2 papers
cs.LG2024
Variance-reduced Zeroth-Order Methods for Fine-Tuning Language Models
Tanmay Gautam, Youngsuk Park, Hao Zhou +2
Fine-tuning language models (LMs) has demonstrated success in a wide array of downstream tasks. However, as LMs are scaled up, the memory requirements for backpropagation become pr…
cs.CL2024
HLAT: High-quality Large Language Model Pre-trained on AWS Trainium
Haozheng Fan, Hao Zhou, Guangtai Huang +6
Getting large language models (LLMs) to perform well on the downstream tasks requires pre-training over trillions of tokens. This typically demands a large number of powerful compu…