RiteeshGrandhi/sciclaimeval-shared-task huggingface dataset:
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth}
    \begin{tabular}{l|c|cccccccc|c} %
    \toprule
    & \textbf{Memory (GB)} & \textbf{CoLA} & \textbf{STS-B} & \textbf{MRPC} & \textbf{RTE} & \textbf{SST2} & \textbf{MNLI} & \textbf{QNLI} & \textbf{QQP} & \textbf{Avg} \\
    \midrule
    Full Fine-Tuning & 4.64 & 62.24 & 90.92 & 91.30 & 79.42 & 94.57 & 87.18 & 92.33 & 92.28 & 86.28 \\
    \midrule
    GaLore & 4.04 & 60.35 & 90.73 & \textbf{92.25} & \textbf{79.42} & 94.04 & \textbf{87.00} & \textbf{92.24} & 91.06 & 85.89 \\
    LoRA & 2.71 & 61.38 & 90.57 & 91.07 & 78.70  & 92.89 & 86.82 & 92.18 & \textbf{91.29} & 85.61 \\

    VeLoRA & \textbf{2.23} & \textbf{64.56} & \textbf{90.81} & 91.26 & 77.98 & \textbf{94.38} & 86.29 & 92.09 & 89.91 & \textbf{85.91} \\

    \bottomrule
    \end{tabular}
\end{adjustbox}
\caption{Table 2 : Comparison of our method with full fine-tuning, GaLore and LORA on GLUE benchmark using pre-trained RoBERTa-Base. Our method reaches the best overall results while showing significant memory improvements, especially compared to GaLore. We bold the best results from the considered PEFT methods. The GPU memory is measured on-device.}
\end{table}