Instructions to use openbmb/MiniCPM-o-2_6-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use openbmb/MiniCPM-o-2_6-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 openbmb/MiniCPM-o-2_6-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf openbmb/MiniCPM-o-2_6-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 openbmb/MiniCPM-o-2_6-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf openbmb/MiniCPM-o-2_6-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/MiniCPM-o-2_6-gguf:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf openbmb/MiniCPM-o-2_6-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/MiniCPM-o-2_6-gguf:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf openbmb/MiniCPM-o-2_6-gguf:Q4_K_M
Use Docker
docker model run hf.co/openbmb/MiniCPM-o-2_6-gguf:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use openbmb/MiniCPM-o-2_6-gguf with Ollama:
ollama run hf.co/openbmb/MiniCPM-o-2_6-gguf:Q4_K_M
- Unsloth Studio
How to use openbmb/MiniCPM-o-2_6-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/MiniCPM-o-2_6-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/MiniCPM-o-2_6-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/MiniCPM-o-2_6-gguf to start chatting
- Pi
How to use openbmb/MiniCPM-o-2_6-gguf with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf openbmb/MiniCPM-o-2_6-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/MiniCPM-o-2_6-gguf:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use openbmb/MiniCPM-o-2_6-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 openbmb/MiniCPM-o-2_6-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/MiniCPM-o-2_6-gguf:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use openbmb/MiniCPM-o-2_6-gguf with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf openbmb/MiniCPM-o-2_6-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 "openbmb/MiniCPM-o-2_6-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"
- Docker Model Runner
How to use openbmb/MiniCPM-o-2_6-gguf with Docker Model Runner:
docker model run hf.co/openbmb/MiniCPM-o-2_6-gguf:Q4_K_M
- Lemonade
How to use openbmb/MiniCPM-o-2_6-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull openbmb/MiniCPM-o-2_6-gguf:Q4_K_M
Run and chat with the model
lemonade run user.MiniCPM-o-2_6-gguf-Q4_K_M
List all available models
lemonade list
How to get speech synthesized and speech recognized?
Following file: mmproj-model-f16.gguf
is used for image understanding, or can it also be used for ASR (automatic speech recognition) and/or speech synthesis (TTS)? If not then how can this gguf model be used to do that.
Please explain with detailed answer.
Readme shows that this repo only supports image inputs. The support for audios is in developing.
I am also developing other parts of minicpm-omni as quickly as possible, which will be open sourced to the community in the near future, including the asr part.
Thank you for working on great model, I wish I could help, though have limited skills. But for learning purposes, what can I do to develop the audio synthesis and ASR part?
any update?