How can I implement incremental learning in a Generative AI model to keep the model up-to-date with new data without retraining from scratch

Question

With the help of code can you tell me How can I implement incremental learning in a Generative AI model to keep the model up-to-date with new data without retraining from scratch?

Answer 1

Incremental learning in Generative AI can be implemented using memory-efficient fine-tuning techniques like LoRA (Low-Rank Adaptation) or continual learning, allowing models to update with new data without retraining from scratch.

Here is the code snippet you can refer to:

Key Features of the Code

• Uses LoRA for Efficient Fine-Tuning – Reduces computational cost while updating the model.
• Preserves Existing Knowledge – Retains prior learning while integrating new data.
• Enables Continual Learning – Allows the model to adapt incrementally without full retraining.
• Minimizes Overfitting – Selectively updates layers instead of modifying the entire model.
• Easily Scalable – Can be applied to various AI models across NLP, vision, and multimodal tasks.

Hence, implementing incremental learning using LoRA enables Generative AI models to stay up-to-date with new data efficiently, ensuring continuous learning without expensive retraining from scratch.