Phi-4-mini-instruct-onnx
| Property | Value |
|---|---|
| Developer | Microsoft |
| Model Type | ONNX Optimized LLM |
| License | MIT |
| Context Length | 128K tokens |
| Model URL | https://huggingface.co/microsoft/Phi-4-mini-instruct-onnx |
What is Phi-4-mini-instruct-onnx?
Phi-4-mini-instruct-onnx is Microsoft's optimized ONNX version of their Phi-4-mini model, specifically designed for high-performance inference across various hardware platforms. This model represents a significant advancement in making large language models more efficient and accessible, featuring both CPU and GPU optimizations with impressive speed improvements through int4 quantization.
Implementation Details
The model comes in multiple optimized configurations, including int4 quantization for both CPU and GPU implementations. It leverages ONNX Runtime for inference acceleration and supports deployment across server platforms, Windows, Linux, Mac desktops, and mobile CPUs. The optimization delivers remarkable performance improvements, with up to 12.4x speedup on AMD EPYC processors and 5x speedup on RTX 4090 GPUs compared to PyTorch implementations.
- Int4 quantization support for both CPU and GPU
- Optimized for ONNX Runtime GenAI
- Multiple deployment options (CPU, CUDA, DirectML)
- Significant performance improvements across different hardware
Core Capabilities
- High-performance inference with int4 quantization
- 128K token context length support
- Cross-platform compatibility
- Robust safety measures and instruction adherence
- Built on high-quality, reasoning-dense synthetic data
Frequently Asked Questions
Q: What makes this model unique?
The model's primary uniqueness lies in its optimized ONNX implementation, offering significant performance improvements through int4 quantization while maintaining model quality. It provides up to 12.4x speedup on certain hardware configurations compared to traditional implementations.
Q: What are the recommended use cases?
The model is particularly well-suited for deployment scenarios requiring efficient inference across various hardware platforms, especially where computational resources are limited. It's ideal for applications needing fast response times while maintaining high-quality language understanding and generation capabilities.
