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

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

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"

Winnow-12B

File size: 2,083 Bytes

fdaebc9

# Runtime profiles and compatibility

These named presets were tested on Linux/CUDA with RTX5070Ti16GB. Observed memory is profile-specific; it is not a universal GPU-fit guarantee.

| Tested preset | Context | Batch / microbatch | Vision | Observed peak device memory |
|---|---:|---|---|---:|
| `12b-nvfp4-vision8k-mtp` |8,192 |2,048 /1,024 |yes |11,773 MiB |
| `12b-q8-text8k-mtp` |8,192 |512 /256 |no |14,927–15,108 MiB |

All use q8_0 target/draft KV, four native branches, one chat slot, full GPU residency, AUTO memory and MTP depth4. These are measured defaults. The unified presets `q8`, `nv4` and `e4b` accept context, cache, batch and native-branch overrides. MTP requires one chat slot and auto memory. Custom settings do not inherit the measured calibration or performance claims; `winnow presets` lists advisory memory estimates. The recorded build used CUDA13.3/SM120. Install the server prerequisites and select the appropriate supported build settings.

## Supported combinations

Q8 vision plus MTP is available with explicit flags, but its measured 8K configuration exceeded 16 GB. Use Q8 MTP through the text preset, NVFP4 for the tested combined vision+MTP preset, or direct Q8 vision without MTP. A larger-memory Q8 combined configuration has not been verified. BF16 has no validated MTP/adaptive preset.

MTP and vision require additional VRAM. Quantization, context, batch and concurrency change memory use. Larger context/concurrency or different hardware is not established by these measurements. Direct vision and adaptive text serving are separate profiles; stop one before starting the other. Adaptive input supports one named question and a text state, with no image calibration claim.

## Measurement scope

Memory peaks are sampled device usage and may miss transients. They are not sustained-capacity measurements. These historical measurements retain their original runtime provenance; later compact parity checks are not new broad memory/quality evaluations. Timing methods and direct64Kvision measurements are in [BENCHMARKS.md](BENCHMARKS.md).