Gemma 3N E4B Instruct
Compact multimodal reasoning
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
import requestsresponse = requests.post("https://modelslab.com/api/v7/llm/chat/completions",json={"key": "YOUR_API_KEY","prompt": "","model_id": ""})print(response.json())
Ready to create?
Start generating with Gemma 3N E4B Instruct on ModelsLab.