  Pre-training followed by fine-tuning is widely adopted among practitioners.
The performance can be improved by "model soups"~\cite{wortsman2022model} via
exploring various hyperparameter configurations.The Learned-Soup, a variant of
model soups, significantly improves the performance but suffers from
substantial memory and time costs due to the requirements of (i) having to load
all fine-tuned models simultaneously, and (ii) a large computational graph
encompassing all fine-tuned models. In this paper, we propose Memory Efficient
Hyperplane Learned Soup (MEHL-Soup) to tackle this issue by formulating the
learned soup as a hyperplane optimization problem and introducing block
coordinate gradient descent to learn the mixing coefficients. At each
iteration, MEHL-Soup only needs to load a few fine-tuned models and build a
computational graph with one combined model. We further extend MEHL-Soup to
MEHL-Soup+ in a layer-wise manner. Experimental results on various ViT models
and data sets show that MEHL-Soup(+) outperforms Learned-Soup(+) in terms of
test accuracy, and also reduces memory usage by more than $13\times$. Moreover,
MEHL-Soup(+) can be run on a single GPU and achieves $9\times$ speed up in soup
construction compared with the Learned-Soup. The code is released at
https://github.com/nblt/MEHL-Soup.
