Instructions to use openbmb/MiniCPM5-1B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openbmb/MiniCPM5-1B-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="openbmb/MiniCPM5-1B-GGUF") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("openbmb/MiniCPM5-1B-GGUF", dtype="auto") - llama-cpp-python
How to use openbmb/MiniCPM5-1B-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="openbmb/MiniCPM5-1B-GGUF", filename="MiniCPM5-1B-F16.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use openbmb/MiniCPM5-1B-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf openbmb/MiniCPM5-1B-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf openbmb/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-server -hf openbmb/MiniCPM5-1B-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf openbmb/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 openbmb/MiniCPM5-1B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf openbmb/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 openbmb/MiniCPM5-1B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf openbmb/MiniCPM5-1B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/openbmb/MiniCPM5-1B-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use openbmb/MiniCPM5-1B-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "openbmb/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": "openbmb/MiniCPM5-1B-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/openbmb/MiniCPM5-1B-GGUF:Q4_K_M
- SGLang
How to use openbmb/MiniCPM5-1B-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 "openbmb/MiniCPM5-1B-GGUF" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "openbmb/MiniCPM5-1B-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "openbmb/MiniCPM5-1B-GGUF" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "openbmb/MiniCPM5-1B-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use openbmb/MiniCPM5-1B-GGUF with Ollama:
ollama run hf.co/openbmb/MiniCPM5-1B-GGUF:Q4_K_M
- Unsloth Studio new
How to use openbmb/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 openbmb/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 openbmb/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 openbmb/MiniCPM5-1B-GGUF to start chatting
- Pi new
How to use openbmb/MiniCPM5-1B-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf openbmb/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": "openbmb/MiniCPM5-1B-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use openbmb/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-server -hf openbmb/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 openbmb/MiniCPM5-1B-GGUF:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use openbmb/MiniCPM5-1B-GGUF with Docker Model Runner:
docker model run hf.co/openbmb/MiniCPM5-1B-GGUF:Q4_K_M
- Lemonade
How to use openbmb/MiniCPM5-1B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull openbmb/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
lmstudio 无法正常对话
- lmstudio 全部使用默认加载后在 chat 中无法正常对话,google ai 说是 lmstudio 未正确解析:chat template。
- lmstudio 设置面板 developer 已经开启了 “在预设中启用模型加载配置支持”。
2026-05-27 06:14:36 [DEBUG]
0.00.976.932 I init: chat template, example_format: 'You are a helpful assistantHelloHi thereHow are you?'
2026-05-27 06:14:36 [DEBUG]
0.00.977.490 I srv init: init: chat template, thinking = 0
2026-05-27 06:14:36 [DEBUG]
0.00.977.612 I srv update_slots: all slots are idle
- ./llama-server -m MiniCPM5-1B-F16.gguf --port 8080 -ngl 99 -c 8192 --jinja --flash-attn on --kv-offload --numa distribute 加载正常对话也正常
0.00.925.699 I init: chat template, example_format: '<s><|im_start|>system
You are a helpful assistant<|im_end|>
<|im_start|>user
Hello<|im_end|>
<|im_start|>assistant
Hi there<|im_end|>
<|im_start|>user
How are you?<|im_end|>
<|im_start|>assistant
<think>
'
0.00.927.570 I srv init: init: chat template, thinking = 1
Thanks for the report — your diagnosis looks correct. LM Studio isn’t applying the embedded Jinja chat template from the GGUF, so the prompt gets flattened (hence thinking = 0 and the broken example_format).
MiniCPM5 support was recently merged in llama.cpp and works with Jinja enabled. LM Studio bundles its own llama.cpp backend, so you’ll need to wait for them to ship a newer version. For now, use llama-server
Thanks you, now it's work.
LM Studio 0.4.15 (Build 1) release
- llama.cpp release b9334 (commit 5190c2e)
Can you add this message about runing only in LM Studio 0.4.15 (build 1)?
upd. It not working on LM Studio 0.4.15 (build 1)? Same effect.



