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

Use Docker

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

LM Studio
Jan

vLLM

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

Install from pip and serve model

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

Use Docker

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

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

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

Run Pi

# Start Pi in your project directory:
pi

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

Lemonade

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

Pull the model

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

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use WhiskyAKM/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 WhiskyAKM/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 "WhiskyAKM/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 / README.md

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metadata

pipeline_tag: text-generation
base_model:
  - openbmb/MiniCPM5-2B
license: apache-2.0
license_name: apache-2.0
library_name: llama-cpp
tags:
  - minicpm5
  - text-generation
  - gguf
  - quantized
languages:
  - en
  - zh

MiniCPM5-2B GGUF

GGUF quantized version of openbmb/MiniCPM5-2B, the second model in the MiniCPM5 series. It is a dense 2B Transformer (LlamaForCausalLM) built for on-device, local deployment, and resource-constrained scenarios, reaching 2B-class open-source SOTA.

Model Overview

MiniCPM5-2B is designed for local assistants, coding agents, tool-use workflows, and reasoning scenarios where a compact model is preferred. It keeps a small deployment footprint while providing native long-context support (131,072 tokens), and was post-trained with RL + OPD (On-Policy Distillation). It supports a chat template with thinking/reasoning mode (controlled via enable_thinking) and XML-style tool calling.

Model Architecture

Property	Value
Architecture	LlamaForCausalLM (dense)
Total Parameters	2.52B
Non-Embedding Parameters	1.98B
Layers	42
Attention Heads (GQA)	16 Q / 2 KV
Context Length	131,072 (128K)
Original Precision	bfloat16
Supported Languages	en, zh

Available GGUF Files

File	Quantization	Size	Use Case
`minicpm5-2b-bf16.gguf`	BF16	4.7 GB	Full precision (reference quality)
`minicpm5-2b-Q8_0.gguf`	Q8_0	2.5 GB	Near-lossless, low overhead
`minicpm5-2b-Q6_K.gguf`	Q6_K	2.0 GB	Very high quality
`minicpm5-2b-Q5_K_M.gguf`	Q5_K_M	1.7 GB	High quality, balanced
`minicpm5-2b-Q5_K_S.gguf`	Q5_K_S	1.7 GB	High quality, slightly smaller
`minicpm5-2b-Q4_K_M.gguf`	Q4_K_M	1.5 GB	Recommended: good quality/size trade-off
`minicpm5-2b-Q4_K_S.gguf`	Q4_K_S	1.4 GB	Smaller, slight quality loss
`minicpm5-2b-Q4_0.gguf`	Q4_0	1.4 GB	Legacy 4-bit, broad compatibility

DSpark Draft Model (Speculative Decoding)

File	Quantization	Size	Use Case
`minicpm5-2b-dspark-bf16.gguf`	BF16	623 MB	Draft model, full precision
`minicpm5-2b-dspark-Q8_0.gguf`	Q8_0	334 MB	Draft model, near-lossless
`minicpm5-2b-dspark-Q4_K_M.gguf`	Q4_K_M	186 MB	Draft model, smallest footprint

These are the MiniCPM5-2B-DSpark draft models, trained for speculative decoding with MiniCPM5-2B. They accelerate generation without changing the target model's outputs.

Usage

llama.cpp CLI

./llama-cli \
  -m minicpm5-2b-Q4_K_M.gguf \
  -p "Explain quantum computing in simple terms." \
  --temp 1.0 --top-p 0.95

llama-server (OpenAI-compatible API)

./llama-server \
  -m minicpm5-2b-Q4_K_M.gguf \
  --host 0.0.0.0 --port 8080

The GGUF also works with Ollama and LM Studio.

Speculative Decoding (DSpark)

Pair the target model with a DSpark draft model to speed up inference. Draft model quality has minimal impact on output, so smaller quants (e.g. Q4_K_M) are usually fine:

./llama-server \
  -m minicpm5-2b-Q4_K_M.gguf \
  -md minicpm5-2b-dspark-Q4_K_M.gguf \
  --host 0.0.0.0 --port 8080

Thinking Mode

The model supports deep-thinking output. You can control it per request via the chat template, e.g. with an OpenAI-compatible API:

"chat_template_kwargs": {"enable_thinking": false}

Tool Calling

MiniCPM5-2B emits XML-style tool calls. Tool definitions are injected into the prompt, and tool results are returned in the observation/tool role. SGLang's built-in minicpm5 parser converts these to OpenAI-compatible tool_calls natively (see upstream model card).

Generation Parameters

Recommended parameters from the original model:

Parameter	Value
Temperature	1.0
Top-P	0.95

Quantization

These GGUF files were created from the BF16 source model using llama-quantize from the llama.cpp project.

Acknowledgements

Original model: openbmb/MiniCPM5-2B
Draft model: openbmb/MiniCPM5-2B-DSpark
Quantization tool: llama.cpp

License

Apache-2.0 License