Instructions to use EldanRing/Winnow-12B 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 EldanRing/Winnow-12B 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 EldanRing/Winnow-12B:BF16 # Run inference directly in the terminal: llama cli -hf EldanRing/Winnow-12B:BF16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf EldanRing/Winnow-12B:BF16 # Run inference directly in the terminal: llama cli -hf EldanRing/Winnow-12B:BF16
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 EldanRing/Winnow-12B:BF16 # Run inference directly in the terminal: ./llama-cli -hf EldanRing/Winnow-12B:BF16
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 EldanRing/Winnow-12B:BF16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf EldanRing/Winnow-12B:BF16
Use Docker
docker model run hf.co/EldanRing/Winnow-12B:BF16
- LM Studio
- Jan
- vLLM
How to use EldanRing/Winnow-12B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "EldanRing/Winnow-12B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "EldanRing/Winnow-12B", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/EldanRing/Winnow-12B:BF16
- Ollama
How to use EldanRing/Winnow-12B with Ollama:
ollama run hf.co/EldanRing/Winnow-12B:BF16
- Unsloth Desktop
- Pi
How to use EldanRing/Winnow-12B with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf EldanRing/Winnow-12B:BF16
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "EldanRing/Winnow-12B:BF16" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use EldanRing/Winnow-12B with Docker Model Runner:
docker model run hf.co/EldanRing/Winnow-12B:BF16
- Lemonade
How to use EldanRing/Winnow-12B with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull EldanRing/Winnow-12B:BF16
Run and chat with the model
lemonade run user.Winnow-12B-BF16
List all available models
lemonade list
- Hermes Agent
How to use EldanRing/Winnow-12B with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf EldanRing/Winnow-12B:BF16
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 EldanRing/Winnow-12B:BF16
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use EldanRing/Winnow-12B with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf EldanRing/Winnow-12B:BF16
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 "EldanRing/Winnow-12B:BF16" \ --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"
MTP / dflash2 for LLM completions API
Thank you for your work!
Is it possible to use MTP or dflash draft heads with this model? If yes which ones to download?
Thanks!
Yes! MTP support is now included in the latest Winnow inference release. I’ve uploaded the matching Gemma 4 12B assistant GGUF here too, and the downloader can fetch it when you enable MTP.
Use --mtp on with the download and serve commands. It accelerates chat/reasoning generation; direct decisions read answer logits without generating tokens, so MTP doesn’t speed up that part.
DFlash/DFlash2 isn’t included in this release. The supported path is the matching MTP assistant.
https://github.com/EldanRing/winnow-inference/blob/main/docs/QUICKSTART.md
Amazing, will give it a shot later!
Thank you!