Instructions to use kanarena/supergemma4-e4b-abliterated-multimodal-gguf-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kanarena/supergemma4-e4b-abliterated-multimodal-gguf-4bit with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="kanarena/supergemma4-e4b-abliterated-multimodal-gguf-4bit",
	filename="Supergemma4-E4B-Abliterated-7.5B-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": [
				{
					"type": "text",
					"text": "Describe this image in one sentence."
				},
				{
					"type": "image_url",
					"image_url": {
						"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
					}
				}
			]
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use kanarena/supergemma4-e4b-abliterated-multimodal-gguf-4bit with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf kanarena/supergemma4-e4b-abliterated-multimodal-gguf-4bit:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf kanarena/supergemma4-e4b-abliterated-multimodal-gguf-4bit:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf kanarena/supergemma4-e4b-abliterated-multimodal-gguf-4bit:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf kanarena/supergemma4-e4b-abliterated-multimodal-gguf-4bit: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 kanarena/supergemma4-e4b-abliterated-multimodal-gguf-4bit:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf kanarena/supergemma4-e4b-abliterated-multimodal-gguf-4bit: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 kanarena/supergemma4-e4b-abliterated-multimodal-gguf-4bit:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf kanarena/supergemma4-e4b-abliterated-multimodal-gguf-4bit:Q4_K_M

Use Docker

docker model run hf.co/kanarena/supergemma4-e4b-abliterated-multimodal-gguf-4bit:Q4_K_M

LM Studio
Jan

vLLM

How to use kanarena/supergemma4-e4b-abliterated-multimodal-gguf-4bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kanarena/supergemma4-e4b-abliterated-multimodal-gguf-4bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kanarena/supergemma4-e4b-abliterated-multimodal-gguf-4bit",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/kanarena/supergemma4-e4b-abliterated-multimodal-gguf-4bit:Q4_K_M

Ollama
How to use kanarena/supergemma4-e4b-abliterated-multimodal-gguf-4bit with Ollama:
```
ollama run hf.co/kanarena/supergemma4-e4b-abliterated-multimodal-gguf-4bit:Q4_K_M
```

Unsloth Studio new

How to use kanarena/supergemma4-e4b-abliterated-multimodal-gguf-4bit 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 kanarena/supergemma4-e4b-abliterated-multimodal-gguf-4bit 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 kanarena/supergemma4-e4b-abliterated-multimodal-gguf-4bit to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for kanarena/supergemma4-e4b-abliterated-multimodal-gguf-4bit to start chatting

Pi new

How to use kanarena/supergemma4-e4b-abliterated-multimodal-gguf-4bit with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf kanarena/supergemma4-e4b-abliterated-multimodal-gguf-4bit: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": "kanarena/supergemma4-e4b-abliterated-multimodal-gguf-4bit:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use kanarena/supergemma4-e4b-abliterated-multimodal-gguf-4bit with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf kanarena/supergemma4-e4b-abliterated-multimodal-gguf-4bit: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 kanarena/supergemma4-e4b-abliterated-multimodal-gguf-4bit:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use kanarena/supergemma4-e4b-abliterated-multimodal-gguf-4bit with Docker Model Runner:
```
docker model run hf.co/kanarena/supergemma4-e4b-abliterated-multimodal-gguf-4bit:Q4_K_M
```

Lemonade

How to use kanarena/supergemma4-e4b-abliterated-multimodal-gguf-4bit with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull kanarena/supergemma4-e4b-abliterated-multimodal-gguf-4bit:Q4_K_M

Run and chat with the model

lemonade run user.supergemma4-e4b-abliterated-multimodal-gguf-4bit-Q4_K_M

List all available models

lemonade list

SuperGemma4-E4B-Abliterated-Multimodal GGUF 4bit

This is the llama.cpp-ready GGUF 4bit distribution of Jiunsong/supergemma4-e4b-abliterated.

