Instructions to use sunil-pathak/gemma-3n-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-3n-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-3n-E2B-it-Q6_K:Q6_K
# Run inference directly in the terminal:
llama cli -hf sunil-pathak/gemma-3n-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-3n-E2B-it-Q6_K:Q6_K
# Run inference directly in the terminal:
llama cli -hf sunil-pathak/gemma-3n-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-3n-E2B-it-Q6_K:Q6_K
# Run inference directly in the terminal:
./llama-cli -hf sunil-pathak/gemma-3n-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-3n-E2B-it-Q6_K:Q6_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf sunil-pathak/gemma-3n-E2B-it-Q6_K:Q6_K

Use Docker

docker model run hf.co/sunil-pathak/gemma-3n-E2B-it-Q6_K:Q6_K

LM Studio
Jan

vLLM

How to use sunil-pathak/gemma-3n-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-3n-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-3n-E2B-it-Q6_K",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sunil-pathak/gemma-3n-E2B-it-Q6_K:Q6_K

Ollama
How to use sunil-pathak/gemma-3n-E2B-it-Q6_K with Ollama:
```
ollama run hf.co/sunil-pathak/gemma-3n-E2B-it-Q6_K:Q6_K
```
Unsloth Desktop
Docker Model Runner
How to use sunil-pathak/gemma-3n-E2B-it-Q6_K with Docker Model Runner:
```
docker model run hf.co/sunil-pathak/gemma-3n-E2B-it-Q6_K:Q6_K
```

Lemonade

How to use sunil-pathak/gemma-3n-E2B-it-Q6_K with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull sunil-pathak/gemma-3n-E2B-it-Q6_K:Q6_K

Run and chat with the model

lemonade run user.gemma-3n-E2B-it-Q6_K-Q6_K

List all available models

lemonade list

Atomic Chat

gemma-3n-E2B-it — GGUF (Q6_K)

📊 Performance Metrics

Hardware: Intel(R) Xeon(R) CPU @ 2.20GHz (4 vCPUs)
Size: 3.47 GB
Speed (Generation): 5.41 tokens/sec
Speed (Prompt): 10.76 tokens/sec
KV Cache Usage: 0.0143 GB
Quantization: Q6_K

🔷 Model Overview

This repository contains a GGUF quantized version of:

Base Model: gemma-3n-E2B-it
Format: GGUF (optimized for llama.cpp inference)
Precision: Q6_K
Efficiency Score: 1.5603 (TPS/GB)

GGUF format provides:

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

📦 Files

File	Description
`gemma-3n-E2B-it-Q6_K.gguf`	Quantized GGUF model file

⚙️ Technical Details

Parameter	Value
Architecture	gemma-3n-E2B-it
Format	GGUF
Precision	Q6_K
Runtime	llama.cpp
Benchmark Hardware	Intel(R) Xeon(R) CPU @ 2.20GHz (4 vCPUs)
Context Latency	29.69s
Memory (KV)	0.0143 GB

⚡ 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-3n-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-3n-E2B-it-Q6_K.gguf -p "Explain AI simply"

Downloads last month: 16

GGUF

Model size

4B params

Architecture

gemma3n

Hardware compatibility

6-bit