Instructions to use meghanamakkapati/Gemma-4_quantization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use meghanamakkapati/Gemma-4_quantization 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 meghanamakkapati/Gemma-4_quantization:IQ4_XS
# Run inference directly in the terminal:
llama cli -hf meghanamakkapati/Gemma-4_quantization:IQ4_XS

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf meghanamakkapati/Gemma-4_quantization:IQ4_XS
# Run inference directly in the terminal:
llama cli -hf meghanamakkapati/Gemma-4_quantization:IQ4_XS

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 meghanamakkapati/Gemma-4_quantization:IQ4_XS
# Run inference directly in the terminal:
./llama-cli -hf meghanamakkapati/Gemma-4_quantization:IQ4_XS

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 meghanamakkapati/Gemma-4_quantization:IQ4_XS
# Run inference directly in the terminal:
./build/bin/llama-cli -hf meghanamakkapati/Gemma-4_quantization:IQ4_XS

Use Docker

docker model run hf.co/meghanamakkapati/Gemma-4_quantization:IQ4_XS

LM Studio
Jan

vLLM

How to use meghanamakkapati/Gemma-4_quantization with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "meghanamakkapati/Gemma-4_quantization"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "meghanamakkapati/Gemma-4_quantization",
		"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/meghanamakkapati/Gemma-4_quantization:IQ4_XS

Ollama
How to use meghanamakkapati/Gemma-4_quantization with Ollama:
```
ollama run hf.co/meghanamakkapati/Gemma-4_quantization:IQ4_XS
```

Unsloth Studio

How to use meghanamakkapati/Gemma-4_quantization 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 meghanamakkapati/Gemma-4_quantization 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 meghanamakkapati/Gemma-4_quantization to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for meghanamakkapati/Gemma-4_quantization to start chatting

How to use meghanamakkapati/Gemma-4_quantization with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf meghanamakkapati/Gemma-4_quantization:IQ4_XS

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": "meghanamakkapati/Gemma-4_quantization:IQ4_XS"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use meghanamakkapati/Gemma-4_quantization with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf meghanamakkapati/Gemma-4_quantization:IQ4_XS

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 meghanamakkapati/Gemma-4_quantization:IQ4_XS

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use meghanamakkapati/Gemma-4_quantization with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf meghanamakkapati/Gemma-4_quantization:IQ4_XS

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 "meghanamakkapati/Gemma-4_quantization:IQ4_XS" \
  --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 meghanamakkapati/Gemma-4_quantization with Docker Model Runner:
```
docker model run hf.co/meghanamakkapati/Gemma-4_quantization:IQ4_XS
```

Lemonade

How to use meghanamakkapati/Gemma-4_quantization with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull meghanamakkapati/Gemma-4_quantization:IQ4_XS

Run and chat with the model

lemonade run user.Gemma-4_quantization-IQ4_XS

List all available models

lemonade list

You need to agree to share your contact information to access this model

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

Gated model

You can list files but not access them

Preview of files found in this repository

You need to agree to share your contact information to access this model

Gated model You can list files but not access them

Gated model

You can list files but not access them