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mlx-community
/
MiniCPM3-4B-bfloat16

Text Generation
Transformers
Safetensors
MLX
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minicpm3
conversational
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xet
Community

Instructions to use mlx-community/MiniCPM3-4B-bfloat16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use mlx-community/MiniCPM3-4B-bfloat16 with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="mlx-community/MiniCPM3-4B-bfloat16", trust_remote_code=True)
    messages = [
        {"role": "user", "content": "Who are you?"},
    ]
    pipe(messages)
    # Load model directly
    from transformers import AutoModelForCausalLM
    model = AutoModelForCausalLM.from_pretrained("mlx-community/MiniCPM3-4B-bfloat16", trust_remote_code=True, dtype="auto")
  • MLX

    How to use mlx-community/MiniCPM3-4B-bfloat16 with MLX:

    # Make sure mlx-lm is installed
    # pip install --upgrade mlx-lm
    
    # Generate text with mlx-lm
    from mlx_lm import load, generate
    
    model, tokenizer = load("mlx-community/MiniCPM3-4B-bfloat16")
    
    prompt = "Write a story about Einstein"
    messages = [{"role": "user", "content": prompt}]
    prompt = tokenizer.apply_chat_template(
        messages, add_generation_prompt=True
    )
    
    text = generate(model, tokenizer, prompt=prompt, verbose=True)
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • LM Studio
  • vLLM

    How to use mlx-community/MiniCPM3-4B-bfloat16 with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "mlx-community/MiniCPM3-4B-bfloat16"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/chat/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "mlx-community/MiniCPM3-4B-bfloat16",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
    Use Docker
    docker model run hf.co/mlx-community/MiniCPM3-4B-bfloat16
  • SGLang

    How to use mlx-community/MiniCPM3-4B-bfloat16 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 "mlx-community/MiniCPM3-4B-bfloat16" \
        --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": "mlx-community/MiniCPM3-4B-bfloat16",
    		"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 "mlx-community/MiniCPM3-4B-bfloat16" \
            --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": "mlx-community/MiniCPM3-4B-bfloat16",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
  • Pi new

    How to use mlx-community/MiniCPM3-4B-bfloat16 with Pi:

    Start the MLX server
    # Install MLX LM:
    uv tool install mlx-lm
    # Start a local OpenAI-compatible server:
    mlx_lm.server --model "mlx-community/MiniCPM3-4B-bfloat16"
    Configure the model in Pi
    # Install Pi:
    npm install -g @mariozechner/pi-coding-agent
    # Add to ~/.pi/agent/models.json:
    {
      "providers": {
        "mlx-lm": {
          "baseUrl": "http://localhost:8080/v1",
          "api": "openai-completions",
          "apiKey": "none",
          "models": [
            {
              "id": "mlx-community/MiniCPM3-4B-bfloat16"
            }
          ]
        }
      }
    }
    Run Pi
    # Start Pi in your project directory:
    pi
  • MLX LM

    How to use mlx-community/MiniCPM3-4B-bfloat16 with MLX LM:

    Generate or start a chat session
    # Install MLX LM
    uv tool install mlx-lm
    # Interactive chat REPL
    mlx_lm.chat --model "mlx-community/MiniCPM3-4B-bfloat16"
    Run an OpenAI-compatible server
    # Install MLX LM
    uv tool install mlx-lm
    # Start the server
    mlx_lm.server --model "mlx-community/MiniCPM3-4B-bfloat16"
    # Calling the OpenAI-compatible server with curl
    curl -X POST "http://localhost:8000/v1/chat/completions" \
       -H "Content-Type: application/json" \
       --data '{
         "model": "mlx-community/MiniCPM3-4B-bfloat16",
         "messages": [
           {"role": "user", "content": "Hello"}
         ]
       }'
  • Docker Model Runner

    How to use mlx-community/MiniCPM3-4B-bfloat16 with Docker Model Runner:

    docker model run hf.co/mlx-community/MiniCPM3-4B-bfloat16
MiniCPM3-4B-bfloat16
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  • 1 contributor
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