A High-Performance AI Inference Library for ONNX and Phi-4

Phinx is an advanced AI inference library that leverages ONNX Runtime GenAI and the Phi-4 Multimodal ONNX model for fast, efficient, and scalable AI applications. Designed for developers seeking seamless integration of generative and multimodal AI, Phinx offers an optimized and flexible runtime environment with robust performance.

🚀 Key Features

• ONNX-Powered Inference – Efficient execution of Phi-4 models using ONNX Runtime GenAI.
• Multimodal AI – Supports text, image, and multi-input inference for diverse AI tasks.
• Optimized Performance – Accelerated inference leveraging ONNX optimizations for speed and efficiency.
• Developer-Friendly API – Simple yet powerful APIs for easy integration into Delphi, Python, and other platforms.
• Self-Contained & Virtualized – The Phinx.model file acts as a virtual folder, bundling Phi-4 ONNX model files and all dependencies into a single, portable format.

Phinx is ideal for AI research, creative applications, and production-ready generative AI solutions. Whether you're building chatbots, AI-powered content generation tools, or multimodal assistants, Phinx delivers the speed and flexibility you need!

📂 Phinx Model File Format (Phinx.model)

The Phinx.model format is a specialized file structure for storing ONNX-based machine learning models, optimized for CUDA-powered inference. It encapsulates all essential components, ensuring seamless model execution.

🔹 Key Benefits

1. Self-Contained & Virtualized

   • Acts as a virtual folder within the application.
   • Bundles Phi-4 ONNX model files and dependencies for portability.

2. Optimized for CUDA Inference

   • Designed for GPU acceleration, delivering high-performance AI execution.
   • Ensures fast loading and efficient CUDA computations.

3. Structured & Extensible

   • Stores model weights, metadata, configuration parameters, and dependencies in a well-organized manner.
   • Future-proof design allows for additional configurations and optimizations.

4. Simplified Deployment

   • All required files are consolidated into a single .model file.
   • Eliminates external dependency management for plug-and-play usability.

🛠 Getting Started

🔧 System Requirements

• GPU Requirements: CUDA-compatible NVIDIA GPU with 8–12GB VRAM.
• Storage Requirements: At least 7GB of free disk space.

📥 Download Model

Get the Phinx Model from Hugging Face: 📂 Download Phinx Model

🏗 Setup Instructions

1. Place the downloaded model in your preferred directory.
   • Example path: C:/LLM/PHINX/repo
2. Ensure you have a Delphi version that supports Win64 and Unicode.
3. Developed with: Delphi 12.2
4. Tested on: Windows 11 (24H2)
5. Refer to UTestbed.pas for usage notes and check the examples.

🚧 Project Status

⚠️ Note: This repository is currently in the setup phase. While documentation is being prepared, the code is fully functional and stable. Stay tuned—this README and additional resources will be updated soon! 🚀

📺 Media

🌊 Deep Dive Podcast
Discover in-depth discussions and insights about Sophora and its innovative features. 🚀✨

🎥 Phinx Feature Videos
Explore videos showcasing the powerful capabilities of the Phinx library, including tutorials, demonstrations, and real-world applications. 🎬🔥

phinx001.mp4

phinx002.mp4

phinx003.mp4

💬 Support and Resources

• 🐞 Report Issues: Issue Tracker
• 💬 Join the Community: Forum | Discord
• 📚 Learn Delphi: Learn Delphi

🤝 Contributing

Contributions to ✨ Phinx are highly encouraged! 🌟
Ways to contribute:

• 🐛 Report Bugs: Help us improve by submitting issues.
• 💡 Suggest Features: Share ideas to enhance Phinx.
• 🔧 Create Pull Requests: Improve the library’s capabilities.

🏆 Contributors

📜 License

Phinx is distributed under the BSD-3-Clause License, allowing redistribution and use in both source and binary forms, with or without modification.
See the 📜 LICENSE for more details.

💖 Support & Sponsorship

If you find Phinx useful, please consider sponsoring this project. Your support helps sustain development, improve features, and keep the project thriving.

Other ways to contribute:

• ⭐ Star the repo – It helps increase visibility.
• 📢 Spread the word – Share Phinx with your network.
• 🐛 Report bugs – Help identify issues.
• 🔧 Submit fixes – Found a bug? Fix it and contribute!
• 💡 Suggest enhancements – Share ideas for improvements.

Every contribution, big or small, helps make Phinx better. Thank you for your support! 🚀

---

⚡ Phinx – Powering AI with Phi-4, ONNX & CUDA, Seamlessly and Efficiently! ⚡

Made with ❤️ in Delphi