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Gemma 2 Instruct

gemma2-9b-it
Try it in Playground
TOKEN SPEED
~560 TPS
Powered bygroq
INPUT
Text
OUTPUT
Text
CAPABILITIES
Tool Use, JSON Object Mode
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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

QUANTIZATION

This uses Groq's TruePoint Numerics, which reduces precision only in areas that don't affect accuracy, preserving quality while delivering significant speedup over traditional approaches. Learn more here.

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)

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