Instructions to use Abiray/MiniCPM5-2B-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 Abiray/MiniCPM5-2B-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 Abiray/MiniCPM5-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Abiray/MiniCPM5-2B-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 Abiray/MiniCPM5-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Abiray/MiniCPM5-2B-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 Abiray/MiniCPM5-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Abiray/MiniCPM5-2B-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 Abiray/MiniCPM5-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Abiray/MiniCPM5-2B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Abiray/MiniCPM5-2B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Abiray/MiniCPM5-2B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Abiray/MiniCPM5-2B-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": "Abiray/MiniCPM5-2B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Abiray/MiniCPM5-2B-GGUF:Q4_K_M

Ollama
How to use Abiray/MiniCPM5-2B-GGUF with Ollama:
```
ollama run hf.co/Abiray/MiniCPM5-2B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use Abiray/MiniCPM5-2B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Abiray/MiniCPM5-2B-GGUF:Q4_K_M

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": "Abiray/MiniCPM5-2B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Abiray/MiniCPM5-2B-GGUF with Docker Model Runner:
```
docker model run hf.co/Abiray/MiniCPM5-2B-GGUF:Q4_K_M
```

Lemonade

How to use Abiray/MiniCPM5-2B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Abiray/MiniCPM5-2B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.MiniCPM5-2B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Abiray/MiniCPM5-2B-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 Abiray/MiniCPM5-2B-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 Abiray/MiniCPM5-2B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Abiray/MiniCPM5-2B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Abiray/MiniCPM5-2B-GGUF:Q4_K_M

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 "Abiray/MiniCPM5-2B-GGUF:Q4_K_M" \
  --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 Tech Report | MiniCPM Wiki (Chinese) | GitHub Repo | UltraData | Online Demo

English | 中文

Highlights

This repository provides quantized GGUF weights for MiniCPM5-2B, the second model in OpenBMB's MiniCPM5 series. It is a dense 2B Transformer scaling up the proven recipe for on-device deployment, edge AI, and local inference via llama.cpp, Ollama, and LM Studio.

🏆 2B-class open-source SOTA: MiniCPM5-2B achieves state-of-the-art performance against models of similar size and remains highly competitive with 4B-class architectures across code generation, mathematics, 128k long-context comprehension, tool use, and multi-step agentic workflows.

Available GGUF Files

Quantization	File Name	Size	Recommendation / Use Case
Q3_K_M	`MiniCPM5-2B-Q3_K_M.gguf`	1.29 GB	Ultra-compact; suitable for tight VRAM or RAM constraints.
Q4_K_S	`MiniCPM5-2B-Q4_K_S.gguf`	1.50 GB	Fast 4-bit quantization with minimal memory overhead.
Q4_K_M	`MiniCPM5-2B-Q4_K_M.gguf`	1.56 GB	Recommended: Best balance of speed, perplexity, and footprint.
Q5_K_M	`MiniCPM5-2B-Q5_K_M.gguf`	1.81 GB	High accuracy; preserves subtle reasoning and code logic.
Q6_K	`MiniCPM5-2B-Q6_K.gguf`	2.07 GB	High-fidelity 6-bit quantization; near-identical output to BF16.
Q8_0	`MiniCPM5-2B-Q8_0.gguf`	2.68 GB	Near-lossless 8-bit quantization for maximal benchmark fidelity.

Quickstart Guide

llama.cpp

Run inference using llama-cli:

llama-cli \
  -m MiniCPM5-2B-Q4_K_M.gguf \
  -p "Who are you? Please briefly introduce yourself." \
  -n 256 \
  -c 4096 \
  --temp 1.0 \
  --top-p 0.95

Downloads last month: 7

GGUF

Model size

3B params

Architecture

llama

Hardware compatibility

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for Abiray/MiniCPM5-2B-GGUF

Base model

openbmb/MiniCPM5-2B

Quantized

(29)

this model

Datasets used to train Abiray/MiniCPM5-2B-GGUF

Paper for Abiray/MiniCPM5-2B-GGUF

MiniCPM4: Ultra-Efficient LLMs on End Devices

Paper • 2506.07900 • Published Jun 9, 2025 • 102