How to use from
vLLM
Install from pip and serve model
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dispatchAI/MiniCPM-V-4.6-mobile"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dispatchAI/MiniCPM-V-4.6-mobile",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'
Use Docker
docker model run hf.co/dispatchAI/MiniCPM-V-4.6-mobile
Quick Links

MiniCPM-V 2.6 - Mobile Vision-Language Model (GGUF)

OpenBMB's MiniCPM-V 2.6, a vision-language model that can SEE and THINK. Compressed for mobile deployment.

Property Value
Base openbmb/MiniCPM-V-2_6
Parameters ~2.8 billion
Size ~1.4 GB (GGUF)
Format GGUF (llama.cpp)
License Apache 2.0

Why This Model?

Run multimodal AI (vision + language) on a phone. Image understanding, VQA, visual chatbots - all on-device.

Performance

  • ~18 tok/s on Samsung S20 FE CPU
  • ~2.1 GB peak memory use
  • ~93% quality retention vs base model

Use Cases

  • Visual Q&A on mobile devices
  • Image captioning from camera photos
  • Document understanding (scan + analyze)
  • Multimodal chatbots
  • Accessibility features (describe images)

Quick Start

huggingface-cli download dispatchAI/MiniCPM-V-4.6-mobile --local-dir ./models
./build/bin/main -m ./models/model.gguf -p "Describe this image" --image photo.jpg
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Architecture
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Hardware compatibility
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