Large Language Models (LLMs) have advanced rapidly but face significant
memory demands. While quantization has shown promise for LLMs, current methods
typically require lengthy training to alleviate the performance degradation
from quantization loss. However, deploying LLMs across diverse scenarios with
different resource constraints, e.g., servers and personal computers, requires
repeated training per application, which amplifies the lengthy training
problem. Given that, it is advantageous to train a once-for-all (OFA) supernet
capable of yielding diverse optimal subnets for downstream applications through
one-shot training. Nonetheless, the scale of current language models impedes
efficiency and amplifies interference from weight sharing between subnets. We
make an initial attempt to extend the once-for-all framework to large language
models. Specifically, we decouple shared weights to eliminate the interference
and incorporate Low-Rank adapters for training efficiency. Furthermore, we
observe the imbalance allocation of training resources from the traditional
uniform sampling. A non-parametric scheduler is introduced to adjust the
sampling rate for each quantization configuration, achieving a more balanced
allocation among subnets with varying demands. We validate the approach on
LLaMA2 families, and downstream evaluation confirms our ability to maintain
high performance while significantly reducing deployment time faced with
multiple scenarios.

利用一次训练的一揽子模型，通过去耦合共享权重、使用低秩适配器、调节采样率等方式，减少大型语言模型的内存需求和训练时间，同时保持高性能。