Instructions to use secretmoon/WoonaV1.2-9b-GGUF-Imatrix with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use secretmoon/WoonaV1.2-9b-GGUF-Imatrix with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="secretmoon/WoonaV1.2-9b-GGUF-Imatrix")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("secretmoon/WoonaV1.2-9b-GGUF-Imatrix", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use secretmoon/WoonaV1.2-9b-GGUF-Imatrix 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 secretmoon/WoonaV1.2-9b-GGUF-Imatrix:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf secretmoon/WoonaV1.2-9b-GGUF-Imatrix:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf secretmoon/WoonaV1.2-9b-GGUF-Imatrix:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf secretmoon/WoonaV1.2-9b-GGUF-Imatrix: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 secretmoon/WoonaV1.2-9b-GGUF-Imatrix:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf secretmoon/WoonaV1.2-9b-GGUF-Imatrix: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 secretmoon/WoonaV1.2-9b-GGUF-Imatrix:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf secretmoon/WoonaV1.2-9b-GGUF-Imatrix:Q4_K_M

Use Docker

docker model run hf.co/secretmoon/WoonaV1.2-9b-GGUF-Imatrix:Q4_K_M

LM Studio
Jan

vLLM

How to use secretmoon/WoonaV1.2-9b-GGUF-Imatrix with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "secretmoon/WoonaV1.2-9b-GGUF-Imatrix"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "secretmoon/WoonaV1.2-9b-GGUF-Imatrix",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/secretmoon/WoonaV1.2-9b-GGUF-Imatrix:Q4_K_M

SGLang

How to use secretmoon/WoonaV1.2-9b-GGUF-Imatrix with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "secretmoon/WoonaV1.2-9b-GGUF-Imatrix" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "secretmoon/WoonaV1.2-9b-GGUF-Imatrix",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "secretmoon/WoonaV1.2-9b-GGUF-Imatrix" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "secretmoon/WoonaV1.2-9b-GGUF-Imatrix",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use secretmoon/WoonaV1.2-9b-GGUF-Imatrix with Ollama:
```
ollama run hf.co/secretmoon/WoonaV1.2-9b-GGUF-Imatrix:Q4_K_M
```

Unsloth Studio

How to use secretmoon/WoonaV1.2-9b-GGUF-Imatrix 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 secretmoon/WoonaV1.2-9b-GGUF-Imatrix 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 secretmoon/WoonaV1.2-9b-GGUF-Imatrix to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for secretmoon/WoonaV1.2-9b-GGUF-Imatrix to start chatting

Docker Model Runner
How to use secretmoon/WoonaV1.2-9b-GGUF-Imatrix with Docker Model Runner:
```
docker model run hf.co/secretmoon/WoonaV1.2-9b-GGUF-Imatrix:Q4_K_M
```

Lemonade

How to use secretmoon/WoonaV1.2-9b-GGUF-Imatrix with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull secretmoon/WoonaV1.2-9b-GGUF-Imatrix:Q4_K_M

Run and chat with the model

lemonade run user.WoonaV1.2-9b-GGUF-Imatrix-Q4_K_M

List all available models

lemonade list

Atomic Chat

About

GGUF imatrix quants of AlexBefest/WoonaV1.2-9b model. All quants, except Q6_k and Q8_0 was maded with imatrix quantization method.

Prompt template: Gemma (RECOMMENDED TEMP=0.3-0.5)

<start_of_turn>user\n {prompt}<end_of_turn>

Provided files

Name	Quant method	Bits	Size	Min RAM required	Use case
WoonaV1.2-9b-imat-Q2_K.gguf	Q2_K [imatrix]	2	3.5 GB	5.1 GB	small, very high quality loss - not recommended, but usable (probably faster than Q3_XXS, but worse)
WoonaV1.2-9b-imat-IQ3_XXS.gguf	IQ3_XXS [imatrix]	3	3.5 GB	5.1 GB	small, high quality loss
WoonaV1.2-9b-imat-IQ3_M.gguf	IQ3_M [imatrix]	3	4.2 GB	5.7 GB	small, high quality loss
WoonaV1.2-9b-imat-IQ4_XS.gguf	IQ4_XS [imatrix]	4	4.8 GB	6.3 GB	medium, slightly worse than Q4_K_M
WoonaV1.2-9b-imat-Q4_K_S.gguf	Q4_K_S [imatrix]	4	5.1 GB	6.7 GB	medium, balanced quality loss
WoonaV1.2-9b-imat-Q4_K_M.gguf	Q4_K_M [imatrix]	4	5.4 GB	6.9 GB	medium, balanced quality - recommended
WoonaV1.2-9b-imat-Q5_K_S.gguf	Q5_K_S [imatrix]	5	6 GB	7.6 GB	large, low quality loss - recommended
WoonaV1.2-9b-imat-Q5_K_M.gguf	Q5_K_M [imatrix]	5	6.2 GB	7.8 GB	large, very low quality loss - recommended
WoonaV1.2-9b-Q6_K.gguf	Q6_K [static]	6	7.1 GB	8.7 GB	very large, near perfect quality - recommended
WoonaV1.2-9b-Q8_0.gguf	Q8_0 [static]	8	9.2 GB	10.8 GB	very large, extremely low quality loss

How to Use

llama.cpp The opensource framework for running GGUF LLM models on which all other interfaces are made.
koboldcpp Easy method for windows inference. Lightweight open source fork llama.cpp with a simple graphical interface and many additional features.
LM studio Proprietary free fork llama.cpp with a graphical interface.

Downloads last month: 141

GGUF

Model size

9B params

Architecture

gemma2

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for secretmoon/WoonaV1.2-9b-GGUF-Imatrix

Base model

google/gemma-2-9b

Finetuned

google/gemma-2-9b-it

Finetuned

SlerpE/WoonaV1.2-9b

Quantized

(6)

this model