Large models represent a groundbreaking advancement in multiple application fields,
enabling remarkable achievements across various tasks. However, their unprecedented …

Efficient fine-tuning is vital for adapting large language models (LLMs) to downstream tasks.
However, it requires non-trivial efforts to implement these methods on different models. We …

Abstract Low-Rank Adaptation (LoRA), which updates the dense neural network layers with
pluggable low-rank matrices, is one of the best performed parameter efficient fine-tuning …

Low-Rank Adaptation (LoRA) is a widely-used parameter-efficient finetuning method for
large language models. LoRA saves memory by training only low rank perturbations to …

Prediction methods inputting embeddings from protein language models have reached or
even surpassed state-of-the-art performance on many protein prediction tasks. In natural …

Context: The rapid evolution of Large Language Models (LLMs) has sparked significant
interest in leveraging their capabilities for automating code review processes. Prior studies …

The field of vision-language models (VLMs), which take images and texts as inputs and
output texts, is rapidly evolving and has yet to reach consensus on several key aspects of …

Text-to-image generative models have garnered immense attention for their ability to
produce high-fidelity images from text prompts. Among these, Stable Diffusion distinguishes …

Abstract While Large Language Models (LLMs) excel in world knowledge understanding,
adapting them to specific subfields requires precise adjustments. Due to the model's vast …

Visual language models (VLMs) have made significant advances in accuracy in recent
years. However, their efficiency has received much less attention. This paper introduces …