Instructions to use Menlo/Jan-nano-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 Menlo/Jan-nano-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 Menlo/Jan-nano-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf Menlo/Jan-nano-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 Menlo/Jan-nano-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf Menlo/Jan-nano-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 Menlo/Jan-nano-gguf:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Menlo/Jan-nano-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 Menlo/Jan-nano-gguf:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Menlo/Jan-nano-gguf:Q4_K_M
Use Docker
docker model run hf.co/Menlo/Jan-nano-gguf:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use Menlo/Jan-nano-gguf with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Menlo/Jan-nano-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": "Menlo/Jan-nano-gguf", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Menlo/Jan-nano-gguf:Q4_K_M
- Ollama
How to use Menlo/Jan-nano-gguf with Ollama:
ollama run hf.co/Menlo/Jan-nano-gguf:Q4_K_M
- Unsloth Studio
How to use Menlo/Jan-nano-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 Menlo/Jan-nano-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 Menlo/Jan-nano-gguf to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Menlo/Jan-nano-gguf to start chatting
- Pi
How to use Menlo/Jan-nano-gguf with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Menlo/Jan-nano-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": "Menlo/Jan-nano-gguf:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use Menlo/Jan-nano-gguf with Docker Model Runner:
docker model run hf.co/Menlo/Jan-nano-gguf:Q4_K_M
- Lemonade
How to use Menlo/Jan-nano-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Menlo/Jan-nano-gguf:Q4_K_M
Run and chat with the model
lemonade run user.Jan-nano-gguf-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use Menlo/Jan-nano-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 Menlo/Jan-nano-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 Menlo/Jan-nano-gguf:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use Menlo/Jan-nano-gguf with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Menlo/Jan-nano-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 "Menlo/Jan-nano-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"
About using non-jan app
#2
by alandao - opened
Repost from the other repo!
Hi you can use Qwen3 template from other lmstudio etc... compatible model but remember to disable "thinking" and add this system prompt when using
In this environment you have access to a set of tools you can use to answer the user's question. You can use one tool per message, and will receive the result of that tool use in the user's response. You use tools step-by-step to accomplish a given task, with each tool use informed by the result of the previous tool use.
Tool Use Rules
Here are the rules you should always follow to solve your task:
1. Always use the right arguments for the tools. Never use variable names as the action arguments, use the value instead.
2. Call a tool only when needed: do not call the search agent if you do not need information, try to solve the task yourself.
3. If no tool call is needed, just answer the question directly.
4. Never re-do a tool call that you previously did with the exact same parameters.
5. For tool use, MARK SURE use XML tag format as shown in the examples above. Do not use any other format.
In the meantime we will try to see if we can fix the gguf
Enjoy