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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 groqPython
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)