MiniCPM-V
https://github.com/openbmb/minicpm-v- Category
- AI Tools
- Rank
- No. 213Tools index
- Pricing
- Open Source
- Type
- TOOL
- Builder
- openbmb
- GitHub
- 26.3k stars
- Latest release
- 20250527
- Date
About
Pocket-sized multimodal LLM from OpenBMB designed for efficient image and video understanding on phones and edge devices.
What it does
MiniCPM-V accepts text alongside images or video, then produces conversational answers about that material. The family supports local inference, hosted chat requests, multi-image reasoning, OCR, and configurable decoding. Visual token compression reduces the work required to represent high-resolution media before language generation.
Why it's ranked here
This is a strong choice when deployment efficiency matters as much as visual capability. The documented 1.3-billion-parameter model cuts visual encoding computation by more than half through early compression, while mixed compression rates let implementers trade detail for speed. Its broad runtime and mobile support make those efficiency claims practically useful.
What's good
The deployment surface is unusually broad: hosted requests, Ollama, llama.cpp, vLLM, SGLang, local web demos, and mobile adaptations. The hosted interface uses a familiar chat-completions shape for text, images, and video. Fine-tuning guidance, quantized formats, streaming output, and configurable decoding cover several paths from experiment to deployment.
Tradeoffs
The repository spans several generations, including archived models and instructions tied to older forks, so version selection needs care. Local installation pulls a large, tightly pinned Python dependency set plus an externally hosted wheel. Hosted image and video requests require base64 data URLs. Speech input, speech output, and full-duplex interaction belong to MiniCPM-o, not MiniCPM-V.
How to use it well
Use MiniCPM-V for local or hosted applications that inspect images, documents, multiple pictures, or video under constrained compute. Start through the chat-compatible API or an established inference runtime, then test sampling against beam search for your workload. Choose MiniCPM-o instead when continuous audio, spoken responses, or simultaneous listening and speaking are core requirements.
Technical notes+
README.md presents MiniCPM-V 4.6 as a 1.3B model using intra-ViT early compression and mixed 4x/16x visual token compression, with iOS, Android, and HarmonyOS adaptation. docs/api.md defines an authenticated POST /chat/completions endpoint and base64 image_url and video_url content parts for the 4.5, 4.6, Thinking, and Omni model IDs. requirements.txt pins PyTorch, Transformers, Gradio, Uvicorn, vision packages, evaluation libraries, decord, and a direct HTTP wheel dependency. docs/faqs.md recommends sampling for faster or streaming generation and beam search experiments for deterministic answers; it also documents min_new_tokens as a mitigation for premature multilingual output.
Observed
- Packaging and install surface
- Python dependencies are installed from requirements.txt, including PyTorch, Transformers, Gradio, Uvicorn, OpenCV, Pillow, and decord.
- Hosted interface
- Provides a bearer-authenticated Chat Completions HTTP API for text, image, and video requests.
- Local inference interfaces
- Documentation lists Ollama, llama.cpp, vLLM, SGLang, LLaMA-Factory, Xinference, and a local Gradio web demo.
- Platform support
- MiniCPM-V 4.6 deployment is documented for iOS, Android, and HarmonyOS.
- Media transport
- The hosted API accepts images and videos as base64 data URLs inside chat message content.
- Repository structure
- The documentation includes archived pages for earlier MiniCPM-V and OmniLMM model generations.
Read from README.md, requirements.txt, docs/api.md, docs/faqs.md, docs/wechat.md, docs/omnilmm.md, docs/minicpm_v1.md, docs/minicpm_v2.md, docs/omnilmm_en.md, docs/minicpm_v4_en.md, docs/minicpm_v4_zh.md, docs/minicpm_v2dot6.md, docs/xinference_infer.md, docs/minicpm_o2dot6_en.md.
What it can do
Analyze and describe image content
Image file → Text description of image contents
Answer questions about images
Image file and text question → Text answer based on image analysis
Process and understand video content
Video file → Text description or analysis of video content
Extract text from images
Image containing text → Extracted text content
Generate captions for images
Image file → Descriptive caption text
Perform visual question answering on video
Video file and text question → Text answer based on video analysis
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