phi-4-FP8-Dynamic
| Property | Value |
|---|---|
| Author | cortecs |
| Model Type | Quantized Language Model |
| Hugging Face | cortecs/phi-4-FP8-Dynamic |
| Maximum Context Length | 16384 tokens |
What is phi-4-FP8-Dynamic?
phi-4-FP8-Dynamic is a quantized version of the phi-4 language model, designed to maintain high performance while improving efficiency. With an impressive accuracy recovery of 99.68%, this model demonstrates that quantization can preserve model capabilities while reducing computational requirements. The model supports multiple languages including English, French, German, Italian, and Spanish, showing robust performance across various evaluation benchmarks.
Implementation Details
The model is optimized for high-throughput applications, capable of processing 4623 tokens per second on an NVIDIA L40S GPU. It utilizes dynamic FP8 quantization to achieve efficient inference while maintaining model quality. The implementation supports vLLM deployment for production environments, making it suitable for scalable applications.
- Maintains near-original performance across multiple benchmarks (ARC, Hellaswag, MMLU)
- Supports context length up to 16384 tokens
- Compatible with vLLM for efficient deployment
- Multi-language support with consistent performance
Core Capabilities
- High-throughput processing optimized for production workloads
- Robust multi-language support with comparable performance to the original model
- Efficient resource utilization through FP8 quantization
- Strong performance on complex reasoning tasks and benchmarks
- Easy deployment through standard API interfaces
Frequently Asked Questions
Q: What makes this model unique?
The model stands out for its exceptional balance between efficiency and performance, achieving 99.68% accuracy recovery while significantly reducing computational requirements through FP8 quantization. It's particularly notable for maintaining consistent performance across multiple languages and supporting high-throughput applications.
Q: What are the recommended use cases?
This model is ideal for production environments requiring efficient, high-throughput language processing. It's particularly well-suited for multi-language applications, complex reasoning tasks, and scenarios where computational efficiency is crucial without compromising on performance.
