Post-training model compression is a powerful way to make Large Language Models accessible in resource-constraint environments. Oftentimes, however, practitioners find themselves adjusting their needs to a set of predefined compression parameters, rather than the other way round. In this talk, I will advocate for model compression that aims to be as simple as image compression: enable users to choose a model size that fits their own needs.