Gemma 3 270M It
Compact Powerhouse LLM
Fine-Tune Fast. Deploy Anywhere.
270M Parameters
Hyper-Efficient Architecture
170M embeddings support 256k vocabulary for rare tokens and multilingual tasks.
On-Device Ready
Extreme Energy Efficiency
INT4 quantized model uses 0.75% battery for 25 conversations on Pixel 9.
Instruction Tuned
Task-Specific Fine-Tuning
Strong base for classification, extraction, and intent routing with 32k context.
Examples
See what Gemma 3 270M It can create
Copy any prompt below and try it yourself in the playground.
Code Review
“Review this Python function for bugs and suggest optimizations: def fibonacci(n): if n <= 1: return n else: return fibonacci(n-1) + fibonacci(n-2)”
Text Classification
“Classify this email as spam, urgent, or normal: Subject: Urgent invoice payment needed. Body: Pay now or account suspended.”
Entity Extraction
“Extract all organizations, people, and locations from: Apple Inc. CEO Tim Cook announced new HQ in Cupertino, California.”
JSON Structuring
“Convert this text to JSON: Product: Laptop, Price: 999, Features: 16GB RAM, 512GB SSD, Intel i7.”
For Developers
A few lines of code.
Inference. Three lines.
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 3 270M It on ModelsLab.