Instructions to use pleasen/Gemma-4-E2B-IT-QAT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use pleasen/Gemma-4-E2B-IT-QAT 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 pleasen/Gemma-4-E2B-IT-QAT:UD-Q4_K_XL
# Run inference directly in the terminal:
llama cli -hf pleasen/Gemma-4-E2B-IT-QAT:UD-Q4_K_XL

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf pleasen/Gemma-4-E2B-IT-QAT:UD-Q4_K_XL
# Run inference directly in the terminal:
llama cli -hf pleasen/Gemma-4-E2B-IT-QAT:UD-Q4_K_XL

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 pleasen/Gemma-4-E2B-IT-QAT:UD-Q4_K_XL
# Run inference directly in the terminal:
./llama-cli -hf pleasen/Gemma-4-E2B-IT-QAT:UD-Q4_K_XL

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 pleasen/Gemma-4-E2B-IT-QAT:UD-Q4_K_XL
# Run inference directly in the terminal:
./build/bin/llama-cli -hf pleasen/Gemma-4-E2B-IT-QAT:UD-Q4_K_XL

Use Docker

docker model run hf.co/pleasen/Gemma-4-E2B-IT-QAT:UD-Q4_K_XL

LM Studio
Jan
Ollama
How to use pleasen/Gemma-4-E2B-IT-QAT with Ollama:
```
ollama run hf.co/pleasen/Gemma-4-E2B-IT-QAT:UD-Q4_K_XL
```
Unsloth Desktop

How to use pleasen/Gemma-4-E2B-IT-QAT with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf pleasen/Gemma-4-E2B-IT-QAT:UD-Q4_K_XL

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "pleasen/Gemma-4-E2B-IT-QAT:UD-Q4_K_XL"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use pleasen/Gemma-4-E2B-IT-QAT with Docker Model Runner:
```
docker model run hf.co/pleasen/Gemma-4-E2B-IT-QAT:UD-Q4_K_XL
```

Lemonade

How to use pleasen/Gemma-4-E2B-IT-QAT with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull pleasen/Gemma-4-E2B-IT-QAT:UD-Q4_K_XL

Run and chat with the model

lemonade run user.Gemma-4-E2B-IT-QAT-UD-Q4_K_XL

List all available models

lemonade list

Hermes Agent

How to use pleasen/Gemma-4-E2B-IT-QAT with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf pleasen/Gemma-4-E2B-IT-QAT:UD-Q4_K_XL

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 pleasen/Gemma-4-E2B-IT-QAT:UD-Q4_K_XL

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use pleasen/Gemma-4-E2B-IT-QAT with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf pleasen/Gemma-4-E2B-IT-QAT:UD-Q4_K_XL

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 "pleasen/Gemma-4-E2B-IT-QAT:UD-Q4_K_XL" \
  --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"

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Check out the documentation for more information.

Optimized for resource-constrained and mobile deployment through targeted architectural and numerical optimizations.

Audio encoders removed to reduce the overall model footprint.

Vision layers quantized to Q8_0 or Q4_K_M for substantially reduced storage requirements.

Positional embeddings converted from FP32 to FP16 to further reduce model size while retaining higher precision than the quantized vision layers.

These optimizations reduce the mmproj-BF16 file size to approximately 14%/20% of the original for the Q4_K_M / Q8_0 respectively, significantly lowering the storage requirements for on-device deployment.

license: gemma

Downloads last month: 44

GGUF

Model size

5B params

Architecture

gemma4

Hardware compatibility

4-bit

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