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
Broken Tool Calling?
Tool calling doesn't seem to work properly, maybe there is a template issue? Has anyone got this working?
Hey @And1mon I personnaly have no issue with thinking, it is reliably working. However, I can also observe no tool calling is working, at least with the llama-server webui (for this test, I used --tools all and tried to make it use get_datetime) or openwebui (trying to use get_current_timestamp).
In both case, it correctly sees the list of tools available, but can't call them correctly and output for example name="get_current_timestamp">
`
Example:
[
{
"type": "reasoning",
"id": "r_817333d3d4a7488e8b79a7ae",
"status": "completed",
"start_tag": "<think>",
"end_tag": "</think>",
"attributes": {
"type": "reasoning_content"
},
"content": [
{
"type": "output_text",
"text": "Okay, the user is asking me to demonstrate the get_current_timestamp tool. First, I need to check if I have the right function. Looking back at the tools provided, there's get_current_timestamp with no parameters, and calculate_timestamp with parameters days_ago, weeks_ago, etc.\n\nThe user wants to demonstrate using get_current_timestamp, so I should call that function. The parameters are all null in get_current_timestamp, but the description says it gets the current Unix timestamp in seconds. So I need to make sure to call it without any arguments.\n\nWait, the parameters for get_current_timestamp are listed as {\"properties\": {}, \"type\": \"object\"} so no parameters. So the tool call should be name \"get_current_timestamp\" with empty arguments.\n\nThe user said \"Can you use get_current_timestamp to demonstrate?\" so I should generate a tool call for that function. Since the instructions say to return the tool call in JSON within tool_call tags.\n\nI need to make sure I don't miss any steps. The user might want to see the actual timestamp, but since they didn't provide a time range, maybe the tool is called correctly by default. The calculate_timestamp tool can also be used, but the user is asking specifically about demonstrating get_current_timestamp.\n\nSo I should output the tool call for get_current_timestamp. Let me check the example structure. The example shows \"function name arguments\". Since get_current_timestamp has no parameters, the arguments should be empty object.\n\nSo the correct tool call is {\"name\": \"get_current_timestamp\", \"arguments\": {}}\n\nWait, in the parameters, the get_current_timestamp has \"properties\": {}, \"type\": \"object\", so no parameters. So the arguments should be an empty object.\n\nI should format the tool call as:\n\n{\"name\": \"get_current_timestamp\", \"arguments\": {}}\n\nYes, that's correct.\n\nI need to make sure not to call any other tools. The user only asked about demonstrating get_current_timestamp, so only that function.\n\nSo the response should be to generate that tool call.\n"
}
],
"summary": null,
"started_at": 1779800689.0132089,
"ended_at": 1779800696.767792,
"duration": 7
},
{
"type": "message",
"id": "msg_224367e09dd3464ea549ed1b",
"status": "completed",
"role": "assistant",
"content": [
{
"type": "output_text",
"text": "name=\"get_current_timestamp\">"
}
]
}
]
Yeah the thinking issue was user error on my side, however tool calling appears broken. I think it outputs a tool calling format llama.cpp cannot interpret.
Tool Calling
For tool / function calling, SGLang is the recommended backend. MiniCPM5-1B emits XML-style tool calls and SGLang's built-in minicpm5 parser converts them to OpenAI-compatible tool_calls natively:
python -m sglang.launch_server --model-path openbmb/MiniCPM5-1B --port 30000
--tool-call-parser minicpm5 # or: --tool-call-parser auto
Thanks for raising this!
For llama.cpp, chat completions are supported today. For tool calling, we recommend SGLang or vLLM, which handle MiniCPM5’s tool call format natively (e.g. SGLang with --tool-call-parser minicpm5).