Instructions to use mondk/MiniCPM5-2B-IQ4_XS-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mondk/MiniCPM5-2B-IQ4_XS-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="mondk/MiniCPM5-2B-IQ4_XS-GGUF")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("mondk/MiniCPM5-2B-IQ4_XS-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use mondk/MiniCPM5-2B-IQ4_XS-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 mondk/MiniCPM5-2B-IQ4_XS-GGUF:IQ4_XS
# Run inference directly in the terminal:
llama cli -hf mondk/MiniCPM5-2B-IQ4_XS-GGUF:IQ4_XS

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf mondk/MiniCPM5-2B-IQ4_XS-GGUF:IQ4_XS
# Run inference directly in the terminal:
llama cli -hf mondk/MiniCPM5-2B-IQ4_XS-GGUF:IQ4_XS

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 mondk/MiniCPM5-2B-IQ4_XS-GGUF:IQ4_XS
# Run inference directly in the terminal:
./llama-cli -hf mondk/MiniCPM5-2B-IQ4_XS-GGUF:IQ4_XS

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 mondk/MiniCPM5-2B-IQ4_XS-GGUF:IQ4_XS
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mondk/MiniCPM5-2B-IQ4_XS-GGUF:IQ4_XS

Use Docker

docker model run hf.co/mondk/MiniCPM5-2B-IQ4_XS-GGUF:IQ4_XS

LM Studio
Jan

vLLM

How to use mondk/MiniCPM5-2B-IQ4_XS-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mondk/MiniCPM5-2B-IQ4_XS-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mondk/MiniCPM5-2B-IQ4_XS-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/mondk/MiniCPM5-2B-IQ4_XS-GGUF:IQ4_XS

SGLang

How to use mondk/MiniCPM5-2B-IQ4_XS-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "mondk/MiniCPM5-2B-IQ4_XS-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mondk/MiniCPM5-2B-IQ4_XS-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "mondk/MiniCPM5-2B-IQ4_XS-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mondk/MiniCPM5-2B-IQ4_XS-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use mondk/MiniCPM5-2B-IQ4_XS-GGUF with Ollama:
```
ollama run hf.co/mondk/MiniCPM5-2B-IQ4_XS-GGUF:IQ4_XS
```
Unsloth Desktop
Docker Model Runner
How to use mondk/MiniCPM5-2B-IQ4_XS-GGUF with Docker Model Runner:
```
docker model run hf.co/mondk/MiniCPM5-2B-IQ4_XS-GGUF:IQ4_XS
```

Lemonade

How to use mondk/MiniCPM5-2B-IQ4_XS-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mondk/MiniCPM5-2B-IQ4_XS-GGUF:IQ4_XS

Run and chat with the model

lemonade run user.MiniCPM5-2B-IQ4_XS-GGUF-IQ4_XS

List all available models

lemonade list

Atomic Chat

Model Information

MiniCPM5-2B has the following features:

Type: Causal Language Model
Architecture: Standard LlamaForCausalLM
Number of Parameters: 2,516,756,480
Number of Non-Embedding Parameters: 1,981,982,720
Number of Layers: 42
Number of Attention Heads (GQA): 16 for Q and 2 for KV
Context Length: 131,072

Limitations and Disclaimer

This model has no autonomous intent or legal personhood; its outputs are text generated from statistical patterns and may be inaccurate, biased, or offensive, and may be manipulated by carefully crafted prompts ("jailbreaks") into producing unintended content. Its responses on sensitive topics such as politics, health, finance, and law are not reviewed by experts and should not be treated as professional advice.

This model is provided "AS IS", without warranty of any kind, express or implied, and the developers are not liable for any damages arising from its use. Users must employ the model only for lawful, compliant, and ethical purposes, configure their own safeguards, and label AI-generated content where required; deliberate jailbreaking, injection attacks, or inducing harmful output is prohibited, and any such testing is at the user's own risk.

License

This repository and MiniCPM model weights are released under the Apache-2.0 License.

Downloads last month: -

GGUF

Model size

3B params

Architecture

llama

Hardware compatibility

4-bit

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MiniCPM5-2B-IQ4_XS-GGUF

Model Information

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