163 citations · 417 across the 16 of their papers we have counts for
3 papers · 1 filter
LoftQ: LoRA-Fine-Tuning-Aware Quantization for Large Language Models
Yixiao Li, Yifan Yu, Chen Liang +4
Quantization is an indispensable technique for serving Large Language Models (LLMs) and has recently found its way into LoRA fine-tuning. In this work we focus on the scenario wher…
Meet in the Middle: A New Pre-training Paradigm
Anh Nguyen, Nikos Karampatziakis, Weizhu Chen
Most language models (LMs) are trained and applied in an autoregressive left-to-right fashion, assuming that the next token only depends on the preceding ones. However, this assump…
AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning
Qingru Zhang, Minshuo Chen, Alexander Bukharin +5
Fine-tuning large pre-trained language models on downstream tasks has become an important paradigm in NLP. However, common practice fine-tunes all of the parameters in a pre-traine…