Happy Horse 1.0 is now on ModelsLab

Try Now
Skip to main content
Available now on ModelsLab · Language Model

Gemma 3N E4B InstructCompact multimodal reasoning

Try Gemma 3N E4B InstructAPI Documentation

Multimodal efficiency by design

Multimodal input

Text, image, audio, video

Accepts text, images, audio, and video as input and returns structured text outputs.

On‑device optimized

Runs on low‑resource devices

Uses selective parameter activation to operate with effective 4B parameters and ~3GB memory.

Open weights

Open‑weights LLM

Gemma 3N E4B Instruct model ships with open weights for pre‑trained and instruction‑tuned variants.

Examples

See what Gemma 3N E4B Instruct can create

Copy any prompt below and try it yourself in the playground.

Image description

“Describe the main objects, colors, and composition in this image in one paragraph. Focus on layout and visual style.”

Audio summary

“Transcribe and summarize the spoken content in this audio clip, listing key topics and any named entities mentioned.”

Code explanation

“Explain this Python function line by line, then suggest one optimization that improves performance without changing behavior.”

Multilingual Q&A

“Answer this question in Spanish, then translate your answer into English and highlight the key differences in phrasing.”

For Developers

A few lines of code.
Multimodal LLM in one call

ModelsLab handles the infrastructure: fast inference, auto-scaling, and a developer-friendly API. No GPU management needed.

  • Serverless: scales to zero, scales to millions
  • Pay per token, no minimums
  • Python and JavaScript SDKs, plus REST API
API Documentation 
import requests


response = requests.post(
    "https://modelslab.com/api/v7/llm/chat/completions",
    json={
  "key": "YOUR_API_KEY",
  "prompt": "",
  "model_id": ""
}
)
print(response.json())

FAQ

Common questions about Gemma 3N E4B Instruct

Read the docs

Gemma 3N E4B Instruct is a 4‑billion‑parameter multimodal LLM that accepts text, image, audio, and video and returns text outputs. It is optimized for low‑resource devices and ships with open weights.

The Gemma 3N E4B Instruct API accepts a payload with text, image, audio, or video and returns generated text. The endpoint runs the instruction‑tuned variant on GPU‑accelerated infrastructure.

The Gemma 3N E4B Instruct model is open‑weights, with pre‑trained and instruction‑tuned variants available for download. You can run it locally or via third‑party APIs.

Common use cases include on‑device assistants, multimodal search, content moderation, and low‑latency chat. The model supports 140 languages and handles text, image, audio, and video inputs.

Gemma 3N E4B Instruct offers multimodal input and on‑device efficiency at under 10B parameters. It is a compact alternative to larger server‑grade LLMs while maintaining strong reasoning and multilingual performance.

Yes, Gemma 3N E4B Instruct can serve as a Gemma 3N E4B Instruct alternative via hosted API endpoints. It supports the same instruction‑tuned behavior with lower latency and memory footprint than larger models.

Ready to create?

Start generating with Gemma 3N E4B Instruct on ModelsLab.

Try Gemma 3N E4B InstructAPI Documentation