JavaONNX-Based Generative AI LLMs in Java with Project Babylon
The Open Neural Network Exchange (ONNX) serves as an universal format for representing machine learning models, facilitating their deployment across diverse platforms.
Traditionally, Large Language Models (LLMs) are developed in Python using frameworks like PyTorch, TensorFlow, or scikit-learn, and then exported to ONNX for execution.
In this presentation, we will demonstrate how Java, a language not traditionally associated with AI modeling, can be utilized to produce ONNX models. We will explore the concept of ONNX-based Generative AI LLMs in Java, leveraging Project Babylon's code reflection capabilities. The presentation will showcase a practical Java example of an LLM, detailing its transformation into the ONNX format and subsequent execution.
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Traditionally, Large Language Models (LLMs) are developed in Python using frameworks like PyTorch, TensorFlow, or scikit-learn, and then exported to ONNX for execution.
In this presentation, we will demonstrate how Java, a language not traditionally associated with AI modeling, can be utilized to produce ONNX models. We will explore the concept of ONNX-based Generative AI LLMs in Java, leveraging Project Babylon's code reflection capabilities. The presentation will showcase a practical Java example of an LLM, detailing its transformation into the ONNX format and subsequent execution.
Adam Sotona
When he's not immersed in code, Adam enjoys exploring new technologies, teaching Java, sailing, photography, and playing the ukulele.
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