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Key Technical Specifications
Model Architecture
A 70 billion parameter transformer that includes enhanced attention mechanisms and optimized training objectives. It offers solid instruction-following capabilities and reduced hallucinations.
Performance Metrics
The model demonstrates solid performance across various benchmarks:
- MMLU (5-shot): 79.5% accuracy, showing strong general knowledge
- GSM-8K (8-shot, CoT): 93.0% accuracy in mathematical reasoning
- HumanEval (0-shot): 81.7% pass rate in code generation
Use Cases
Dialogue Applications
Ideal for building reliable conversational experiences with consistent outputs:
- Customer support and service chatbots
- Interactive assistants and guides
- Educational dialogue systems
- Conversational interfaces for applications
Content Generation
Excels at creating high-quality content with a balance of creativity and accuracy:
- Marketing and promotional content
- Documentation and technical writing
- Creative writing and storytelling
- Content adaptation and summarization
Best Practices
- Structure your prompts: Break complex tasks into clear steps for more reliable outputs
- Enable JSON mode: For generating structured data and maintaining consistent output formats
- Include examples: Add sample outputs or specific formats to guide complex generations
Get Started with llama3-70b
Experience the versatile llama3-70b-8192 with Groq speed now:
shell
pip install groqPython
from groq import Groq
client = Groq()
completion = client.chat.completions.create(
model="llama3-70b-8192",
messages=[
{
"role": "user",
"content": "Explain why fast inference is critical for reasoning models"
}
]
)
print(completion.choices[0].message.content)