Instructions to use WhiskyAKM/MiniCPM-V-4.7-35B-A3B-NVFP4-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use WhiskyAKM/MiniCPM-V-4.7-35B-A3B-NVFP4-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 WhiskyAKM/MiniCPM-V-4.7-35B-A3B-NVFP4-GGUF:NVFP4
# Run inference directly in the terminal:
llama cli -hf WhiskyAKM/MiniCPM-V-4.7-35B-A3B-NVFP4-GGUF:NVFP4

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf WhiskyAKM/MiniCPM-V-4.7-35B-A3B-NVFP4-GGUF:NVFP4
# Run inference directly in the terminal:
llama cli -hf WhiskyAKM/MiniCPM-V-4.7-35B-A3B-NVFP4-GGUF:NVFP4

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 WhiskyAKM/MiniCPM-V-4.7-35B-A3B-NVFP4-GGUF:NVFP4
# Run inference directly in the terminal:
./llama-cli -hf WhiskyAKM/MiniCPM-V-4.7-35B-A3B-NVFP4-GGUF:NVFP4

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 WhiskyAKM/MiniCPM-V-4.7-35B-A3B-NVFP4-GGUF:NVFP4
# Run inference directly in the terminal:
./build/bin/llama-cli -hf WhiskyAKM/MiniCPM-V-4.7-35B-A3B-NVFP4-GGUF:NVFP4

Use Docker

docker model run hf.co/WhiskyAKM/MiniCPM-V-4.7-35B-A3B-NVFP4-GGUF:NVFP4

LM Studio
Jan

vLLM

How to use WhiskyAKM/MiniCPM-V-4.7-35B-A3B-NVFP4-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "WhiskyAKM/MiniCPM-V-4.7-35B-A3B-NVFP4-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": "WhiskyAKM/MiniCPM-V-4.7-35B-A3B-NVFP4-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/WhiskyAKM/MiniCPM-V-4.7-35B-A3B-NVFP4-GGUF:NVFP4

Ollama
How to use WhiskyAKM/MiniCPM-V-4.7-35B-A3B-NVFP4-GGUF with Ollama:
```
ollama run hf.co/WhiskyAKM/MiniCPM-V-4.7-35B-A3B-NVFP4-GGUF:NVFP4
```
Unsloth Desktop

How to use WhiskyAKM/MiniCPM-V-4.7-35B-A3B-NVFP4-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf WhiskyAKM/MiniCPM-V-4.7-35B-A3B-NVFP4-GGUF:NVFP4

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": "WhiskyAKM/MiniCPM-V-4.7-35B-A3B-NVFP4-GGUF:NVFP4"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use WhiskyAKM/MiniCPM-V-4.7-35B-A3B-NVFP4-GGUF with Docker Model Runner:
```
docker model run hf.co/WhiskyAKM/MiniCPM-V-4.7-35B-A3B-NVFP4-GGUF:NVFP4
```

Lemonade

How to use WhiskyAKM/MiniCPM-V-4.7-35B-A3B-NVFP4-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull WhiskyAKM/MiniCPM-V-4.7-35B-A3B-NVFP4-GGUF:NVFP4

Run and chat with the model

lemonade run user.MiniCPM-V-4.7-35B-A3B-NVFP4-GGUF-NVFP4

List all available models

lemonade list

Hermes Agent

How to use WhiskyAKM/MiniCPM-V-4.7-35B-A3B-NVFP4-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf WhiskyAKM/MiniCPM-V-4.7-35B-A3B-NVFP4-GGUF:NVFP4

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 WhiskyAKM/MiniCPM-V-4.7-35B-A3B-NVFP4-GGUF:NVFP4

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use WhiskyAKM/MiniCPM-V-4.7-35B-A3B-NVFP4-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf WhiskyAKM/MiniCPM-V-4.7-35B-A3B-NVFP4-GGUF:NVFP4

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 "WhiskyAKM/MiniCPM-V-4.7-35B-A3B-NVFP4-GGUF:NVFP4" \
  --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"

MiniCPM-V 4.7 35B-A3B NVFP4 — GGUF

NVFP4 GGUF conversions derived from the MiniCPM-V 4.7 35B-A3B model, providing highly efficient 4-bit floating-point quantization for multimodal tasks.

This repository provides NVFP4 (NVIDIA 4-bit floating-point) GGUF files, making the model usable with llama.cpp and other GGUF-compatible inference engines.

Model Overview

MiniCPM-V 4.7 35B-A3B is the first MoE (Mixture-of-Experts) model in the MiniCPM-V series. It is designed to advance the performance–efficiency Pareto frontier, delivering high capability density and smooth on-device inference for resource-constrained environments.

Model Architecture

Property	Value
Architecture	MiniCPM-V 4.7 (MoE)
Parameters	35B-A3B
Capabilities	Text, Image, Video
Key Technique	Intra-ViT early compression & MoE Architecture
Token Compression	Supports mixed 4x/16x visual token compression
Supported Modalities	Text, Image, Video
Context Length	High-efficiency multimodal context

GGUF Files

File	Format	Description
`minicpm-v-4.7-35b-a3b-nvfp4.gguf`	NVFP4	NVFP4 quantized model - optimized for efficiency
`mmproj.gguf`	—	Multimodal projector (vision/video)

About NVFP4: NVFP4 is NVIDIA's 4-bit floating-point format (E2M1). It offers significant memory savings while preserving quality close to bfloat16, and is accelerated on NVIDIA Blackwell GPUs via llama.cpp.

Usage

llama.cpp (CLI)

# Multimodal inference (Image/Video)
./llama-cli \
  -m minicpm-v-4.7-35b-a3b-nvfp4.gguf \
  --mmproj mmproj.gguf \
  -p "Describe this image in detail." \
  --temp 1.0 --top-k 64 --top-p 0.95

llama-server (OpenAI-compatible API)

# Multimodal server
./llama-server \
  -m minicpm-v-4.7-35b-a3b-nvfp4.gguf \
  --mmproj mmproj.gguf \
  --host [IP_ADDRESS] --port 8080

Generation Parameters

Recommended parameters for MiniCPM-V 4.7:

Parameter	Value
Temperature	1.0
Top-K	64
Top-P	0.95

Key Features

Efficiency: Advances the performance–efficiency Pareto frontier for MLLMs.
Architecture: First open-source MoE model in the series with early visual token compression.
On-Device Ready: Specifically optimized for smooth on-device inference without compromising performance.
Versatile: Supports single-image, multi-image, and video understanding.