Llama-3.3-70B-Versatile
Llama-3.3-70B-Versatile is Meta's advanced multilingual large language model, optimized for a wide range of natural language processing tasks. With 70 billion parameters, it offers high performance across various benchmarks while maintaining efficiency suitable for diverse applications.
Key Technical Specifications
Model Architecture
Built upon Meta's Llama 3.3 architecture, this model utilizes an optimized transformer design with 70 billion parameters. It incorporates Grouped-Query Attention (GQA) to enhance inference scalability and efficiency. The model has been fine-tuned using supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align outputs with human preferences for helpfulness and safety.
Performance Metrics
The Llama-3.3-70B-Versatile model demonstrates exceptional performance across multiple benchmarks:
- MMLU (Massive Multitask Language Understanding): 86.0% accuracy
- HumanEval (code generation): 88.4% pass@1
- MATH (mathematical problem solving): 77.0% sympy intersection score
- MGSM (Multilingual Grade School Math): 91.1% exact match
Technical Details
| FEATURE | VALUE |
|---|---|
| Context Window (Tokens) | 128K |
| Max Output Tokens | 32,768 |
| Max File Size | N/A |
| Token Generation Speed | ~275 TPS |
| Input Token Price | $0.59 per 1M tokens |
| Output Token Price | $0.79 per 1M tokens |
| Tool Use |  |
| JSON Mode | |
| Image Support |  |
Use Cases
Advanced Language Understanding
Leverage the model's strong multilingual capabilities for complex language understanding tasks across different domains.
Code Generation and Problem Solving
Utilize the model's great performance in code generation, mathematical problem-solving and analytical tasks.
Best Practices
- Clearly specify task instructions and provide sufficient context in your prompts for precise responses.
- Clearly define tool and function definitions for the model to understand their intended use cases, required parameters, expected outputs, and any constraints.
Get Started with Llama-3.3-70B-Versatile
Experience llama-3.3-70b-versatile on Groq:
pip install groq1from groq import Groq
2client = Groq()
3completion = client.chat.completions.create(
4 model="llama-3.3-70b-versatile",
5 messages=[
6 {
7 "role": "user",
8 "content": "Explain why fast inference is critical for reasoning models"
9 }
10 ]
11)
12print(completion.choices[0].message.content)