for automating image tagging, accessibility tools, and visual data extraction. Built under the Apache-2.0 license, it offers the flexibility needed for commercial integration and customization.

openbmbvisual-question-answering
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01 / MODEL CARD

Model card

MiniCPM V is a compact yet powerful vision-language model designed for efficient multimodal processing. Unlike monolithic VLM architectures, it focuses on high-performance visual question answering (VQA) while maintaining a small enough footprint for deployment in resource-constrained environments. For developers, this means a lower barrier to entry for integrating image-to-text capabilities into applications without requiring massive GPU clusters. It excels at interpreting visual context and translating it into structured text, making it ideal for automating image tagging, accessibility tools, and visual data extraction. Built under the Apache-2.0 license, it offers the flexibility needed for commercial integration and customization.

Model typevisual-question-answering
Provideropenbmb
LicenseApache-2.0
02 / FILES & VERSIONS

Model files and versions

Model cardModel description and metadata available in this entry
Listed
Source repositoryhttps://huggingface.co/openbmb/MiniCPM-V
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Version informationUse the source repository for the latest version
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03 / DOWNLOAD

Download this model

We recommend using the ModelScope CLI or SDK. Install ModelScope first, then choose a full snapshot, single file, SDK or Git LFS workflow.

This entry points to Hugging Face. The commands use the matching ModelScope repository format; confirm that the repository exists on ModelScope before running them. Model repository: openbmb/MiniCPM-V
Install ModelScope

Install the CLI and SDK dependency before downloading.

pip install modelscope
Download the full model repository

Download the complete weights, configuration and model card.

modelscope download --model openbmb/MiniCPM-V
Download one file to a local directory

README.md is used as an example; replace it with another repository file when needed.

modelscope download --model openbmb/MiniCPM-V README.md --local_dir ./dir
Download with the SDK

Useful in Python projects and automation scripts.

from modelscope import snapshot_download
model_dir = snapshot_download('openbmb/MiniCPM-V')
Clone with Git

Make sure Git LFS is installed correctly.

git lfs install
git clone https://www.modelscope.cn/openbmb/MiniCPM-V.git
Clone without downloading LFS blobs

Fetch the repository structure first, then pull large files when needed.

GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/openbmb/MiniCPM-V.git
04 / WORKFLOW

How to use

  1. 01
    Step 1

    Read the model card and source information.

  2. 02
    Step 2

    Start with a small, non-sensitive evaluation.

  3. 03
    Step 3

    Review quality, licensing and usage limits.

  4. 04
    Step 4

    Adopt it only after validation.

05 / DISCUSSIONS

Discussions

Use this space to keep checking source information, usage experience and maintenance status.

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