Llama-3.1-Nemotron-70B-Instruct-HF-FP8-dynamic
| Property | Value |
|---|---|
| Parameter Count | 70.6B |
| Model Type | Large Language Model |
| Languages | English, German, French, Italian, Portuguese, Hindi, Spanish, Thai |
| License | llama3.1 |
| Tensor Type | BF16/F8_E4M3 |
What is Llama-3.1-Nemotron-70B-Instruct-HF-FP8-dynamic?
This model is a highly optimized version of the Llama-3.1-Nemotron-70B-Instruct model, featuring FP8 quantization for both weights and activations. It achieves remarkable efficiency by reducing the model's memory footprint by approximately 50% while maintaining over 99% accuracy compared to the original model.
Implementation Details
The model implements sophisticated quantization techniques, using symmetric per-channel quantization for linear operators within transformer blocks. It features dynamic activation quantization on a per-token basis and is specifically optimized for deployment with vLLM.
- 8-bit quantization for weights and activations
- Symmetric per-channel quantization implementation
- Dynamic token-based activation quantization
- 50% reduction in disk size and GPU memory requirements
Core Capabilities
- Multi-language support across 8 languages
- 99.41% recovery rate on Arena-Hard evaluation
- 100% recovery on OpenLLM v1 benchmarks
- Efficient deployment through vLLM backend
- Optimized for assistant-like chat applications
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its exceptional balance between efficiency and performance, achieving nearly identical results to the original 70B model while requiring only half the computational resources through advanced FP8 quantization.
Q: What are the recommended use cases?
The model is ideal for commercial and research applications requiring efficient deployment of large language models, particularly in scenarios involving multiple languages and assistant-like chat interactions. It's particularly suitable for environments where resource optimization is crucial without compromising on performance.
