Instructions to use llaa33219/MiniCPM-V-4.6-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use llaa33219/MiniCPM-V-4.6-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="llaa33219/MiniCPM-V-4.6-GGUF",
	filename="MiniCPM-V-4.6-F16.gguf",
)

llm.create_chat_completion(
	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"
					}
				}
			]
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use llaa33219/MiniCPM-V-4.6-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf llaa33219/MiniCPM-V-4.6-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf llaa33219/MiniCPM-V-4.6-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf llaa33219/MiniCPM-V-4.6-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf llaa33219/MiniCPM-V-4.6-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 llaa33219/MiniCPM-V-4.6-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf llaa33219/MiniCPM-V-4.6-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 llaa33219/MiniCPM-V-4.6-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf llaa33219/MiniCPM-V-4.6-GGUF:Q4_K_M

Use Docker

docker model run hf.co/llaa33219/MiniCPM-V-4.6-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use llaa33219/MiniCPM-V-4.6-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "llaa33219/MiniCPM-V-4.6-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": "llaa33219/MiniCPM-V-4.6-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/llaa33219/MiniCPM-V-4.6-GGUF:Q4_K_M

Ollama
How to use llaa33219/MiniCPM-V-4.6-GGUF with Ollama:
```
ollama run hf.co/llaa33219/MiniCPM-V-4.6-GGUF:Q4_K_M
```

Unsloth Studio

How to use llaa33219/MiniCPM-V-4.6-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for llaa33219/MiniCPM-V-4.6-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for llaa33219/MiniCPM-V-4.6-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for llaa33219/MiniCPM-V-4.6-GGUF to start chatting

How to use llaa33219/MiniCPM-V-4.6-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf llaa33219/MiniCPM-V-4.6-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "llaa33219/MiniCPM-V-4.6-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use llaa33219/MiniCPM-V-4.6-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf llaa33219/MiniCPM-V-4.6-GGUF:Q4_K_M

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 llaa33219/MiniCPM-V-4.6-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use llaa33219/MiniCPM-V-4.6-GGUF with Docker Model Runner:
```
docker model run hf.co/llaa33219/MiniCPM-V-4.6-GGUF:Q4_K_M
```

Lemonade

How to use llaa33219/MiniCPM-V-4.6-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull llaa33219/MiniCPM-V-4.6-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.MiniCPM-V-4.6-GGUF-Q4_K_M

List all available models

lemonade list

MiniCPM-V-4.6 GGUF

GGUF format quantized versions of openbmb/MiniCPM-V-4.6.

Available Variants

File	Quantization	Size	BPW	Description
MiniCPM-V-4.6-F16.gguf	F16	1.5GB	16.0	Full precision
MiniCPM-V-4.6-Q4_K_M.gguf	Q4_K_M	812MB	8.5	4-bit, medium
MiniCPM-V-4.6-Q4_K_S.gguf	Q4_K_S	812MB	8.5	4-bit, small
MiniCPM-V-4.6-Q3_K_M.gguf	Q3_K_M	812MB	8.5	3-bit, medium
MiniCPM-V-4.6-Q3_K_S.gguf	Q3_K_S	812MB	8.5	3-bit, small
MiniCPM-V-4.6-Q2_K.gguf	Q2_K	812MB	8.5	2-bit, smallest

Model Info

Architecture: MiniCPMV4_6ForConditionalGeneration
Vision Encoder: SigLIP2-400M (27 layers, hidden_size=1152)
Language Model: Qwen3.5-0.8B (24 layers, hidden_size=1024)
Total Parameters: 1.3B
Context Length: 262K tokens
License: Apache-2.0

Usage

llama.cpp

llama-server -m MiniCPM-V-4.6-Q2_K.gguf --port 8080

Python

from llama_cpp import Llama

llm = Llama(model_path="./MiniCPM-V-4.6-Q2_K.gguf", n_ctx=512, n_threads=4)
output = llm.create_chat_completion(
    messages=[{"role": "user", "content": "你好，你是谁？"}],
    max_tokens=100,
    temperature=0.0,
)
print(output['choices'][0]['message']['content'])

Ollama

FROM ./MiniCPM-V-4.6-Q2_K.gguf

ollama create minicpm-v-4.6 -f Modelfile
ollama run minicpm-v-4.6

Quantization Guide

Type	Bits	Quality	Speed	Use Case
F16	16	Best	Slow	Development
Q4_K_M	4	Good	Fast	General use
Q3_K_M	3	Medium	Faster	Balanced
Q2_K	2	Lower	Fastest	Edge/mobile

Conversion

python convert_hf_to_gguf.py openbmb/MiniCPM-V-4.6 --outfile MiniCPM-V-4.6-F16.gguf --outtype f16
llama-quantize MiniCPM-V-4.6-F16.gguf MiniCPM-V-4.6-Q2_K.gguf Q2_K

License

Apache-2.0

Downloads last month: 886

GGUF

Model size

0.4B params

Architecture

qwen35

Hardware compatibility

2-bit

3-bit

4-bit

16-bit

Model tree for llaa33219/MiniCPM-V-4.6-GGUF

Base model

openbmb/MiniCPM-V-4.6

Quantized

(17)

this model

Collection including llaa33219/MiniCPM-V-4.6-GGUF

MiniCPM-V-4.6

Collection

1 item • Updated 6 days ago