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

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Gemma-4 E4B IQ4_XS — Quantized GGUF

Compressed submission for the Resilient AI Challenge (Image-to-Text category), joint initiative of the Government of France, Government of India, UNESCO, and ITU.

Model details

Field	Value
Base model	`google/gemma-4-E4B-IT`
Fine-tuning	Parameter-efficient fine-tuning
Compression	IQ4_XS (~4.25 bpw, importance-matrix quantization)
Format	GGUF (llama.cpp)
Vision projector	`mmproj-BF16.gguf` (BF16, unchanged)

Model size

Model	Size
F16 baseline	15.05 GB
IQ4_XS (this model)	5.06 GB
mmproj (vision projector)	0.99 GB
Compression ratio	~3.7× smaller than F16

Running the model

Use llama-server with the provided llama_server_config.json:

llama-server \
  -m gemma4-E4B-IQ4_XS.gguf \
  --mmproj mmproj-BF16.gguf \
  --host 0.0.0.0 --port 8080 \
  --n-gpu-layers 99 \
  --ctx-size 8192

Generation parameters: temperature=1.0, top_p=0.95, top_k=64

Downloads last month: -

GGUF

Model size

7B params

Architecture

gemma4

Hardware compatibility

4-bit