Llama-3_1-Nemotron-51B-Instruct
| Property | Value |
|---|---|
| Parameter Count | 51.5B |
| License | NVIDIA Open Model License |
| Training Period | August-September 2024 |
| Hardware Requirements | 1x H100-80GB (FP8) or 2x H100/A100-80GB (BF16) |
| Context Length | 8192 tokens |
What is Llama-3_1-Nemotron-51B-Instruct?
Llama-3_1-Nemotron-51B-Instruct is NVIDIA's innovative language model that represents a significant advancement in the balance between model accuracy and computational efficiency. Derived from Llama-3.1-70B-instruct, this model utilizes a groundbreaking Neural Architecture Search (NAS) approach to optimize performance while reducing memory footprint.
Implementation Details
The model employs a sophisticated block-wise distillation process from its parent model, featuring variable architectures across different blocks. Key technical innovations include Variable Grouped Query Attention (VGQA), skip attention mechanisms, and variable FFN ratios. The knowledge distillation process involved 40 billion tokens from FineWeb, Buzz-V1.2, and Dolma datasets.
- Advanced VGQA with flexible KV head counts (1-8)
- Optimized block structure with selective attention skipping
- Dynamic FFN expansion/compression ratios
- BF16 and FP8 quantization support
Core Capabilities
- Strong performance on MT-bench (8.99 score)
- Impressive MMLU results (80.2% with 5-shot)
- Exceptional GSM8K performance (91.43% with 5-shot)
- High accuracy on Winogrande (84.53%)
- Effective multi-turn chat capabilities
Frequently Asked Questions
Q: What makes this model unique?
The model's distinctive feature is its Neural Architecture Search approach, which enables optimal efficiency-accuracy tradeoffs while maintaining high performance. It can run on a single H100-80GB GPU with FP8 quantization, making it more accessible for production deployment.
Q: What are the recommended use cases?
The model excels in English language tasks, coding, and general chat applications. It's particularly well-suited for commercial applications requiring a balance of performance and computational efficiency. The model supports both single-turn and multi-turn conversations.
