Instructions to use mlx-community/MiniCPM-V-4.7-1B-5bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mlx-community/MiniCPM-V-4.7-1B-5bit with MLX:

# Make sure mlx-vlm is installed
# pip install --upgrade mlx-vlm

from mlx_vlm import load, generate
from mlx_vlm.prompt_utils import apply_chat_template
from mlx_vlm.utils import load_config

# Load the model
model, processor = load("mlx-community/MiniCPM-V-4.7-1B-5bit")
config = load_config("mlx-community/MiniCPM-V-4.7-1B-5bit")

# Prepare input
image = ["http://images.cocodataset.org/val2017/000000039769.jpg"]
prompt = "Describe this image."

# Apply chat template
formatted_prompt = apply_chat_template(
    processor, config, prompt, num_images=1
)

# Generate output
output = generate(model, processor, formatted_prompt, image)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use mlx-community/MiniCPM-V-4.7-1B-5bit 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/MiniCPM-V-4.7-1B-5bit"

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/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/MiniCPM-V-4.7-1B-5bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent

How to use mlx-community/MiniCPM-V-4.7-1B-5bit with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "mlx-community/MiniCPM-V-4.7-1B-5bit"

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 mlx-community/MiniCPM-V-4.7-1B-5bit

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use mlx-community/MiniCPM-V-4.7-1B-5bit with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "mlx-community/MiniCPM-V-4.7-1B-5bit"

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 "mlx-community/MiniCPM-V-4.7-1B-5bit" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

MiniCPM-V-4.7-1B-5bit / processor_config.json

prince-canuma

Add 5bit MLX conversion of openbmb/MiniCPM-V-4.7-1B (main e928101a)

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{
  "processor_class": "MiniCPMV4_7Processor",
  "image_processor": {
    "max_slice_nums": 9,
    "patch_size": 14,
    "scale_resolution": 448,
    "slice_mode": true,
    "use_image_id": true,
    "downsample_mode": "16x",
    "pack_image_by_patch": true,
    "image_mean": [
      0.5,
      0.5,
      0.5
    ],
    "image_std": [
      0.5,
      0.5,
      0.5
    ]
  },
  "video_processor": {
    "max_slice_nums": 9,
    "patch_size": 14,
    "scale_resolution": 448,
    "slice_mode": true,
    "use_image_id": true,
    "downsample_mode": "16x",
    "pack_image_by_patch": true,
    "max_num_frames": 128,
    "stack_frames": 1,
    "image_mean": [
      0.5,
      0.5,
      0.5
    ],
    "image_std": [
      0.5,
      0.5,
      0.5
    ]
  }
}

	{
	"processor_class": "MiniCPMV4_7Processor",
	"image_processor": {
	"max_slice_nums": 9,
	"patch_size": 14,
	"scale_resolution": 448,
	"slice_mode": true,
	"use_image_id": true,
	"downsample_mode": "16x",
	"pack_image_by_patch": true,
	"image_mean": [
	0.5,
	0.5,
	0.5
	],
	"image_std": [
	0.5,
	0.5,
	0.5
	]
	},
	"video_processor": {
	"max_slice_nums": 9,
	"patch_size": 14,
	"scale_resolution": 448,
	"slice_mode": true,
	"use_image_id": true,
	"downsample_mode": "16x",
	"pack_image_by_patch": true,
	"max_num_frames": 128,
	"stack_frames": 1,
	"image_mean": [
	0.5,
	0.5,
	0.5
	],
	"image_std": [
	0.5,
	0.5,
	0.5
	]
	}
	}