  Training general-purpose vision models on purely sequential visual data,
eschewing linguistic inputs, has heralded a new frontier in visual
understanding. These models are intended to not only comprehend but also
seamlessly transit to out-of-domain tasks. However, current endeavors are
hamstrung by an over-reliance on colossal models, exemplified by models with
upwards of 3B parameters, and the necessity for an extensive corpus of visual
data, often comprising a staggering 400B tokens. In this paper, we delve into
the development of an efficient, autoregression-based vision model,
innovatively architected to operate on a limited dataset. We meticulously
demonstrate how this model achieves proficiency in a spectrum of visual tasks
spanning both high-level and low-level semantic understanding during the
testing phase. Our empirical evaluations underscore the model's agility in
adapting to various tasks, heralding a significant reduction in the parameter
footprint, and a marked decrease in training data requirements, thereby paving
the way for more sustainable and accessible advancements in the field of
generalist vision models. The code is available at
https://github.com/ggjy/DeLVM.
