Llama-3.3-70B-Instruct-FP8-Dynamic
| Property | Value |
|---|---|
| Parameter Count | 70 Billion |
| Context Length | 128k tokens |
| Training Data | 15T+ tokens |
| Knowledge Cutoff | December 2023 |
| Supported Languages | English, German, French, Italian, Portuguese, Hindi, Spanish, Thai |
| License | Llama 3.3 Community License |
What is Llama-3.3-70B-Instruct-FP8-Dynamic?
This is Meta's latest iteration of the Llama series, specifically optimized with FP8 dynamic quantization by Infermatic.ai. It's a multilingual large language model designed for instruction-following tasks, featuring enhanced performance across various benchmarks and improved safety measures.
Implementation Details
The model utilizes an optimized transformer architecture with Grouped-Query Attention (GQA) for improved inference scalability. It features FP8 dynamic quantization for efficient deployment while maintaining performance, and supports integration with popular frameworks like Transformers and bitsandbytes.
- Optimized for both standard chat and tool-use capabilities
- Supports 8-bit and 4-bit quantization options
- Includes comprehensive safety measures and guardrails
- Features 128k context window for handling longer sequences
Core Capabilities
- Strong performance in code generation (88.4% pass@1 on HumanEval)
- Advanced mathematical reasoning (77.0% on MATH benchmark)
- Robust multilingual understanding and generation
- Integrated tool-use functionality
- Enhanced safety features and content filtering
Frequently Asked Questions
Q: What makes this model unique?
This model combines Meta's latest Llama 3.3 architecture with FP8 dynamic quantization, offering an optimal balance between performance and efficiency. It features comprehensive multilingual support and enhanced safety measures while maintaining strong performance across various benchmarks.
Q: What are the recommended use cases?
The model is well-suited for commercial and research applications including assistant-like chat, code generation, mathematical reasoning, and tool-based interactions. It's particularly effective for multilingual applications and can be integrated into systems requiring sophisticated language understanding and generation capabilities.
