Llama-3.2-1B-Instruct-FP8
| Property | Value |
|---|---|
| Parameter Count | 1.5B parameters |
| Model Type | Instruction-tuned Language Model |
| Architecture | Llama-3 |
| License | Llama3.2 |
| Supported Languages | English, German, French, Italian, Portuguese, Hindi, Spanish, Thai |
What is Llama-3.2-1B-Instruct-FP8?
Llama-3.2-1B-Instruct-FP8 is an optimized version of the original Llama-3.2-1B-Instruct model, specifically designed to provide efficient performance while maintaining accuracy. This model represents a significant advancement in model compression, utilizing FP8 quantization to reduce both memory requirements and computational demands.
Implementation Details
The model employs sophisticated quantization techniques, converting weights and activations from 16-bit to 8-bit precision. This optimization results in approximately 50% reduction in GPU memory usage and doubles the matrix-multiply compute throughput. The quantization process uses a symmetric static per-channel scheme for weights and a symmetric per-tensor scheme for activations.
- Weight quantization reduces memory footprint by 50%
- Calibrated using 512 sequences from Neural Magic's calibration dataset
- Maintains performance within 1% of the original model
- Implements FP8 data type for optimal efficiency
Core Capabilities
- Multi-lingual support across 8 languages
- Assistant-style chat functionality
- Achieves 52.11% average score across major benchmarks
- Efficient deployment using vLLM backend
- Enhanced throughput for production environments
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its exceptional balance between efficiency and performance. The FP8 quantization enables significant resource savings while maintaining 99.8% of the original model's accuracy across major benchmarks like MMLU, ARC-Challenge, and GSM-8k.
Q: What are the recommended use cases?
The model is ideal for commercial and research applications requiring multilingual capabilities and assistant-like chat functionality. It's particularly suitable for deployment scenarios where resource efficiency is crucial while maintaining high performance standards.
