Hugging Face's logo Hugging Face
  • Models
  • Datasets
  • Spaces
  • Buckets new
  • Docs
  • Enterprise
  • Pricing
    • Website
      • Tasks
      • HuggingChat
      • Collections
      • Languages
      • Organizations
    • Community
      • Blog
      • Posts
      • Daily Papers
      • Hardware
      • Learn
      • Discord
      • Forum
      • GitHub
    • Solutions
      • Team & Enterprise
      • Hugging Face PRO
      • Enterprise Support
      • Inference Providers
      • Inference Endpoints
      • Storage Buckets

  • Log In
  • Sign Up

openbmb
/
MiniCPM-V-4-gguf

Image-Text-to-Text
Transformers
GGUF
multilingual
minicpm-v
vision
ocr
multi-image
video
custom_code
conversational
Model card Files Files and versions
xet
Community
6

Instructions to use openbmb/MiniCPM-V-4-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use openbmb/MiniCPM-V-4-gguf with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("image-text-to-text", model="openbmb/MiniCPM-V-4-gguf", trust_remote_code=True)
    messages = [
        {
            "role": "user",
            "content": [
                {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
                {"type": "text", "text": "What animal is on the candy?"}
            ]
        },
    ]
    pipe(text=messages)
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("openbmb/MiniCPM-V-4-gguf", trust_remote_code=True, device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • llama.cpp

    How to use openbmb/MiniCPM-V-4-gguf 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 openbmb/MiniCPM-V-4-gguf:Q4_K_M
    # Run inference directly in the terminal:
    llama cli -hf openbmb/MiniCPM-V-4-gguf:Q4_K_M
    Install from WinGet (Windows)
    winget install llama.cpp
    # Start a local OpenAI-compatible server with a web UI:
    llama serve -hf openbmb/MiniCPM-V-4-gguf:Q4_K_M
    # Run inference directly in the terminal:
    llama cli -hf openbmb/MiniCPM-V-4-gguf:Q4_K_M
    Use pre-built binary
    # Download pre-built binary from:
    # https://github.com/ggerganov/llama.cpp/releases
    # Start a local OpenAI-compatible server with a web UI:
    ./llama-server -hf openbmb/MiniCPM-V-4-gguf:Q4_K_M
    # Run inference directly in the terminal:
    ./llama-cli -hf openbmb/MiniCPM-V-4-gguf:Q4_K_M
    Build from source code
    git clone https://github.com/ggerganov/llama.cpp.git
    cd llama.cpp
    cmake -B build
    cmake --build build -j --target llama-server llama-cli
    # Start a local OpenAI-compatible server with a web UI:
    ./build/bin/llama-server -hf openbmb/MiniCPM-V-4-gguf:Q4_K_M
    # Run inference directly in the terminal:
    ./build/bin/llama-cli -hf openbmb/MiniCPM-V-4-gguf:Q4_K_M
    Use Docker
    docker model run hf.co/openbmb/MiniCPM-V-4-gguf:Q4_K_M
  • LM Studio
  • Jan
  • vLLM

    How to use openbmb/MiniCPM-V-4-gguf with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "openbmb/MiniCPM-V-4-gguf"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/chat/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "openbmb/MiniCPM-V-4-gguf",
    		"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/openbmb/MiniCPM-V-4-gguf:Q4_K_M
  • SGLang

    How to use openbmb/MiniCPM-V-4-gguf with SGLang:

    Install from pip and serve model
    # Install SGLang from pip:
    pip install sglang
    # Start the SGLang server:
    python3 -m sglang.launch_server \
        --model-path "openbmb/MiniCPM-V-4-gguf" \
        --host 0.0.0.0 \
        --port 30000
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:30000/v1/chat/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "openbmb/MiniCPM-V-4-gguf",
    		"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 images
    docker run --gpus all \
        --shm-size 32g \
        -p 30000:30000 \
        -v ~/.cache/huggingface:/root/.cache/huggingface \
        --env "HF_TOKEN=<secret>" \
        --ipc=host \
        lmsysorg/sglang:latest \
        python3 -m sglang.launch_server \
            --model-path "openbmb/MiniCPM-V-4-gguf" \
            --host 0.0.0.0 \
            --port 30000
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:30000/v1/chat/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "openbmb/MiniCPM-V-4-gguf",
    		"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"
    						}
    					}
    				]
    			}
    		]
    	}'
  • Ollama

    How to use openbmb/MiniCPM-V-4-gguf with Ollama:

    ollama run hf.co/openbmb/MiniCPM-V-4-gguf:Q4_K_M
  • Unsloth Desktop
  • Docker Model Runner

    How to use openbmb/MiniCPM-V-4-gguf with Docker Model Runner:

    docker model run hf.co/openbmb/MiniCPM-V-4-gguf:Q4_K_M
  • Lemonade

    How to use openbmb/MiniCPM-V-4-gguf with Lemonade:

    Pull the model
    # Download Lemonade from https://lemonade-server.ai/
    lemonade pull openbmb/MiniCPM-V-4-gguf:Q4_K_M
    Run and chat with the model
    lemonade run user.MiniCPM-V-4-gguf-Q4_K_M
    List all available models
    lemonade list
  • Atomic Chat
MiniCPM-V-4-gguf / ane_minicpmv4_vit_f16.mlmodelc
823 MB
Ctrl+K
Ctrl+K
  • 3 contributors
History: 1 commit
tc-mb
Initial commit: MiniCPM-V-4-gguf model
f174180 about 1 year ago
  • analytics
    Initial commit: MiniCPM-V-4-gguf model about 1 year ago
  • weights
    Initial commit: MiniCPM-V-4-gguf model about 1 year ago
  • coremldata.bin

    Pickle imports

    • No problematic imports detected

    What is a pickle import?

    713 Bytes
    xet
    Initial commit: MiniCPM-V-4-gguf model about 1 year ago
  • metadata.json
    2.32 kB
    Initial commit: MiniCPM-V-4-gguf model about 1 year ago
  • model.mil
    331 kB
    Initial commit: MiniCPM-V-4-gguf model about 1 year ago