Instructions to use Wolkyen/Pluto-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Wolkyen/Pluto-1 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 Wolkyen/Pluto-1:Q4_0
# Run inference directly in the terminal:
llama cli -hf Wolkyen/Pluto-1:Q4_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Wolkyen/Pluto-1:Q4_0
# Run inference directly in the terminal:
llama cli -hf Wolkyen/Pluto-1:Q4_0

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf Wolkyen/Pluto-1:Q4_0
# Run inference directly in the terminal:
./build/bin/llama cli -hf Wolkyen/Pluto-1:Q4_0

Use Docker

docker model run hf.co/Wolkyen/Pluto-1:Q4_0

Llama new
LM Studio
Jan

vLLM

How to use Wolkyen/Pluto-1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Wolkyen/Pluto-1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Wolkyen/Pluto-1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Wolkyen/Pluto-1:Q4_0

Ollama
How to use Wolkyen/Pluto-1 with Ollama:
```
ollama run hf.co/Wolkyen/Pluto-1:Q4_0
```
Unsloth Desktop

How to use Wolkyen/Pluto-1 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Wolkyen/Pluto-1:Q4_0

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": "Wolkyen/Pluto-1:Q4_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Wolkyen/Pluto-1 with Docker Model Runner:
```
docker model run hf.co/Wolkyen/Pluto-1:Q4_0
```

Lemonade

How to use Wolkyen/Pluto-1 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Wolkyen/Pluto-1:Q4_0

Run and chat with the model

lemonade run user.Pluto-1-Q4_0

List all available models

lemonade list

Hermes Agent

How to use Wolkyen/Pluto-1 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Wolkyen/Pluto-1:Q4_0

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 Wolkyen/Pluto-1:Q4_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Wolkyen/Pluto-1 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Wolkyen/Pluto-1:Q4_0

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 "Wolkyen/Pluto-1:Q4_0" \
  --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"

Pluto 1 — The Final Open-Weight Pluto

Pluto 1 is a 1.2-billion-parameter instruction-tuned language model developed by Wolkyen, designed to deliver capable, responsive, and efficient AI in a compact package.

As the last open-weight model in the Pluto family, Pluto 1 marks the end of an important chapter for Wolkyen and the transition toward a new generation of closed-source flagship models, beginning with Pluto 1.5.

Key Features

1.2B Parameters: Compact and computationally efficient.
Instruction-Tuned: Designed to understand and follow natural-language instructions.
Versatile: Suitable for conversations, text generation, and general-purpose assistance.
Apache 2.0 License: Permits commercial use, modification, and redistribution under the license terms.
The Final Open-Weight Pluto: The last publicly released open-weight model in the Pluto family.

A Turning Point

Pluto 1 represents more than another model release. It is the final step in Pluto's open-weight journey and the foundation of a new era focused on advancing intelligence, speed, and efficiency.

While future Pluto generations move into the closed-source arena, Pluto 1 remains available to the community.

Pluto 1 — The End of an Era. The Beginning of Another.

Developed by Wolkyen
Website: https://wolkyen.grok.me

Downloads last month: 51

GGUF

Model size

1B params

Architecture

lfm2

Hardware compatibility

4-bit