Instructions to use second-state/MiniCPM-V-4_5-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use second-state/MiniCPM-V-4_5-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="second-state/MiniCPM-V-4_5-GGUF",
	filename="MiniCPM-V-4_5-Q2_K.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use second-state/MiniCPM-V-4_5-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf second-state/MiniCPM-V-4_5-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf second-state/MiniCPM-V-4_5-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 second-state/MiniCPM-V-4_5-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf second-state/MiniCPM-V-4_5-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 second-state/MiniCPM-V-4_5-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf second-state/MiniCPM-V-4_5-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 second-state/MiniCPM-V-4_5-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf second-state/MiniCPM-V-4_5-GGUF:Q4_K_M

Use Docker

docker model run hf.co/second-state/MiniCPM-V-4_5-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use second-state/MiniCPM-V-4_5-GGUF with Ollama:
```
ollama run hf.co/second-state/MiniCPM-V-4_5-GGUF:Q4_K_M
```

Unsloth Studio new

How to use second-state/MiniCPM-V-4_5-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 second-state/MiniCPM-V-4_5-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 second-state/MiniCPM-V-4_5-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for second-state/MiniCPM-V-4_5-GGUF to start chatting

Pi new

How to use second-state/MiniCPM-V-4_5-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf second-state/MiniCPM-V-4_5-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": "second-state/MiniCPM-V-4_5-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use second-state/MiniCPM-V-4_5-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 second-state/MiniCPM-V-4_5-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 second-state/MiniCPM-V-4_5-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use second-state/MiniCPM-V-4_5-GGUF with Docker Model Runner:
```
docker model run hf.co/second-state/MiniCPM-V-4_5-GGUF:Q4_K_M
```

Lemonade

How to use second-state/MiniCPM-V-4_5-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull second-state/MiniCPM-V-4_5-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.MiniCPM-V-4_5-GGUF-Q4_K_M

List all available models

lemonade list

MiniCPM-V-4_5-GGUF

Original Model

openbmb/MiniCPM-V-4_5

Run with LlamaEdge

LlamaEdge version: coming soon

Prompt template

Prompt type: minicpmv

Prompt string

<|system|>
{system_message}<|end|>
<|user|>
{user_message_1}<|end|>
<|assistant|>
{assistant_message_1}<|end|>
<|user|>
{user_message_2}<|end|>
<|assistant|>

The {user_message_n} has the format: {image_base64_encoding_string}\n{user_question}.

Context size: 128000

Run as LlamaEdge service

wasmedge --dir .:. \
  --nn-preload default:GGML:AUTO:MiniCPM-V-4_5-Q5_K_M.gguf \
  llama-api-server.wasm \
  --prompt-template minicpmv \
  --ctx-size 128000 \
  --llava-mmproj MiniCPM-V-4_5-mmproj-f16.gguf \
  --model-name minicpmv-26

Quantized GGUF Models

Name	Quant method	Bits	Size	Use case
MiniCPM-V-4_5-Q2_K.gguf	Q2_K	2	3.28 GB	smallest, significant quality loss - not recommended for most purposes
MiniCPM-V-4_5-Q3_K_L.gguf	Q3_K_L	3	4.43 GB	small, substantial quality loss
MiniCPM-V-4_5-Q3_K_M.gguf	Q3_K_M	3	4.12 GB	very small, high quality loss
MiniCPM-V-4_5-Q3_K_S.gguf	Q3_K_S	3	3.77 GB	very small, high quality loss
MiniCPM-V-4_5-Q4_0.gguf	Q4_0	4	4.77 GB	legacy; small, very high quality loss - prefer using Q3_K_M
MiniCPM-V-4_5-Q4_K_M.gguf	Q4_K_M	4	5.03 GB	medium, balanced quality - recommended
MiniCPM-V-4_5-Q4_K_S.gguf	Q4_K_S	4	4.80 GB	small, greater quality loss
MiniCPM-V-4_5-Q5_0.gguf	Q5_0	5	5.72 GB	legacy; medium, balanced quality - prefer using Q4_K_M
MiniCPM-V-4_5-Q5_K_M.gguf	Q5_K_M	5	5.85 GB	large, very low quality loss - recommended
MiniCPM-V-4_5-Q5_K_S.gguf	Q5_K_S	5	5.72 GB	large, low quality loss - recommended
MiniCPM-V-4_5-Q6_K.gguf	Q6_K	6	6.72 GB	very large, extremely low quality loss
MiniCPM-V-4_5-Q8_0.gguf	Q8_0	8	8.71 GB	very large, extremely low quality loss - not recommended
MiniCPM-V-4_5-f16.gguf	f16	16	16.4 GB
MiniCPM-V-4_5-mmproj-f16.gguf	f16	16	1.10 GB

Quantized with llama.cpp b6301.

Downloads last month: 783

GGUF

Model size

8B params

Architecture

qwen3

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for second-state/MiniCPM-V-4_5-GGUF

Base model

openbmb/MiniCPM-V-4_5

Quantized

(4)

this model