Instructions to use Abiray/MiniCPM5-1B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Abiray/MiniCPM5-1B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Abiray/MiniCPM5-1B-GGUF",
	filename="minicpm5-1b-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

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 Studio

How to use Abiray/MiniCPM5-1B-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 Abiray/MiniCPM5-1B-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 Abiray/MiniCPM5-1B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Abiray/MiniCPM5-1B-GGUF to start chatting

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 @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": "Abiray/MiniCPM5-1B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

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 new

OpenClaw new

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"

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

MiniCPM5-1B (GGUF Quantizations)

This repository contains custom GGUF format quantizations of the 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:

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

Downloads last month: 5,184

GGUF

Model size

1B params

Architecture

llama

Hardware compatibility

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for Abiray/MiniCPM5-1B-GGUF

Base model

openbmb/MiniCPM5-1B

Quantized

(76)

this model

Quantizations

1 model

Abiray
/

MiniCPM5-1B-GGUF

MiniCPM5-1B (GGUF Quantizations)

📦 Available Files and Quantizations

🚀 Quick Start with llama.cpp

1. Interactive CLI

Model tree for Abiray/MiniCPM5-1B-GGUF

Spaces using Abiray/MiniCPM5-1B-GGUF 10