Hugging Face's logo Hugging Face
  • Models
  • Datasets
  • Spaces
  • Buckets new
  • Docs
  • Enterprise
  • Pricing

  • Log In
  • Sign Up

openbmb
/
MiniCPM4-8B-mlx

Text Generation
Transformers
Safetensors
Chinese
English
minicpm4
conversational
custom_code
4-bit precision
Model card Files Files and versions
xet
Community
1

Instructions to use openbmb/MiniCPM4-8B-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use openbmb/MiniCPM4-8B-mlx with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="openbmb/MiniCPM4-8B-mlx", trust_remote_code=True)
    messages = [
        {"role": "user", "content": "Who are you?"},
    ]
    pipe(messages)
    # Load model directly
    from transformers import AutoModelForCausalLM
    model = AutoModelForCausalLM.from_pretrained("openbmb/MiniCPM4-8B-mlx", trust_remote_code=True, dtype="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • vLLM

    How to use openbmb/MiniCPM4-8B-mlx with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "openbmb/MiniCPM4-8B-mlx"
    # 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/MiniCPM4-8B-mlx",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
    Use Docker
    docker model run hf.co/openbmb/MiniCPM4-8B-mlx
  • SGLang

    How to use openbmb/MiniCPM4-8B-mlx 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/MiniCPM4-8B-mlx" \
        --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/MiniCPM4-8B-mlx",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
    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/MiniCPM4-8B-mlx" \
            --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/MiniCPM4-8B-mlx",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
  • Docker Model Runner

    How to use openbmb/MiniCPM4-8B-mlx with Docker Model Runner:

    docker model run hf.co/openbmb/MiniCPM4-8B-mlx
New discussion
Resources
  • PR & discussions documentation
  • Code of Conduct
  • Hub documentation

Improve model card: Update paper link, add project page and relevant tags

#1 opened 8 months ago by
nielsr
Company
TOS Privacy About Careers
Website
Models Datasets Spaces Pricing Docs