Gemma-2 Instruct (27B)
Scale Reasoning Efficiently
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
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-2 Instruct (27B) on ModelsLab.