  Instruction tuning has shown promising potential for developing
general-purpose AI capabilities by using large-scale pre-trained models and
boosts growing research to integrate multimodal information for creative
applications. However, existing works still face two main limitations: the high
training costs and heavy computing resource dependence of full model
fine-tuning, and the lack of semantic information in instructions, which
hinders multimodal alignment. Addressing these challenges, this paper proposes
a novel approach to utilize Parameter-Efficient Tuning for generAl-purpose
vision-Language models, namely PETAL. PETAL revolutionizes the training process
by requiring only 0.5% of the total parameters, achieved through a unique mode
approximation technique, which significantly reduces the training costs and
reliance on heavy computing resources. Furthermore, PETAL enhances the semantic
depth of instructions in two innovative ways: 1) by introducing adaptive
instruction mixture-of-experts(MOEs), and 2) by fortifying the score-based
linkage between parameter-efficient tuning and mutual information. Our
extensive experiments across five multimodal downstream benchmarks reveal that
PETAL not only outperforms current state-of-the-art methods in most scenarios
but also surpasses full fine-tuning models in effectiveness. Additionally, our
approach demonstrates remarkable advantages in few-shot settings, backed by
comprehensive visualization analyses. Our source code is available at:
https://github. com/melonking32/PETAL.
