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LLaMA-Adapter: Efficient Fine-Tuning Method by 476736794wjp is a document available to read on EtoBox.

What is LLaMA-Adapter: Efficient Fine-Tuning Method about?

LLaMA-Adapter is an efficient fine-tuning method for the LLaMA model that introduces only 1.2M learnable parameters and can be trained in under an hour on 8 A100 GPUs. It utilizes learnable adaption prompts and a zero-initialized attention mechanism to enhance instruction-following capabilities while preserving pre-trained knowledge. The approach also extends to multi-modal reasoning, demonstrating superior performance on various benchmarks and can be applied to other pre-trained models like ViT and RoBERTa

Author

476736794wjp

Language

EN