Gemma 2 9B It
Efficient 9B Instruction Tuning
Deploy Gemma 2 9B It
9B Parameters
Outperforms Larger Models
Gemma 2 9B It matches models 2-3x larger using interleaved attentions and distillation.
Instruction Tuned
Chat Template Optimized
Uses role-based formatting for dialogue with 8192 token context window.
Open Source
API Ready Integration
Run Gemma 2 9B It model via OpenAI-compatible endpoints on standard hardware.
Examples
See what Gemma 2 9B It can create
Copy any prompt below and try it yourself in the playground.
Code Explanation
“Explain quicksort algorithm step-by-step with Python pseudocode. Use simple terms for beginners.”
JSON Parser
“Write a Python function to parse nested JSON and extract all string values into a flat list. Handle errors gracefully.”
Math Proof
“Prove Pythagorean theorem using similar triangles. Include diagram description and key equations.”
Email Draft
“Draft professional email requesting project extension due to resource constraints. Keep concise and polite.”
For Developers
A few lines of code.
Chat completions. 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())