- Libraries
- MLX
How to use TheStageAI/gemma-4-E4B-it with MLX:
# Make sure mlx-vlm is installed
# pip install --upgrade mlx-vlm
from mlx_vlm import load, generate
from mlx_vlm.prompt_utils import apply_chat_template
from mlx_vlm.utils import load_config
# Load the model
model, processor = load("TheStageAI/gemma-4-E4B-it")
config = load_config("TheStageAI/gemma-4-E4B-it")
# Prepare input
image = ["http://images.cocodataset.org/val2017/000000039769.jpg"]
prompt = "Describe this image."
# Apply chat template
formatted_prompt = apply_chat_template(
processor, config, prompt, num_images=1
)
# Generate output
output = generate(model, processor, formatted_prompt, image)
print(output) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use TheStageAI/gemma-4-E4B-it 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 TheStageAI/gemma-4-E4B-it
# Run inference directly in the terminal:
llama cli -hf TheStageAI/gemma-4-E4B-it
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf TheStageAI/gemma-4-E4B-it
# Run inference directly in the terminal:
llama cli -hf TheStageAI/gemma-4-E4B-it
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 TheStageAI/gemma-4-E4B-it
# Run inference directly in the terminal:
./llama-cli -hf TheStageAI/gemma-4-E4B-it
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 TheStageAI/gemma-4-E4B-it
# Run inference directly in the terminal:
./build/bin/llama-cli -hf TheStageAI/gemma-4-E4B-it
Use Docker
docker model run hf.co/TheStageAI/gemma-4-E4B-it
- LM Studio
- Jan
- vLLM
How to use TheStageAI/gemma-4-E4B-it with vLLM:
Install from pip and serve model
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TheStageAI/gemma-4-E4B-it"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "TheStageAI/gemma-4-E4B-it",
"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/TheStageAI/gemma-4-E4B-it
- Ollama
How to use TheStageAI/gemma-4-E4B-it with Ollama:
ollama run hf.co/TheStageAI/gemma-4-E4B-it
- Unsloth Desktop
- Pi
How to use TheStageAI/gemma-4-E4B-it with Pi:
Start the MLX server
# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "TheStageAI/gemma-4-E4B-it"
Configure the model in Pi
# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
"providers": {
"mlx-lm": {
"baseUrl": "http://localhost:8080/v1",
"api": "openai-completions",
"apiKey": "none",
"models": [
{
"id": "TheStageAI/gemma-4-E4B-it"
}
]
}
}
}Run Pi
# Start Pi in your project directory:
pi
- Docker Model Runner
How to use TheStageAI/gemma-4-E4B-it with Docker Model Runner:
docker model run hf.co/TheStageAI/gemma-4-E4B-it
- Lemonade
How to use TheStageAI/gemma-4-E4B-it with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/
lemonade pull TheStageAI/gemma-4-E4B-it
Run and chat with the model
lemonade run user.gemma-4-E4B-it-{{QUANT_TAG}}List all available models
lemonade list
- Hermes Agent
How to use TheStageAI/gemma-4-E4B-it with Hermes Agent:
Start the MLX server
# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "TheStageAI/gemma-4-E4B-it"
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 TheStageAI/gemma-4-E4B-it
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use TheStageAI/gemma-4-E4B-it with OpenClaw:
Start the MLX server
# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "TheStageAI/gemma-4-E4B-it"
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 "TheStageAI/gemma-4-E4B-it" \
--custom-provider-id mlx-lm \
--custom-compatibility openai \
--custom-text-input \
--accept-risk \
--skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
