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