nmagic-Meta-Llama-3.1-8B-Instruct-FP8
| Property | Value |
|---|---|
| Base Model | Meta-Llama-3.1-8B-Instruct |
| License | llama3.1 |
| Release Date | July 23, 2024 |
| Optimization | FP8 Quantization |
| Benchmark Score | 73.44 (OpenLLM v1) |
What is nmagic-Meta-Llama-3.1-8B-Instruct-FP8?
This is an optimized version of Meta's Llama 3.1 8B instruction-tuned model, utilizing FP8 quantization for both weights and activations. The model achieves remarkable efficiency by reducing memory requirements by approximately 50% while maintaining 99.52% of the original model's performance. It's specifically designed for commercial and research applications, particularly excelling in assistant-like chat scenarios.
Implementation Details
The model employs symmetric per-tensor quantization, specifically targeting the linear operators within transformer blocks. The optimization process used LLM Compressor with 512 sequences from UltraChat for calibration, resulting in a highly efficient model that can be deployed using the vLLM backend.
- Weight quantization: FP8 format reducing from 16 to 8 bits per parameter
- Activation quantization: FP8 with symmetric per-tensor scaling
- Calibrated using UltraChat dataset
- Compatible with vLLM for efficient deployment
Core Capabilities
- Maintains high performance across multiple benchmarks (MMLU, ARC-Challenge, GSM-8K, etc.)
- Achieves 73.44 average score on OpenLLM benchmark
- Supports multiple languages for commercial and research applications
- Optimized for assistant-like chat interactions
- 50% reduced memory footprint compared to original model
Frequently Asked Questions
Q: What makes this model unique?
The model's key innovation lies in its efficient FP8 quantization scheme that significantly reduces resource requirements while maintaining nearly identical performance to the original model. This makes it particularly valuable for deployment scenarios where memory optimization is crucial.
Q: What are the recommended use cases?
The model is best suited for commercial and research applications requiring assistant-like chat capabilities in English. It's particularly effective for deployments where memory efficiency is important while maintaining high-quality language understanding and generation capabilities.
