Residual-Based Fine-Tuning

• Residual-based fine-tuning is a parameter-efficient approach that integrates residual adapters into pre-trained models for improved adaptability.
• It minimizes retraining overhead while maintaining performance by incorporating methods such as LoRA and prompt tuning.
• This technique is applied in adapting large-scale language and vision models, allowing effective customization with minimal computational cost.

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