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Generative AI with LLMs: Reparameterization: A Parameter-Efficient Fine-Tuning Method for LLMs

Learn what reparameterization is and how it can help you fine-tune large language models (LLMs) with minimal resources and maximal performance.


Which of the following are Parameter Efficient Fine-Tuning (PEFT) methods? Select all that apply.

A. Additive
B. Subtractive
C. Reparameterization
D. Selective


C. Reparameterization


The correct answer is C. Reparameterization. Reparameterization is a general term for PEFT methods that modify the existing parameters of a pre-trained model in a way that preserves its original functionality while allowing for adaptation to a new task. Some examples of reparameterization methods are adapter, LoRA, and prefix tuning. These methods insert additional modules or layers into the pre-trained model and fine-tune only those parameters, leaving the rest of the model fixed. This reduces the computational and storage costs of fine-tuning, as well as the risk of overfitting or catastrophic forgetting.

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Alex Lim is a certified IT Technical Support Architect with over 15 years of experience in designing, implementing, and troubleshooting complex IT systems and networks. He has worked for leading IT companies, such as Microsoft, IBM, and Cisco, providing technical support and solutions to clients across various industries and sectors. Alex has a bachelor’s degree in computer science from the National University of Singapore and a master’s degree in information security from the Massachusetts Institute of Technology. He is also the author of several best-selling books on IT technical support, such as The IT Technical Support Handbook and Troubleshooting IT Systems and Networks. Alex lives in Bandar, Johore, Malaysia with his wife and two chilrdren. You can reach him at [email protected] or follow him on Website | Twitter | Facebook

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