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This method reduces the computational cost of fine-tuning and is used in models like AdapterHub. By leveraging pre-training and fine-tuning techniques, LLMs can achieve state-of-the-art performance on a wide variety of NLP tasks, while also benefiting from the knowledge and understanding gained...