Instructions to use sunil-pathak/gemma-4-E2B-it-Q6_K with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use sunil-pathak/gemma-4-E2B-it-Q6_K 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 sunil-pathak/gemma-4-E2B-it-Q6_K:Q6_K
# Run inference directly in the terminal:
llama cli -hf sunil-pathak/gemma-4-E2B-it-Q6_K:Q6_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf sunil-pathak/gemma-4-E2B-it-Q6_K:Q6_K
# Run inference directly in the terminal:
llama cli -hf sunil-pathak/gemma-4-E2B-it-Q6_K:Q6_K

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 sunil-pathak/gemma-4-E2B-it-Q6_K:Q6_K
# Run inference directly in the terminal:
./llama-cli -hf sunil-pathak/gemma-4-E2B-it-Q6_K:Q6_K

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 sunil-pathak/gemma-4-E2B-it-Q6_K:Q6_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf sunil-pathak/gemma-4-E2B-it-Q6_K:Q6_K

Use Docker

docker model run hf.co/sunil-pathak/gemma-4-E2B-it-Q6_K:Q6_K

LM Studio
Jan

vLLM

How to use sunil-pathak/gemma-4-E2B-it-Q6_K with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sunil-pathak/gemma-4-E2B-it-Q6_K"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sunil-pathak/gemma-4-E2B-it-Q6_K",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sunil-pathak/gemma-4-E2B-it-Q6_K:Q6_K

Ollama
How to use sunil-pathak/gemma-4-E2B-it-Q6_K with Ollama:
```
ollama run hf.co/sunil-pathak/gemma-4-E2B-it-Q6_K:Q6_K
```
Unsloth Desktop

How to use sunil-pathak/gemma-4-E2B-it-Q6_K with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf sunil-pathak/gemma-4-E2B-it-Q6_K:Q6_K

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": "sunil-pathak/gemma-4-E2B-it-Q6_K:Q6_K"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use sunil-pathak/gemma-4-E2B-it-Q6_K with Docker Model Runner:
```
docker model run hf.co/sunil-pathak/gemma-4-E2B-it-Q6_K:Q6_K
```

Lemonade

How to use sunil-pathak/gemma-4-E2B-it-Q6_K with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull sunil-pathak/gemma-4-E2B-it-Q6_K:Q6_K

Run and chat with the model

lemonade run user.gemma-4-E2B-it-Q6_K-Q6_K

List all available models

lemonade list

Hermes Agent

How to use sunil-pathak/gemma-4-E2B-it-Q6_K with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf sunil-pathak/gemma-4-E2B-it-Q6_K:Q6_K

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 sunil-pathak/gemma-4-E2B-it-Q6_K:Q6_K

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use sunil-pathak/gemma-4-E2B-it-Q6_K with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf sunil-pathak/gemma-4-E2B-it-Q6_K:Q6_K

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 "sunil-pathak/gemma-4-E2B-it-Q6_K:Q6_K" \
  --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"

gemma-4-E2B-it — GGUF (Q6_K)

📊 Performance Metrics

Size: 3.58 GB
Speed: 4.69 tokens/sec
Format: GGUF (llama.cpp optimized)
Quantization: Q6_K

🔷 Model Overview

This repository contains a GGUF quantized version of:

Base Model: gemma-4-E2B-it
Format: GGUF (optimized for llama.cpp inference)
Precision: Q6_K
Purpose: Efficient local inference on CPU/GPU

GGUF format provides:

Fast loading via memory mapping
Single-file model distribution
Cross-platform compatibility
Efficient inference with llama.cpp

📦 Files

File	Description
`gemma-4-E2B-it-Q6_K.gguf`	Quantized GGUF model file

⚙️ Technical Details

Parameter	Value
Architecture	gemma-4-E2B-it
Format	GGUF
Precision	Q6_K
Runtime	llama.cpp
Use Case	Local inference / deployment

⚡ Why GGUF?

GGUF is designed for efficient inference:

Optimized for llama.cpp
Supports CPU and GPU inference
Single-file deployment
Memory-mapped loading for speed
Ideal for edge / local environments

⚠️ License & Usage

This is a converted derivative model.

You must comply with the original model license of gemma-4-E2B-it
This is not an official release
No additional rights are granted
Original ownership remains with the base model creator

🚀 Quick Start (llama.cpp)

./llama-cli -m gemma-4-E2B-it-Q6_K.gguf -p "Explain AI simply"

Downloads last month: 13

GGUF

Model size

5B params

Architecture

gemma4

Hardware compatibility

6-bit