| Property | Value |
|---|---|
| Parameter Count | 70 Billion |
| Context Length | 128,000 tokens |
| Training Data | 15T+ tokens |
| Knowledge Cutoff | December 2023 |
| License | Llama 3.3 Community License |
What is Llama-3.3-70B-Instruct-bnb-4bit?
This is a 4-bit quantized version of Meta's Llama 3.3 70B instruction-tuned model, optimized for efficient deployment while maintaining high performance. The model represents a significant advancement in multilingual language modeling, supporting English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai.
Implementation Details
The model utilizes Grouped-Query Attention (GQA) for improved inference scalability and has been optimized using bitsandbytes for 4-bit quantization, significantly reducing memory requirements while maintaining performance. It's designed for both research and commercial applications, with particular strength in assistant-like chat scenarios.
- Optimized 4-bit quantization for efficient deployment
- 128k context window for handling long sequences
- Supports multiple tool use formats
- Advanced multilingual capabilities across 8 languages
Core Capabilities
- Strong performance in code generation (88.4% pass@1 on HumanEval)
- Advanced mathematical reasoning (77.0 score on MATH CoT)
- Robust multilingual understanding (91.1 EM score on MGSM)
- Tool use integration with 77.3 score on BFCL v2
Frequently Asked Questions
Q: What makes this model unique?
The model combines state-of-the-art performance with efficient 4-bit quantization, making it accessible for deployment on limited hardware while maintaining impressive capabilities across multiple languages and tasks. It represents a significant improvement in instruction-following and tool use compared to previous versions.
Q: What are the recommended use cases?
The model excels in assistant-like chat applications, code generation, mathematical reasoning, and multilingual tasks. It's particularly well-suited for commercial applications requiring sophisticated language understanding and generation capabilities while operating under memory constraints.