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 20 days ago

Commit

2f2c499

verified ·

1 Parent(s): b703405

Pin verified inference source and immutable model downloads

Browse files

Files changed (2) hide show

docs/QUICKSTART.md +3 -0
release-manifest.json +3 -1

docs/QUICKSTART.md CHANGED Viewed

@@ -10,6 +10,7 @@ typed-decision integration.
 ```sh
 git clone https://github.com/EldanRing/winnow-inference.git
 cd winnow-inference
 python3 scripts/build.py --jobs 8
 ```
@@ -19,6 +20,7 @@ Use the Hugging Face CLI:
 ```sh
 hf download EldanRing/Winnow-12B \
   gguf/Winnow-12B-Q8_0.gguf gguf/mmproj-F16.gguf SHA256SUMS \
   --local-dir models/Winnow-12B
@@ -95,6 +97,7 @@ To download the merged BF16 model without the GGUF files:
 ```sh
 hf download EldanRing/Winnow-12B \
   --exclude 'gguf/*' \
   --local-dir models/Winnow-12B-BF16
 ```

 ```sh
 git clone https://github.com/EldanRing/winnow-inference.git
 cd winnow-inference
+git checkout 563806df1be0a431dbbce0cebe0f11c7199d8dd9
 python3 scripts/build.py --jobs 8
 ```
 ```sh
 hf download EldanRing/Winnow-12B \
+  --revision b2b14213dfa252e6d6b543c8b334762e51772d29 \
   gguf/Winnow-12B-Q8_0.gguf gguf/mmproj-F16.gguf SHA256SUMS \
   --local-dir models/Winnow-12B
 ```sh
 hf download EldanRing/Winnow-12B \
+  --revision b2b14213dfa252e6d6b543c8b334762e51772d29 \
   --exclude 'gguf/*' \
   --local-dir models/Winnow-12B-BF16
 ```

release-manifest.json CHANGED Viewed

@@ -186,5 +186,7 @@
     "decision_parallel": 4,
     "chat_parallel": 1,
     "memory_policy": "exclusive"
-  }
 }

     "decision_parallel": 4,
     "chat_parallel": 1,
     "memory_policy": "exclusive"
+  },
+  "inference_revision": "563806df1be0a431dbbce0cebe0f11c7199d8dd9",
+  "model_artifact_revision": "b2b14213dfa252e6d6b543c8b334762e51772d29"
 }