Instructions to use Abiray/MiniCPM5-1B-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/MiniCPM5-1B-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/MiniCPM5-1B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Abiray/MiniCPM5-1B-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/MiniCPM5-1B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Abiray/MiniCPM5-1B-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/MiniCPM5-1B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Abiray/MiniCPM5-1B-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/MiniCPM5-1B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Abiray/MiniCPM5-1B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Abiray/MiniCPM5-1B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Abiray/MiniCPM5-1B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Abiray/MiniCPM5-1B-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/MiniCPM5-1B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Abiray/MiniCPM5-1B-GGUF:Q4_K_M

Ollama
How to use Abiray/MiniCPM5-1B-GGUF with Ollama:
```
ollama run hf.co/Abiray/MiniCPM5-1B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use Abiray/MiniCPM5-1B-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/MiniCPM5-1B-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/MiniCPM5-1B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Abiray/MiniCPM5-1B-GGUF with Docker Model Runner:
```
docker model run hf.co/Abiray/MiniCPM5-1B-GGUF:Q4_K_M
```

Lemonade

How to use Abiray/MiniCPM5-1B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Abiray/MiniCPM5-1B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.MiniCPM5-1B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Abiray/MiniCPM5-1B-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/MiniCPM5-1B-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/MiniCPM5-1B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Abiray/MiniCPM5-1B-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/MiniCPM5-1B-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/MiniCPM5-1B-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"

MiniCPM5-1B-GGUF

File size: 1,844 Bytes

b7c71f2

---
license: apache-2.0
language:
  - en
  - zh
pipeline_tag: text-generation
tags:
  - gguf
  - minicpm
  - minicpm5
  - llama
  - text-generation
  - tool-calling
  - on-device
  - edge-ai
  - llama.cpp
base_model: openbmb/MiniCPM5-1B
---

# MiniCPM5-1B (GGUF Quantizations)

This repository contains custom `GGUF` format quantizations of the [openbmb/MiniCPM5-1B](https://huggingface.co/openbmb/MiniCPM5-1B) model. 

MiniCPM5-1B is a highly capable 1-billion parameter Transformer built for on-device, local deployment, and resource-constrained scenarios. It utilizes a standard `LlamaForCausalLM` architecture, features hybrid reasoning (built-in `<think>` tokens), and supports a massive 131k context window.

## 📦 Available Files and Quantizations

These models were quantized specifically for high-efficiency CPU/Edge inference using the `llama.cpp` framework.

| Filename | Format | Size | Description |
| :--- | :--- | :--- | :--- |
| `minicpm5-1b-Q4_K_M.gguf` | Q4_K_M | 657 MB | Excellent balance of performance and size. **(Recommended for 4GB RAM/Mobile)** |
| `minicpm5-1b-Q5_K_M.gguf` | Q5_K_M | 751 MB | Higher accuracy, slight increase in size. |
| `minicpm5-1b-Q6_K.gguf` | Q6_K | 851 MB | Near-perfect fidelity to the base model. |
| `minicpm5-1b-Q8_0.gguf` | Q8_0 | 1.1 GB | Maximum quantized quality; fast loading. |
| `minicpm5-1b-f16.gguf` | F16 | 2.1 GB | Unquantized master weight container. |

## 🚀 Quick Start with llama.cpp

Because MiniCPM5-1B uses standard Llama architecture, it is fully supported by `llama.cpp` out of the box. No custom forks or kernels are required.

### 1. Interactive CLI
To run the model directly in your terminal using CPU threads:

```bash
./llama-cli -m minicpm5-1b-Q4_K_M.gguf -p "Artificial intelligence and local model deployment are transforming technology because" -n 256 -t 4