Image-Text-to-Text
GGUF
gemma4_unified
winnow
gemma4
typed-decisions
local-inference
vision
conversational
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"
Download processor_config.json from EldanRing/Winnow-12B: direct link, hf CLI and curl.
- Browser
- Download file 1.38 kB
-
https://huggingface.co/EldanRing/Winnow-12B/resolve/main/processor_config.json
- Command line
-
hf download hf://EldanRing/Winnow-12B/processor_config.json
-
curl -L -o processor_config.json https://huggingface.co/EldanRing/Winnow-12B/resolve/main/processor_config.json
1.38 kB
| { | |
| "audio_ms_per_token": 40, | |
| "audio_seq_length": 750, | |
| "feature_extractor": { | |
| "audio_samples_per_token": 640, | |
| "feature_extractor_type": "Gemma4UnifiedAudioFeatureExtractor", | |
| "feature_size": 640, | |
| "padding_side": "right", | |
| "padding_value": 0.0, | |
| "return_attention_mask": true, | |
| "sampling_rate": 16000 | |
| }, | |
| "image_processor": { | |
| "do_convert_rgb": true, | |
| "do_normalize": false, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_mean": [ | |
| 0.0, | |
| 0.0, | |
| 0.0 | |
| ], | |
| "image_processor_type": "Gemma4UnifiedImageProcessor", | |
| "image_std": [ | |
| 1.0, | |
| 1.0, | |
| 1.0 | |
| ], | |
| "max_soft_tokens": 280, | |
| "patch_size": 16, | |
| "pooling_kernel_size": 3, | |
| "resample": 3, | |
| "rescale_factor": 0.00392156862745098 | |
| }, | |
| "image_seq_length": 280, | |
| "processor_class": "Gemma4UnifiedProcessor", | |
| "video_processor": { | |
| "do_convert_rgb": true, | |
| "do_normalize": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "do_sample_frames": true, | |
| "image_mean": [ | |
| 0.0, | |
| 0.0, | |
| 0.0 | |
| ], | |
| "image_std": [ | |
| 1.0, | |
| 1.0, | |
| 1.0 | |
| ], | |
| "max_soft_tokens": 70, | |
| "num_frames": 32, | |
| "patch_size": 16, | |
| "pooling_kernel_size": 3, | |
| "resample": 3, | |
| "rescale_factor": 0.00392156862745098, | |
| "return_metadata": false, | |
| "video_processor_type": "Gemma4UnifiedVideoProcessor" | |
| } | |
| } | |