Instructions to use sangwon1472/Gemma-MLX-Studio with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sangwon1472/Gemma-MLX-Studio with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="sangwon1472/Gemma-MLX-Studio",
	filename="best_4b_v2_stronger-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use sangwon1472/Gemma-MLX-Studio with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf sangwon1472/Gemma-MLX-Studio:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf sangwon1472/Gemma-MLX-Studio:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf sangwon1472/Gemma-MLX-Studio:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf sangwon1472/Gemma-MLX-Studio: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 sangwon1472/Gemma-MLX-Studio:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf sangwon1472/Gemma-MLX-Studio: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 sangwon1472/Gemma-MLX-Studio:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf sangwon1472/Gemma-MLX-Studio:Q4_K_M

Use Docker

docker model run hf.co/sangwon1472/Gemma-MLX-Studio:Q4_K_M

LM Studio
Jan
Ollama
How to use sangwon1472/Gemma-MLX-Studio with Ollama:
```
ollama run hf.co/sangwon1472/Gemma-MLX-Studio:Q4_K_M
```

Unsloth Studio new

How to use sangwon1472/Gemma-MLX-Studio 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 sangwon1472/Gemma-MLX-Studio 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 sangwon1472/Gemma-MLX-Studio to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for sangwon1472/Gemma-MLX-Studio to start chatting

Pi new

How to use sangwon1472/Gemma-MLX-Studio with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf sangwon1472/Gemma-MLX-Studio: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": "sangwon1472/Gemma-MLX-Studio:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use sangwon1472/Gemma-MLX-Studio with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf sangwon1472/Gemma-MLX-Studio: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 sangwon1472/Gemma-MLX-Studio:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use sangwon1472/Gemma-MLX-Studio with Docker Model Runner:
```
docker model run hf.co/sangwon1472/Gemma-MLX-Studio:Q4_K_M
```

Lemonade

How to use sangwon1472/Gemma-MLX-Studio with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull sangwon1472/Gemma-MLX-Studio:Q4_K_M

Run and chat with the model

lemonade run user.Gemma-MLX-Studio-Q4_K_M

List all available models

lemonade list

Gemma MLX Studio

GGUF exports for the best_4b_v2_stronger run produced with Gemma MLX Studio.

Files

best_4b_v2_stronger-Q4_K_M.gguf
- recommended for practical LM Studio / llama.cpp use
best_4b_v2_stronger-f16.gguf
- high precision archive / conversion source

Notes

Base family: Gemma 4 E4B IT
Fine-tuning workflow: local MLX + LoRA, then fused export
Primary language: Korean

Suggested LM Studio settings

Temperature: 0.2
Top P: 0.9
Top K: 40
Repetition Penalty: 1.1

Prompt style

This model tends to work best with:

short and structured instructions
explicit output length constraints
proper noun preservation

Example:

First Fire Horizon와 Nightglass Relay의 차이를 3문장으로 설명해줘.
First Fire Horizon에는 튜토리얼, 기본 보급, 첫 항로 개방만 연결하고,
Nightglass Relay에는 신호, 기록, 중계만 연결해.
고유명사는 번역하지 마.

Downloads last month: 74

GGUF

Model size

8B params

Architecture

gemma4

Hardware compatibility

4-bit

16-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for sangwon1472/Gemma-MLX-Studio

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google/gemma-4-E4B

Finetuned

google/gemma-4-E4B-it

Quantized

(180)

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