Instructions to use Loke-60000/nautilus-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Loke-60000/nautilus-gguf 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 Loke-60000/nautilus-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Loke-60000/nautilus-gguf:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Loke-60000/nautilus-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Loke-60000/nautilus-gguf:Q4_K_M

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 Loke-60000/nautilus-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Loke-60000/nautilus-gguf:Q4_K_M

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 Loke-60000/nautilus-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Loke-60000/nautilus-gguf:Q4_K_M

Use Docker

docker model run hf.co/Loke-60000/nautilus-gguf:Q4_K_M

LM Studio
Jan

vLLM

How to use Loke-60000/nautilus-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Loke-60000/nautilus-gguf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Loke-60000/nautilus-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Loke-60000/nautilus-gguf:Q4_K_M

Ollama
How to use Loke-60000/nautilus-gguf with Ollama:
```
ollama run hf.co/Loke-60000/nautilus-gguf:Q4_K_M
```
Unsloth Desktop

How to use Loke-60000/nautilus-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Loke-60000/nautilus-gguf:Q4_K_M

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": "Loke-60000/nautilus-gguf:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Loke-60000/nautilus-gguf with Docker Model Runner:
```
docker model run hf.co/Loke-60000/nautilus-gguf:Q4_K_M
```

Lemonade

How to use Loke-60000/nautilus-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Loke-60000/nautilus-gguf:Q4_K_M

Run and chat with the model

lemonade run user.nautilus-gguf-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Loke-60000/nautilus-gguf with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Loke-60000/nautilus-gguf:Q4_K_M

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 Loke-60000/nautilus-gguf:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Loke-60000/nautilus-gguf with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Loke-60000/nautilus-gguf:Q4_K_M

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 "Loke-60000/nautilus-gguf:Q4_K_M" \
  --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"

Nautilus

Nautilus is a small terminal assistant, 0.8B parameters. You describe what you want in plain words, and it replies with the command to run. It works on Linux, macOS and Windows (PowerShell), and it's small enough to run on almost any machine: the q4_k_m file is about 530 MB.

Accuracy in %, full benchmarks run through their official harnesses, with every command executed and checked. The general models used each benchmark's standard prompt; Nautilus used its one-line system prompt.

What it does

Every reply is exactly one of three things:

Reply	When
a bare command, e.g. `du -sh ~/Downloads/* \| sort -h`	the request can be done in the terminal
`ANSWER: <one to three sentences>`	the job is done, or the question needs an explanation rather than a command
`UNCLEAR: <one question>`	the request is ambiguous, or running a guess could do damage

When you send the command's output back to it, it continues from there: the next command, or an ANSWER: once the task is done.

What it doesn't do

It isn't a chat model. It won't hold a conversation, write prose, or answer questions unrelated to the terminal.
It proposes commands but doesn't run them. Always read a command before you run it, especially anything that deletes, overwrites or changes permissions.
It gives one command per turn, not multi-line scripts.
It can get rare flags wrong. It's reliable on common tools but can invent a flag for less common ones.

How to use it

Use one short line as the system prompt, describing the machine. Nautilus was trained on this format:

nautilus on <OS> (<shell>), <user>@<host>, home <home directory>.

For example:

nautilus on Ubuntu 24.04 LTS (bash), sam@lab, home /home/sam.
nautilus on macOS 15 Sequoia (zsh), sam@lab, home /Users/sam.
nautilus on Windows 11 Pro (PowerShell 7), sam@lab, home C:\Users\sam.

Then send the request as the user message, and send the terminal output back as the next user message. Use temperature 0 and turn thinking off.

llama.cpp

llama-server -m nautilus-q4_k_m.gguf --jinja --reasoning off -c 8192

curl http://localhost:8080/v1/chat/completions -H 'Content-Type: application/json' -d '{
  "messages": [
    {"role": "system", "content": "nautilus on Ubuntu 24.04 LTS (bash), sam@lab, home /home/sam."},
    {"role": "user", "content": "which folders in my home take the most space?"}
  ],
  "temperature": 0
}'

A typical exchange:

user:      I want to move the pngs in ~/Pictures/2026 into a folder called shots
nautilus:  ls ~/Pictures/2026; test -d ~/Pictures/shots && echo exists || echo missing
user:      IMG_0431.png  IMG_0432.png  notes.txt
           missing
nautilus:  mkdir -p ~/Pictures/shots && mv ~/Pictures/2026/*.png ~/Pictures/shots/
user:      sam@lab:~$
nautilus:  ANSWER: Moved the two pngs into ~/Pictures/shots.

Files

File	Size
`nautilus-q4_k_m.gguf`	529 MB
`nautilus-q8_0.gguf`	812 MB
`nautilus-f16.gguf`	1.5 GB

Downloads last month: 183

GGUF

Model size

0.8B params

Architecture

qwen35

Hardware compatibility

4-bit

8-bit

16-bit

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