Groq
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Key Technical Specifications

Model Architecture

Built on Qwen's architecture with 32 billion parameters, the model is trained on 5.5 trillion tokens of diverse data and optimized for versatile real-world applications with instant responses, reliable tool use, and native JSON support.

Performance Metrics

The model demonstrates exceptional performance across diverse tasks:
  • 94.5% score on MATH-500 benchmark
  • 70.0% pass rate on AIME 2024
  • Robust performance on complex reasoning tasks

Use Cases

Complex Problem Solving
Excels at tasks requiring deep analysis and structured thinking.
  • Multi-step reasoning and analysis
  • Mathematical problem solving
  • Strategic planning and decision support
  • Research synthesis and summarization
Content Creation
Generates high-quality content across various formats and styles.
  • Long-form article writing
  • Creative writing and storytelling
  • Technical documentation
  • Marketing copy and content adaptation

Best Practices

  • Leverage the context window: Include comprehensive information for more accurate and contextual responses
  • Simplify complex queries: Break down multi-part questions into clear, small steps for more reliable reasoning
  • Enable JSON mode: For generating structured data or when you need outputs in a specific format
  • Include examples: Add sample outputs or specific formats to guide the model into specific output structures

Get Started with Qwen-2.5-32B

Experience state-of-the-art language understanding and generation with Qwen-2.5-32B with Groq speed:

shell
pip install groq
Python
from groq import Groq
client = Groq()
completion = client.chat.completions.create(
    model="qwen-2.5-32b",
    messages=[
        {
            "role": "user",
            "content": "Explain why fast inference is critical for reasoning models"
        }
    ]
)
print(completion.choices[0].message.content)

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