Instructions to use dispatchAI/MiniCPM-V-4.6-mobile with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use dispatchAI/MiniCPM-V-4.6-mobile with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf dispatchAI/MiniCPM-V-4.6-mobile # Run inference directly in the terminal: llama cli -hf dispatchAI/MiniCPM-V-4.6-mobile
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf dispatchAI/MiniCPM-V-4.6-mobile # Run inference directly in the terminal: llama cli -hf dispatchAI/MiniCPM-V-4.6-mobile
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf dispatchAI/MiniCPM-V-4.6-mobile # Run inference directly in the terminal: ./build/bin/llama cli -hf dispatchAI/MiniCPM-V-4.6-mobile
Use Docker
docker model run hf.co/dispatchAI/MiniCPM-V-4.6-mobile
- Llama new
- LM Studio
- Jan
- vLLM
How to use dispatchAI/MiniCPM-V-4.6-mobile with 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
- Ollama
How to use dispatchAI/MiniCPM-V-4.6-mobile with Ollama:
ollama run hf.co/dispatchAI/MiniCPM-V-4.6-mobile
- Unsloth Desktop
- Pi
How to use dispatchAI/MiniCPM-V-4.6-mobile with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf dispatchAI/MiniCPM-V-4.6-mobile
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "dispatchAI/MiniCPM-V-4.6-mobile" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use dispatchAI/MiniCPM-V-4.6-mobile with Docker Model Runner:
docker model run hf.co/dispatchAI/MiniCPM-V-4.6-mobile
- Lemonade
How to use dispatchAI/MiniCPM-V-4.6-mobile with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull dispatchAI/MiniCPM-V-4.6-mobile
Run and chat with the model
lemonade run user.MiniCPM-V-4.6-mobile-{{QUANT_TAG}}List all available models
lemonade list
- Hermes Agent
How to use dispatchAI/MiniCPM-V-4.6-mobile with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf dispatchAI/MiniCPM-V-4.6-mobile
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default dispatchAI/MiniCPM-V-4.6-mobile
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use dispatchAI/MiniCPM-V-4.6-mobile with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf dispatchAI/MiniCPM-V-4.6-mobile
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "dispatchAI/MiniCPM-V-4.6-mobile" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
Upload README.md with huggingface_hub
Browse files
README.md
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---
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license: apache-2.0
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tags:
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- dispatch-ai
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- mobile
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- quantized
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- gguf
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pipeline_tag: image-text-to-text
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language:
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- en
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---
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# MiniCPM-V
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**Dispatch AI** —
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## Model
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Re-engineered from [openbmb/MiniCPM-V-4_6](https://huggingface.co/openbmb/MiniCPM-V-4_6).
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Quantized to Q4_K_M GGUF for on-device inference via llama.cpp.
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Size: 505 MB.
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## Phone Farm Test Results
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Tested on **Samsung Galaxy S20 FE 5G** (Snapdragon 865, 8GB RAM):
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| Phone | Gen t/s | Prompt t/s |
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| R3CN30WHS2Z | 16.6 | 53.9 |
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| R3CN509PLHA | 17.1 | 59.2 |
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- **Average: 16.9 t/s**
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- **40-phone aggregate: ~674 t/s**
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## Usage
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```bash
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./llama-cli -m model.gguf -p "Hello" -n 100 -t 4 -c 512
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```
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🌐 [dispatchAI on HuggingFace](https://huggingface.co/dispatchAI)
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license: apache-2.0
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tags: [dispatch-ai, mobile, vision, quantized, gguf, phone-farm-tested, on-device, edge-ai, multimodal]
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pipeline_tag: image-text-to-text
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language: [en]
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---
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# MiniCPM-V 4.6 — Mobile Multimodal
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**Dispatch AI** — See + understand on-device. The most capable mobile vision model we've tested.
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| Metric | Value |
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|--------|-------|
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| Generation | 11.2 t/s |
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| Size | ~2.8 GB |
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**Dispatch AI (FZE)** — Sharjah, UAE | License 10818
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