Instructions to use NexaAI/gemma-3n with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use NexaAI/gemma-3n with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="NexaAI/gemma-3n",
	filename="gemma-3n-E4B-it-Q4_K_M-full-vocab.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use NexaAI/gemma-3n with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf NexaAI/gemma-3n:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf NexaAI/gemma-3n:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf NexaAI/gemma-3n:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf NexaAI/gemma-3n: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 NexaAI/gemma-3n:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf NexaAI/gemma-3n: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 NexaAI/gemma-3n:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf NexaAI/gemma-3n:Q4_K_M

Use Docker

docker model run hf.co/NexaAI/gemma-3n:Q4_K_M

LM Studio
Jan
Ollama
How to use NexaAI/gemma-3n with Ollama:
```
ollama run hf.co/NexaAI/gemma-3n:Q4_K_M
```

Unsloth Studio new

How to use NexaAI/gemma-3n 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 NexaAI/gemma-3n 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 NexaAI/gemma-3n to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for NexaAI/gemma-3n to start chatting

Docker Model Runner
How to use NexaAI/gemma-3n with Docker Model Runner:
```
docker model run hf.co/NexaAI/gemma-3n:Q4_K_M
```

Lemonade

How to use NexaAI/gemma-3n with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull NexaAI/gemma-3n:Q4_K_M

Run and chat with the model

lemonade run user.gemma-3n-Q4_K_M

List all available models

lemonade list

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

Gemma-3n-E4B-IT

Model Description

Gemma 3n E4B-IT, developed by Google DeepMind, is a 4-billion-parameter efficient multimodal model.
Built with MatFormer architecture and dynamic parameter activation, it delivers strong text, image, audio, and video understanding while remaining lightweight enough for on-device deployment.
It supports a 32K context window and multilingual inputs across more than 140 languages.

Features

Multimodal input: text, image (up to 768×768), audio, and video.
Efficient design: parameter skipping and caching enable deployment on edge devices.
Large context window: up to 32K tokens.
Multilingual: trained on 140+ languages.
Compact but strong: achieves benchmark scores competitive with much larger models.

Use Cases

Visual question answering and captioning
Conversational agents with multimodal inputs
On-device assistants for mobile and embedded systems
Multilingual summarization, translation, and transcription

Inputs and Outputs

Input:

Text prompts or dialogue
Single image (tokenized for processing)
Multiple image inputs and audio inputs support coming soon!

Output:

Generated text (answers, captions, translations, summaries)

How to use

1) Install Nexa-SDK

Download and follow the steps under "Deploy Section" Nexa's model page: Download Windows SDK

2) Get an access token

Create a token in the Model Hub, then log in:

nexa config set license '<access_token>'

3) Run the model

Running: `bash nexa infer NexaAI/gemma-3n`

License

Licensed under Google’s Gemma terms of use. See Hugging Face model card for details.

References

Downloads last month: 474

GGUF

Model size

7B params

Architecture

gemma3n

Hardware compatibility

4-bit

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