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"

EldanRing commited on 4 days ago

Commit

859a2cb

verified ·

1 Parent(s): 6f3417c

Fix checksum and license links

Browse files

Files changed (1) hide show

README.md +2 -2

README.md CHANGED Viewed

@@ -44,7 +44,7 @@ Choose one target model. Add the matching projector for image input.
 | [Winnow-12B-NVFP4.gguf](https://huggingface.co/EldanRing/Winnow-12B/resolve/main/gguf/Winnow-12B-NVFP4.gguf?download=true) | Smaller Linux/CUDA 8K text and vision presets | 8.16 GB / 7.60 GiB |
 | [mmproj-Winnow-12B.gguf](https://huggingface.co/EldanRing/Winnow-12B/resolve/main/gguf/mmproj-Winnow-12B.gguf?download=true) | F16 vision projector for any of the three targets | 175 MB / 0.163 GiB |
-Use the exact target filename or a [Winnow preset](docs/RUNTIME-PROFILES.md). [Checksums](SHA256SUMS) identify every file.
 ## Running the model
@@ -97,6 +97,6 @@ Candidate probabilities are normalized over the supplied options. Confidence des
 ## Credits and license
-Winnow-12B is an independent fine-tune by EldanRing of Google DeepMind's [Gemma 4 12B IT](https://huggingface.co/google/gemma-4-12B-it), released under [Apache 2.0](https://ai.google.dev/gemma/docs/gemma_4_license). See [LICENSE](LICENSE) and [NOTICE](NOTICE).
 The separate inference code builds on [llama.cpp](https://github.com/ggml-org/llama.cpp) by Georgi Gerganov and contributors and preserves its MIT license. Jev-style refers to the typed-decision interface; Winnow is not affiliated with or endorsed by TypeSafe, Google, or llama.cpp.

 | [Winnow-12B-NVFP4.gguf](https://huggingface.co/EldanRing/Winnow-12B/resolve/main/gguf/Winnow-12B-NVFP4.gguf?download=true) | Smaller Linux/CUDA 8K text and vision presets | 8.16 GB / 7.60 GiB |
 | [mmproj-Winnow-12B.gguf](https://huggingface.co/EldanRing/Winnow-12B/resolve/main/gguf/mmproj-Winnow-12B.gguf?download=true) | F16 vision projector for any of the three targets | 175 MB / 0.163 GiB |
+Use the exact target filename or a [Winnow preset](docs/RUNTIME-PROFILES.md). [Checksums](https://huggingface.co/EldanRing/Winnow-12B/blob/main/SHA256SUMS) identify every file.
 ## Running the model
 ## Credits and license
+Winnow-12B is an independent fine-tune by EldanRing of Google DeepMind's [Gemma 4 12B IT](https://huggingface.co/google/gemma-4-12B-it), released under [Apache 2.0](https://ai.google.dev/gemma/docs/gemma_4_license). See [LICENSE](https://huggingface.co/EldanRing/Winnow-12B/blob/main/LICENSE) and [NOTICE](https://huggingface.co/EldanRing/Winnow-12B/blob/main/NOTICE).
 The separate inference code builds on [llama.cpp](https://github.com/ggml-org/llama.cpp) by Georgi Gerganov and contributors and preserves its MIT license. Jev-style refers to the typed-decision interface; Winnow is not affiliated with or endorsed by TypeSafe, Google, or llama.cpp.