Large Language Models (LLMs) went from non-existent to ubiquitous in the machine
learning discourse within a few years. Due to the fast pace of the field, it is difficult to identify …

We present QLoRA, an efficient finetuning approach that reduces memory usage enough to
finetune a 65B parameter model on a single 48GB GPU while preserving full 16-bit …

Large language models (LLMs) have shown excellent performance on various tasks, but the
astronomical model size raises the hardware barrier for serving (memory size) and slows …

Large language models (LLMs) show excellent performance but are compute-and memory-
intensive. Quantization can reduce memory and accelerate inference. However, existing …

Large language models have been widely adopted but require significant GPU memory for
inference. We develop a procedure for Int8 matrix multiplication for feed-forward and …

We show for the first time that large-scale generative pretrained transformer (GPT) family
models can be pruned to at least 50% sparsity in one-shot, without any retraining, at minimal …

The high computational and memory requirements of large language model (LLM) inference
make it feasible only with multiple high-end accelerators. Motivated by the emerging …

Generative Pre-trained Transformer models, known as GPT or OPT, set themselves apart
through breakthrough performance across complex language modelling tasks, but also by …

Large language models (LLMs) with hundreds of billions of parameters have sparked a new
wave of exciting AI applications. However, they are computationally expensive at inference …

This survey paper provides a comprehensive overview of the recent advancements and
challenges in applying large language models to the field of audio signal processing. Audio …