Contents include:

Converted and quantized supergemma4-e4b-abliterated model from Jiunsong/supergemma4-e4b-abliterated
mmproj file for image functionality, sourced from lmstudio-community/gemma-4-E4B-it-GGUF (for enabling vision)
Configuration file (Virtual model) for using Reasoning(aka 'Think') within LM Studio

How to Install

It is a installation guide for using the model in LM Studio on Windows. Before proceeding with these steps, you must first install and launch LM Studio."

Instructions:

Open Windows Terminal (PowerShell).
Change the directory to LM Studio's "Models Directory." (If you specified a different path for your Models Directory, change to that custom path instead.)

cd $env:USERPROFILE\.lmstudio\models

Clone the following repository into a folder named kanarena.

git clone https://huggingface.co/kanarena/supergemma4-e4b-abliterated-multimodal-gguf-4bit .\kanarena\supergemma4-e4b-abliterated-multimodal-gguf-4bit\

Move the configuration files (virtual model) to the main models directory.

Move-Item -Path ".\kanarena\supergemma4-e4b-abliterated-multimodal-gguf-4bit\jiunsong" -Destination ".\"

You can now use this model in LM Studio. If the model does not appear, please Refresh model list.

SuperGemma4-E4B-Abliterated-Multimodal GGUF 4bit

이 배포판은 Jiunsong/supergemma4-e4b-abliterated 모델을 llama.cpp 또는 LM 스튜디오 에서 사용할 수 있는 GGUF 파일입니다.

변환 및 양자화 모델: Jiunsong/supergemma4-e4b-abliterated에서 변환되고 양자화된 Jiunsong/supergemma4-e4b-abliterated 모델 파일.
이미지 기능용 모델 (mmproj): 비전(Vision) 기능을 활성화하기 위해 lmstudio-community/gemma-4-E4B-it-GGUF에서 가져온 이미지 관련 mmproj 파일.
추론 설정 파일 (가상 모델): LM Studio 내에서 추론(Reasoning, Think로 알려져있음) 기능 사용을 위한 가상 모델 설정 파일.

설치법

이 설치법은 윈도우용 LM 스튜디오에서 모델 사용하기 위한 것입니다. 아래 설치법을 따라하기 전에 먼저 LM 스튜디오를 설치하고 최초 실행을 하여야 합니다.

설치법:

윈도우 터미널(Powershell)을 엽니다.
LM Studio의 "Models Directory" 위치로 디렉토리를 변경합니다. (만약 Models Directory를 다른 경로로 지정했다면 해당 경로로 디렉토리를 변경합니다.)

cd $env:USERPROFILE\.lmstudio\models

이 레포지토리를 kanarena 이름의 폴더 안에 다운로드 합니다.

git clone https://huggingface.co/kanarena/supergemma4-e4b-abliterated-multimodal-gguf-4bit .\kanarena\supergemma4-e4b-abliterated-multimodal-gguf-4bit\

설정 파일(가상 모델)을 model(상위 디렉토리)로 이동합니다.

Move-Item -Path ".\kanarena\supergemma4-e4b-abliterated-multimodal-gguf-4bit\jiunsong" -Destination ".\"

이제 LM Studio 에서 이 모델을 사용할 수 있습니다. 만약 보이지 않는 경우 모델 리스트 새로고침을 해주세요.

Downloads last month: 989

GGUF

Model size

8B params

Architecture

gemma4

Hardware compatibility

4-bit

Model tree for kanarena/supergemma4-e4b-abliterated-multimodal-gguf-4bit

Base model

google/gemma-4-E4B

Finetuned

google/gemma-4-E4B-it

Finetuned

Jiunsong/supergemma4-e4b-abliterated

Quantized

(14)

this model

kanarena
/

supergemma4-e4b-abliterated-multimodal-gguf-4bit

SuperGemma4-E4B-Abliterated-Multimodal GGUF 4bit

How to Install

SuperGemma4-E4B-Abliterated-Multimodal GGUF 4bit

설치법

Model tree for kanarena/supergemma4-e4b-abliterated-multimodal-gguf-4bit

Spaces using kanarena/supergemma4-e4b-abliterated-multimodal-gguf-4bit 3