Phi-4-multimodal-instruct-onnx
| Property | Value |
|---|---|
| Developer | Microsoft |
| Model Type | ONNX Multimodal |
| License | MIT |
| Context Length | 128K tokens |
| Hugging Face | Link |
What is Phi-4-multimodal-instruct-onnx?
Phi-4-multimodal-instruct-onnx is an optimized ONNX conversion of Microsoft's Phi-4 multimodal model, specifically quantized to int4 precision to enhance inference performance. This model represents a significant advancement in multimodal AI, capable of processing text, images, and audio inputs while maintaining high efficiency and performance.
Implementation Details
The model is optimized for multiple execution environments, including CUDA and DirectML, and features specialized quantization techniques for improved performance. It inherits the research and datasets used in Phi-3.5 and 4.0 models, incorporating both supervised fine-tuning and direct preference optimization.
- Int4 quantization for optimized inference
- ONNX Runtime integration for enhanced performance
- Multiple execution backend support (CPU, CUDA, DirectML)
- 128K token context length capability
Core Capabilities
- Multimodal input processing (text, image, audio)
- High-performance inference through ONNX optimization
- Precise instruction adherence
- Built-in safety measures
- Extended context handling
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its optimized ONNX implementation and int4 quantization, making it particularly efficient for production deployments while maintaining the robust multimodal capabilities of the original Phi-4 model.
Q: What are the recommended use cases?
The model is ideal for applications requiring efficient multimodal processing, including content analysis, multimedia understanding, and interactive AI systems that need to process text, images, and audio simultaneously.
