Gemma 2 Instruct

gemma2-9b-it

Try it in Playground

TOKEN SPEED

~560 TPS

INPUT

Text

OUTPUT

Text

CAPABILITIES

Tool Use, JSON Mode

Google

model card

Gemma 2 9B IT is a lightweight, state-of-the-art open model from Google, built from the same research and technology used to create the Gemini models. This instruction-tuned variant is a text-to-text, decoder-only large language model optimized for conversational use cases. With 9 billion parameters, it's well-suited for a variety of text generation tasks including question answering, summarization, and reasoning, while being deployable in resource-constrained environments.

PRICING

Input

$0.20

5.0M / $1

Output

$0.20

5.0M / $1

LIMITS

CONTEXT WINDOW

8,192

MAX OUTPUT TOKENS

8,192

Key Technical Specifications

Model Architecture

Built upon Google's Gemma 2 architecture, this model is a decoder-only transformer with 9 billion parameters. It incorporates advanced techniques from the Gemini research and has been instruction-tuned for conversational applications. The model uses a specialized chat template with role-based formatting and specific delimiters for optimal performance in dialogue scenarios.

Performance Metrics

The model demonstrates strong performance across various benchmarks, particularly excelling in reasoning and knowledge tasks:

MMLU (Massive Multitask Language Understanding): 71.3% accuracy
HellaSwag (commonsense reasoning): 81.9% accuracy
HumanEval (code generation): 40.2% pass@1
GSM8K (mathematical reasoning): 68.6% accuracy
TriviaQA (knowledge retrieval): 76.6% accuracy

Use Cases

Content Creation and Communication

Ideal for generating high-quality text content across various formats:

Creative text generation (poems, scripts, marketing copy)
Conversational AI and chatbot applications
Text summarization of documents and reports

Research and Education

Perfect for academic and research applications:

Natural Language Processing research foundation
Interactive language learning tools
Knowledge exploration and question answering

Best Practices

Use proper chat template: Apply the model's specific chat template with <start_of_turn> and <end_of_turn> delimiters for optimal conversational performance
Provide clear instructions: Frame tasks with clear prompts and instructions for better results
Consider context length: Optimize your prompts within the 8K context window for best performance
Leverage instruction tuning: Take advantage of the model's conversational training for dialogue-based applications

Get Started with Gemma 2 9B IT

Experience the capabilities of gemma2-9b-it with Groq speed:

shell

pip install groq

Python

from groq import Groq
client = Groq()
completion = client.chat.completions.create(
    model="gemma2-9b-it",
    messages=[
        {
            "role": "user",
            "content": "Explain why fast inference is critical for reasoning models"
        }
    ]
)
print(completion.choices[0].message.content)