  Large language models (LLMs) and vision language models (VLMs) demonstrate
excellent performance on a wide range of tasks by scaling up parameter counts
from O(10^9) to O(10^{12}) levels and further beyond. These large scales make
it impossible to adapt and deploy fully specialized models given a task of
interest. Parameter-efficient fine-tuning (PEFT) emerges as a promising
direction to tackle the adaptation and serving challenges for such large
models. We categorize PEFT techniques into two types: intrusive and
non-intrusive. Intrusive PEFT techniques directly change a model's internal
architecture. Though more flexible, they introduce significant complexities for
training and serving. Non-intrusive PEFT techniques leave the internal
architecture unchanged and only adapt model-external parameters, such as
embeddings for input. In this work, we describe AdaLink as a non-intrusive PEFT
technique that achieves competitive performance compared to SoTA intrusive PEFT
(LoRA) and full model fine-tuning (FT) on various tasks. We evaluate using both
text-only and multimodal tasks, with experiments that account for both
parameter-count scaling and training regime (with and without instruction
tuning).
