Instructions to use daniloreddy/gemma-4-E2B-it_GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use daniloreddy/gemma-4-E2B-it_GGUF 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 daniloreddy/gemma-4-E2B-it_GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf daniloreddy/gemma-4-E2B-it_GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf daniloreddy/gemma-4-E2B-it_GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf daniloreddy/gemma-4-E2B-it_GGUF: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 daniloreddy/gemma-4-E2B-it_GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf daniloreddy/gemma-4-E2B-it_GGUF: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 daniloreddy/gemma-4-E2B-it_GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf daniloreddy/gemma-4-E2B-it_GGUF:Q4_K_M

Use Docker

docker model run hf.co/daniloreddy/gemma-4-E2B-it_GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use daniloreddy/gemma-4-E2B-it_GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "daniloreddy/gemma-4-E2B-it_GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "daniloreddy/gemma-4-E2B-it_GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/daniloreddy/gemma-4-E2B-it_GGUF:Q4_K_M

Ollama
How to use daniloreddy/gemma-4-E2B-it_GGUF with Ollama:
```
ollama run hf.co/daniloreddy/gemma-4-E2B-it_GGUF:Q4_K_M
```

Unsloth Studio

How to use daniloreddy/gemma-4-E2B-it_GGUF 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 daniloreddy/gemma-4-E2B-it_GGUF 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 daniloreddy/gemma-4-E2B-it_GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for daniloreddy/gemma-4-E2B-it_GGUF to start chatting

How to use daniloreddy/gemma-4-E2B-it_GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf daniloreddy/gemma-4-E2B-it_GGUF: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": "daniloreddy/gemma-4-E2B-it_GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use daniloreddy/gemma-4-E2B-it_GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf daniloreddy/gemma-4-E2B-it_GGUF: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 daniloreddy/gemma-4-E2B-it_GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use daniloreddy/gemma-4-E2B-it_GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf daniloreddy/gemma-4-E2B-it_GGUF:Q4_K_M

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 "daniloreddy/gemma-4-E2B-it_GGUF:Q4_K_M" \
  --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 daniloreddy/gemma-4-E2B-it_GGUF with Docker Model Runner:
```
docker model run hf.co/daniloreddy/gemma-4-E2B-it_GGUF:Q4_K_M
```

Lemonade

How to use daniloreddy/gemma-4-E2B-it_GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull daniloreddy/gemma-4-E2B-it_GGUF:Q4_K_M

Run and chat with the model

lemonade run user.gemma-4-E2B-it_GGUF-Q4_K_M

List all available models

lemonade list

Gemma-4-E2B-IT - GGUF High-Quality Quantizations

This repository provides GGUF quantized versions of the google/gemma-4-E2B-it model, optimized for local execution using llama.cpp and compatible ecosystems.

📌 Version Notes

All quantizations were generated from the official FP16 weights.

Target: Efficient execution on consumer hardware, mobile/edge devices, and systems with limited memory.
Performance: The output quality (reasoning, coherence, and accuracy) is strictly dependent on the base model's parameter scale (1B).

📊 Quantization Table

File	Method	Bit	Description
fp16.gguf	FP16	16-bit	Original Weights. No quantization applied. Maximum fidelity.
Q8_0.gguf	Q8_0	8-bit	Near-lossless. Practically identical to the original model with lower memory footprint.
Q5_K_M.gguf	Q5_K_M	5-bit	High Precision. Minimizes quantization error for critical tasks.
Q4_K_M.gguf	Q4_K_M	4-bit	Recommended. Best balance between speed and performance.
Q4_K_S.gguf	Q4_K_S	4-bit	Fast/Small. Optimized for maximum throughput and low RAM usage.

🛠️ Technical Details

Quantization Date: 2026-04-05
Tool used: llama-quantize (llama.cpp)
Method: K-Quantization (optimized for AVX2/AVX-512 and modern GPU architectures).

🚀 How to Use

Start a local OpenAI-compatible server with a web UI:

llama.cpp (CLI) using model from HuggingFace

./llama-cli -hf daniloreddy/gemma-4-E2B-it_GGUF:Q4_K_M -p "User: Hello! Assistant:" -n 512 --temp 0.7

llama.cpp (CLI) using downloaded model

./llama-cli -m path/to/gemma-4-E2B-it_Q4_K_M.gguf -p "User: Hello! Assistant:" -n 512 --temp 0.7

llama.cpp (SERVER) using model from HuggingFace

./llama-server -hf daniloreddy/gemma-4-E2B-it_GGUF:Q4_K_M --port 8080 -c 4096

llama.cpp (SERVER) using downloaded model

./llama-server -m /path/to/gemma-4-E2B-it_Q4_K_M.gguf --port 8080 -c 4096

Downloads last month: 220

GGUF

Model size

5B params

Architecture

gemma4

Hardware compatibility

4-bit

5-bit

8-bit

View +1 variant

Model tree for daniloreddy/gemma-4-E2B-it_GGUF

Base model

google/gemma-4-E2B

Finetuned

google/gemma-4-E2B-it

Quantized

(298)

this model