Microsoft Phi-4 ONNX
| Property | Value |
|---|---|
| Developer | Microsoft |
| Model Type | ONNX Optimized Language Model |
| License | MIT |
| Model URL | https://huggingface.co/microsoft/phi-4-onnx |
What is phi-4-onnx?
Phi-4-onnx is an optimized ONNX implementation of Microsoft's Phi-4 language model, specifically designed for efficient inference across various computing platforms. This version leverages ONNX Runtime to deliver enhanced performance while maintaining the base model's capabilities. The model implements int4 quantization techniques for both CPU and GPU deployments, making it particularly suitable for resource-constrained environments.
Implementation Details
The model offers multiple optimized configurations targeting different deployment scenarios. It utilizes RTN-based int4 quantization, providing efficient inference capabilities across CPU, GPU, and mobile platforms. The implementation includes specific optimizations for various execution environments including CPU, CUDA, and DirectML.
- CPU optimization with int4 quantization and block-32 acceleration
- GPU-specific implementation with RTN quantization
- DirectML support for Windows platforms
- Cross-platform compatibility (Windows, Linux, Mac)
Core Capabilities
- Efficient inference across multiple hardware platforms
- Reduced memory footprint through int4 quantization
- High-performance execution through ONNX Runtime
- Support for advanced reasoning tasks
- Optimized for both server and edge deployment
Frequently Asked Questions
Q: What makes this model unique?
This model stands out through its optimized ONNX implementation of the Phi-4 architecture, offering significant performance benefits while maintaining the base model's capabilities. The int4 quantization makes it particularly suitable for deployment in resource-constrained environments.
Q: What are the recommended use cases?
The model is ideal for scenarios requiring efficient inference on various hardware platforms, particularly where resource optimization is crucial. It's suitable for both server-side applications and edge devices, supporting advanced reasoning tasks while maintaining performance efficiency.
