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Available now on ModelsLab · AI Model

Gemma 2 9B ItEfficient 9B Instruction Tuning

Try Gemma 2 9B ItAPI Documentation

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
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 2 9B It

Read the docs

Gemma 2 9B It is Google's 9B parameter instruction-tuned LLM. It delivers top performance in its class with 8192 token context. Open weights enable broad deployment.

Send POST to chat completions endpoint with model 'google/gemma-2-9b-it'. Use OpenAI format with messages array. Include Bearer token for auth.

Features group-query attention and interleaved local-global attentions. Trained on 8T tokens via distillation. Runs on consumer GPUs.

Yes, Gemma 2 9B It outperforms Llama 3 in benchmarks for its size. Offers similar capabilities with lighter footprint. Ideal for edge inference.

Supports 8192 input tokens. Uses RoPE embeddings for extension. Output tokens vary by provider.

Available via DeepInfra, Groq, Hugging Face. Check provider docs for endpoints and pricing. Compatible with transformers library.

Ready to create?

Start generating with Gemma 2 9B It on ModelsLab.

Try Gemma 2 9B ItAPI Documentation