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

Libraries

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

tc-mb commited on Aug 6, 2025

Commit

7a7580f

1 Parent(s): f174180

update readme

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README.md CHANGED Viewed

@@ -16,22 +16,22 @@ tags:
 <h1>A GPT-4V Level MLLM for Single Image, Multi Image and Video on Your Phone</h1>
-[GitHub](https://github.com/OpenBMB/MiniCPM-o) | [Demo](https://minicpm-v.openbmb.cn/)</a>
 ## MiniCPM-V 4.0
-**MiniCPM-V 4.0** is the latest model in the MiniCPM-V series. The model is built in an end-to-end fasion based on SigLip2-400M and MiniCPM4-3B with a total of 4.1B parameters. It inherents the strong single-image, multi-image and video understanding performance of MiniCPM-V 2.6 with the efficiency largely improved. Notable features of MiniCPM-V 4.0 include:
 - 🔥 **Leading Visual Capability.**
-  MiniCPM-V 4.0 achieves an average score of 69.0 on OpenCompass, a comprehensive evaluation of 8 popular benchmarks, outperforming both MiniCPM-V 2.6 (8.1B, 65.2) and Qwen2.5-VL-3B-Instruct (3.8B, 64.5). **With only 4.1B parameters, it surpasses the widely used proprietary model GPT-4.1-mini-20250414** for single image understanding.  It also outperforms MiniCPM-V 2.6 on both multi-image understanding and video understanding.
 - 🚀 **Superior Efficiency.**
-  Designed for performance on end devices, MiniCPM-V 4.0 runs smoothly on the **iPhone 16 Pro Max, devlivering 17.9 tokens/second decoding speed**. Compared to the already efficient MiniCPM-V 2.6, MiniCPM-V 4.0 further achieves a 30% throughput boost while offering enhanced visual understanding.
 -  💫  **Easy Usage.**
-  MiniCPM-V 4.0 can be easily used in various ways including **llama.cpp, Ollama, vLLM, SGLang, LLaMA-Factory and local web demo** etc. Get started easily with our **well-structured  [Cookbook](https://github.com/OpenSQZ/MiniCPM-V-CookBook)**, featuring detailed instructions and practical examples.
 ### Evaluation

 <h1>A GPT-4V Level MLLM for Single Image, Multi Image and Video on Your Phone</h1>
+[GitHub](https://github.com/OpenBMB/MiniCPM-o) | [Demo](http://211.93.21.133:8889/)</a>
 ## MiniCPM-V 4.0
+**MiniCPM-V 4.0** is the latest efficient model in the MiniCPM-V series. The model is built based on SigLIP2-400M and MiniCPM4-3B with a total of 4.1B parameters. It inherits the strong single-image, multi-image and video understanding performance of MiniCPM-V 2.6 with largely improved efficiency. Notable features of MiniCPM-V 4.0 include:
 - 🔥 **Leading Visual Capability.**
+   With only 4.1B parameters, MiniCPM-V 4.0 achieves an average score of 69.0 on OpenCompass, a comprehensive evaluation of 8 popular benchmarks, **outperforming GPT-4.1-mini-20250414, MiniCPM-V 2.6 (8.1B params, OpenCompass 65.2) and Qwen2.5-VL-3B-Instruct (3.8B params, OpenCompass 64.5)**. It also shows good performance in multi-image understanding and video understanding.
 - 🚀 **Superior Efficiency.**
+  Designed for on-device deployment, MiniCPM-V 4.0 runs smoothly on end devices. For example, it devlivers **less than 2s first token delay and more than 17 token/s decoding on iPhone 16 Pro Max**, without heating problems. It also shows superior throughput under concurrent requests.
 -  💫  **Easy Usage.**
+  MiniCPM-V 4.0 can be easily used in various ways including **llama.cpp, Ollama, vLLM, SGLang, LLaMA-Factory and local web demo** etc. We also open-source iOS App that can run on iPhone and iPad. Get started easily with our well-structured [Cookbook](https://github.com/OpenSQZ/MiniCPM-V-CookBook), featuring detailed instructions and practical examples.
 ### Evaluation