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

The typical process for developing LLMs involves pre-training a general foundation model
on massive data, followed by fine-tuning on task-specific data to create specialized experts …

Existing methods for adapting large language models (LLMs) to new tasks are not suited to
multi-task adaptation because they modify all the model weights--causing destructive …

Large language models (LLMs) have revolutionized lots of fields of research. Although it is
well-known that fine-tuning is essential for enhancing the capabilities of LLMs, existing …

Speculative decoding (SD) has attracted a significant amount of research attention due to
the substantial speedup it can achieve for LLM inference. However, despite the high …

Teaching new information to pre-trained large language models (PLM) is a crucial but
challenging task. Model adaptation techniques, such as fine-tuning and parameter-efficient …

Fine-tuning is a crucial process for adapting large language models (LLMs) to diverse
applications. In certain scenarios, such as multi-tenant serving, deploying multiple LLMs …

Proteins are the primary functional unit of the cell, and their interactions drive cellular
function. Interactions between proteins are responsible for a wide variety of functions raning …