  Transfer learning has become a popular task adaptation method in the era of
foundation models. However, many foundation models require large storage and
computing resources, which makes off-the-shelf deployment impractical.
Post-training compression techniques such as pruning and quantization can help
lower deployment costs. Unfortunately, the resulting performance degradation
limits the usability and benefits of such techniques. To close this performance
gap, we propose CrAFT, a simple fine-tuning framework that enables effective
post-training network compression. In CrAFT, users simply employ the default
fine-tuning schedule along with sharpness minimization objective,
simultaneously facilitating task adaptation and compression-friendliness.
Contrary to the conventional sharpness minimization techniques, which are
applied during pretraining, the CrAFT approach adds negligible training
overhead as fine-tuning is done in under a couple of minutes or hours with a
single GPU. The effectiveness of CrAFT, which is a general-purpose tool that
can significantly boost one-shot pruning and post-training quantization, is
demonstrated on both convolution-based and attention-based vision foundation
models on a variety of target tasks. The code will be made publicly available.
