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

Use Docker

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

LM Studio
Jan

vLLM

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

Install from pip and serve model

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

Use Docker

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

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

How to use abenzerps/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 abenzerps/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": "abenzerps/MiniCPM5-2B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

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

Lemonade

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

Pull the model

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

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use abenzerps/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 abenzerps/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 "abenzerps/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"

MiniCPM5-2B GGUF

GGUF quantizations of OpenBMB/MiniCPM5-2B, a 2B dense Llama-based model for local deployment, coding, reasoning, long-context work, and tool use. The source checkpoint supports a native context length of 131,072 tokens (128K).

Benchmarks

Benchmark results reported by OpenBMB for the original MiniCPM5-2B checkpoint.

Capability comparison reported by OpenBMB for the original MiniCPM5-2B checkpoint.

GGUF files

Quantization	File	Size
Q2_K	MiniCPM5-2B-Q2_K.gguf	1.04 GB
Q3_K_M	MiniCPM5-2B-Q3_K_M.gguf	1.29 GB
Q4_0	MiniCPM5-2B-Q4_0.gguf	1.49 GB
Q4_K_S	MiniCPM5-2B-Q4_K_S.gguf	1.50 GB
Q4_K_M	MiniCPM5-2B-Q4_K_M.gguf	1.56 GB
Q5_K_M	MiniCPM5-2B-Q5_K_M.gguf	1.81 GB
Q6_K	MiniCPM5-2B-Q6_K.gguf	2.07 GB
Q8_0	MiniCPM5-2B-Q8_0.gguf	2.68 GB
IQ2_M	MiniCPM5-2B-IQ2_M.gguf	0.97 GB
IQ3_M	MiniCPM5-2B-IQ3_M.gguf	1.23 GB
IQ4_XS	MiniCPM5-2B-IQ4_XS.gguf	1.42 GB

The model is text-only. No vision projector or MTP files are included. The IQ files use an importance matrix generated from WikiText-2 and are intended for recent llama.cpp builds. SHA-256 checksums are provided in SHA256SUMS.txt.

Chat template

The GGUF files embed the upstream chat template. chat_template.jinja is provided as an external copy for runtimes that require a separate template file.

Usage

Use a current llama.cpp build with MiniCPM5 support. The example below uses an 8K context; increase -c up to 131072 when sufficient memory is available.

llama-cli \
  -m MiniCPM5-2B-Q4_K_M.gguf \
  -c 8192 --jinja \
  --temp 1.0 --top-p 0.95 \
  -p "Explain why reproducible builds matter."

For an OpenAI-compatible server:

llama-server \
  -m MiniCPM5-2B-Q4_K_M.gguf \
  -c 8192 --jinja --host 0.0.0.0 --port 8080

Tool-call behavior depends on the serving runtime's parser and API integration; use the embedded template and verify tool calls in the target application.

Source

Model: OpenBMB/MiniCPM5-2B
Source revision: 3497c460c89e00520c3cfa2e73f49ab7647f1177
Conversion: the original Q4_0–Q8_0 files use upstream llama.cpp commit f114f91f9ed6792cf402437e3874adad98902744; the additional Q2_K, Q3_K_M, Q4_K_S, IQ2_M, IQ3_M, and IQ4_XS files use upstream commit 67672dc5b76f8bc17785a19d3dc6d1463fc2902c
License: Apache-2.0
Checksums: SHA256SUMS.txt

Downloads last month: 3,734

GGUF

Model size

3B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for abenzerps/MiniCPM5-2B-GGUF

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openbmb/MiniCPM5-2B

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Collection including abenzerps/MiniCPM5-2B-GGUF

MiniCPM5-2B

Collection

MiniCPM5-2B model releases, including GGUF, MLX and DSpark conversions. • 5 items • Updated Sep 7