Instructions to use wayzer2321/Luna-v2-9B-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 wayzer2321/Luna-v2-9B-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 wayzer2321/Luna-v2-9B-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf wayzer2321/Luna-v2-9B-GGUF:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf wayzer2321/Luna-v2-9B-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf wayzer2321/Luna-v2-9B-GGUF:Q8_0

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 wayzer2321/Luna-v2-9B-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf wayzer2321/Luna-v2-9B-GGUF:Q8_0

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 wayzer2321/Luna-v2-9B-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf wayzer2321/Luna-v2-9B-GGUF:Q8_0

Use Docker

docker model run hf.co/wayzer2321/Luna-v2-9B-GGUF:Q8_0

LM Studio
Jan

vLLM

How to use wayzer2321/Luna-v2-9B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "wayzer2321/Luna-v2-9B-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": "wayzer2321/Luna-v2-9B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/wayzer2321/Luna-v2-9B-GGUF:Q8_0

Ollama
How to use wayzer2321/Luna-v2-9B-GGUF with Ollama:
```
ollama run hf.co/wayzer2321/Luna-v2-9B-GGUF:Q8_0
```
Unsloth Desktop

How to use wayzer2321/Luna-v2-9B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf wayzer2321/Luna-v2-9B-GGUF:Q8_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": "wayzer2321/Luna-v2-9B-GGUF:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use wayzer2321/Luna-v2-9B-GGUF with Docker Model Runner:
```
docker model run hf.co/wayzer2321/Luna-v2-9B-GGUF:Q8_0
```

Lemonade

How to use wayzer2321/Luna-v2-9B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull wayzer2321/Luna-v2-9B-GGUF:Q8_0

Run and chat with the model

lemonade run user.Luna-v2-9B-GGUF-Q8_0

List all available models

lemonade list

Hermes Agent

How to use wayzer2321/Luna-v2-9B-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 wayzer2321/Luna-v2-9B-GGUF:Q8_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 wayzer2321/Luna-v2-9B-GGUF:Q8_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use wayzer2321/Luna-v2-9B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf wayzer2321/Luna-v2-9B-GGUF:Q8_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 "wayzer2321/Luna-v2-9B-GGUF:Q8_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"

CURRENT RELEASE / АКТУАЛЬНАЯ ВЕРСИЯ: Luna v2 · 9B. Release catalog / каталог релизов · Current GGUF / актуальный GGUF The older Luna 9B v1 and separate 27B edition are archived releases. Старая 9B v1 и отдельная 27B — прошлые версии; эта карточка относится к актуальной v2.

Luna v2 — Qwen3.5 9B Q8_0 with MTP

Public GGUF release dated 2026-10-05.

Luna is Wayzer's conversational character: informal slang, sarcasm, surreal humor, emotional shifts, and creator lore.

Release identity

Checkpoint: Luna v2.
Base: unsloth/Qwen3.5-9B.
Quantization: Q8_0.
File: luna-3.5-9b-Q8_0-MTP.gguf.
File size: approximately 9.11 GiB.
MTP tensors: preserved.
Source: merged safetensors.

This uses a different fine-tuned checkpoint from the older public Luna 9B Q4. Comparing those files therefore compares both checkpoint and quantization.

Ollama: local import

Download the GGUF into the current directory and create Modelfile:

FROM ./luna-3.5-9b-Q8_0-MTP.gguf
PARAMETER temperature 0.75
PARAMETER top_p 0.92
PARAMETER repeat_penalty 1.08
PARAMETER num_predict 140
PARAMETER num_ctx 2048

Then run:

ollama create luna-v2 -f Modelfile
ollama run luna-v2

These sampling settings are a starting point, not the training parameters.

MTP and memory

Preserved MTP weights require a compatible inference implementation to be used. An ordinary model import does not establish that MTP acceleration is enabled.

Runtime memory must also cover caches and backend buffers in addition to the GGUF file. Speed depends on hardware, offloading, context, and backend.

Provenance

Converted from the merged Luna v2 checkpoint using llama.cpp conversion.

Downloads last month: 122

GGUF

Model size

9B params

Architecture

qwen35

Hardware compatibility

8-bit

Model tree for wayzer2321/Luna-v2-9B-GGUF

Base model

Qwen/Qwen3.5-9B-Base

Finetuned

Qwen/Qwen3.5-9B

Finetuned

unsloth/Qwen3.5-9B

Finetuned

wayzer2321/Luna-v2-9B-Merged

Quantized

(3)

this model

wayzer2321
/

Luna-v2-9B-GGUF

Luna v2 — Qwen3.5 9B Q8_0 with MTP

Release identity

Ollama: local import

MTP and memory

Provenance

Model tree for wayzer2321/Luna-v2-9B-GGUF

Space using wayzer2321/Luna-v2-9B-GGUF 1