T-Few Fine-Tuning LLM

The demand for applications powered by large language models (LLMs) is increasing, from chatbots to virtual assistants to content generation. However, to achieve optimal performance and accuracy, it is necessary to fine-tune these models on specific tasks and domains. Traditionally, finetuning involved updating the weights of all layers in the model, which can be time-consuming and require extensive computational resources. T-Few finetuning is an additive Parameter Efficient Finetuning technique that inserts additional layers, comprising approximately 0.01% of the baseline model’s size. It adds 1D vectors L_K, L_V, and L_FF that are multiplied with the K, V, and feed-forward weights during inference.

This Post was first published on AI-ContentLab

https://www.ai-contentlab.com/2023/09/t-few-finetuning-llm.html

Overview of T-Few Finetuning

T-Few finetuning is an additive Parameter Efficient Finetuning technique that inserts additional layers, comprising approximately 0.01% of the baseline model’s size. Specifically, it adds 1D vectors L_K, L_V, and L_FF that are multiplied with the K, V, and feed-forward weights during inference.

T-Few finetuning adds 1D vectors that are multiplied with the K, V, and feed-forward weights during inference. (Source: Liu et. al, 2022)