Instructions to use Loke-60000/nautilus-preview-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use Loke-60000/nautilus-preview-gguf with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Loke-60000/nautilus-preview-gguf", filename="nautilus-v5-f16.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Loke-60000/nautilus-preview-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-preview-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf Loke-60000/nautilus-preview-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-preview-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf Loke-60000/nautilus-preview-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-preview-gguf:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Loke-60000/nautilus-preview-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-preview-gguf:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Loke-60000/nautilus-preview-gguf:Q4_K_M
Use Docker
docker model run hf.co/Loke-60000/nautilus-preview-gguf:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use Loke-60000/nautilus-preview-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-preview-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-preview-gguf", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Loke-60000/nautilus-preview-gguf:Q4_K_M
- Ollama
How to use Loke-60000/nautilus-preview-gguf with Ollama:
ollama run hf.co/Loke-60000/nautilus-preview-gguf:Q4_K_M
- Unsloth Studio
How to use Loke-60000/nautilus-preview-gguf with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Loke-60000/nautilus-preview-gguf to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Loke-60000/nautilus-preview-gguf to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Loke-60000/nautilus-preview-gguf to start chatting
- Pi
How to use Loke-60000/nautilus-preview-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-preview-gguf:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/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-preview-gguf:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use Loke-60000/nautilus-preview-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-preview-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-preview-gguf:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use Loke-60000/nautilus-preview-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-preview-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-preview-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"
- Docker Model Runner
How to use Loke-60000/nautilus-preview-gguf with Docker Model Runner:
docker model run hf.co/Loke-60000/nautilus-preview-gguf:Q4_K_M
- Lemonade
How to use Loke-60000/nautilus-preview-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Loke-60000/nautilus-preview-gguf:Q4_K_M
Run and chat with the model
lemonade run user.nautilus-preview-gguf-Q4_K_M
List all available models
lemonade list
nautilus-preview-gguf
GGUF builds of nautilus-preview, the model behind snail — a zsh plugin that turns plain English at the prompt into a shell command, an answer, or a commit message.
| file | size | use |
|---|---|---|
nautilus-v6-q8_0.gguf |
812 MB | recommended. Matches bf16 on every behaviour measured |
nautilus-v6-q4_k_m.gguf |
529 MB | smallest; loses the documentation-lookup behaviour (see below) |
nautilus-v6-f16.gguf |
1.5 GB | reference, no reason to serve it |
v5 files are kept alongside for comparison.
Pick Q8_0 if you use command_help
v6 can reply HELP: <tool> when it does not know a tool well enough to write the command,
and snail then feeds back that tool's tldr page, --help, or man. On the ten golden
records that warrant a lookup, bf16 and Q8_0 both ask 10/10. Q4_K_M asks 4/10 and
answers the rest from its own guesses, which is the exact failure the feature exists to
prevent. For plain command translation the two are close; for knowing what it does not
know, they are not.
Serve it
llama-server -m nautilus-v6-q8_0.gguf --alias nautilus \
--host 127.0.0.1 --port 8080 --ctx-size 8192 --jinja -ngl 99
Then point snail's config at it:
endpoint = 'http://127.0.0.1:8080/v1/chat/completions'
model = 'nautilus'
command_help = true # optional
Send enable_thinking: false — roughly 85% of training is the no-think path, and that is
the shipped path. Q4_K_M measures 0.29s median / 0.64s p90 per request on a 3090.
Full results, training details and known weaknesses are on the main model card.
Licence
Apache-2.0, inherited from Qwen3.5-0.8B.
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