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Gemma-2 Instruct (27B)Scale Reasoning Efficiently

Try Gemma-2 Instruct (27B)API Documentation

Deploy Gemma-2 Instruct 27B

Grouped-Query Attention

Efficient Inference Engine

Gemma-2 Instruct (27B) runs full precision on single GPU with GQA and local-global attention.

Benchmarks

Outperforms Larger Models

Gemma-2 Instruct (27B) beats Llama 3 70B on MMLU and GSM8K via knowledge distillation.

Instruction-Tuned Precision

Handles Complex Tasks

Gemma-2 Instruct (27B) LLM excels in question answering, summarization, and code generation.

Examples

See what Gemma-2 Instruct (27B) can create

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

Code Review

“Review this Python function for efficiency and suggest optimizations: def fibonacci(n): if n <= 1: return n else: return fibonacci(n-1) + fibonacci(n-2)”

Math Proof

“Prove that the sum of the first n natural numbers is n(n+1)/2 using mathematical induction. Provide step-by-step reasoning.”

Text Summary

“Summarize the key innovations in Transformer architectures from the Gemma 2 technical report, focusing on attention mechanisms.”

Reasoning Chain

“A bat and ball cost $1.10 total. The bat costs $1 more than the ball. How much does the ball cost? Explain step by step.”

For Developers

A few lines of code.
Instruct 27B. 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 Instruct (27B)

Read the docs

Gemma-2 Instruct (27B) is Google's open instruction-tuned LLM with 27B parameters. It uses GQA and knowledge distillation for top benchmarks. Deploy via Gemma-2 Instruct (27B) API.

Gemma-2 Instruct (27B) API provides efficient text generation endpoints. Supports 8K context with RoPE embeddings. Optimized for single GPU inference.

Gemma-2 Instruct (27B) outperforms Llama 3 70B on LMSys arena and MMLU. It sets SOTA for open models under 30B parameters.

Gemma-2 Instruct (27B) alternative includes Llama 3 or Qwen models. Gemma-2 Instruct (27B) LLM leads in efficiency for its size.

Run gemma 2 instruct 27b via APIs like this platform or Ollama. Gemma-2 Instruct (27B) supports quantized inference.

Gemma 2 instruct 27b api handles 256K vocab and 4K-8K context. Best for clear prompts in reasoning tasks.

Ready to create?

Start generating with Gemma-2 Instruct (27B) on ModelsLab.

Try Gemma-2 Instruct (27B)API Documentation