3 papers
cs.CL2024
Enabling High-Sparsity Foundational Llama Models with Efficient Pretraining and Deployment
Abhinav Agarwalla, Abhay Gupta, Alexandre Marques +9
Large language models (LLMs) have revolutionized Natural Language Processing (NLP), but their size creates computational bottlenecks. We introduce a novel approach to create accura…
cs.CL2024
MediSwift: Efficient Sparse Pre-trained Biomedical Language Models
Vithursan Thangarasa, Mahmoud Salem, Shreyas Saxena +3
Large language models (LLMs) are typically trained on general source data for various domains, but a recent surge in domain-specific LLMs has shown their potential to outperform ge…
cs.LG2023
SPDF: Sparse Pre-training and Dense Fine-tuning for Large Language Models
Vithursan Thangarasa, Abhay Gupta, William Marshall +5
The pre-training and fine-tuning paradigm has contributed to a number of breakthroughs in Natural Language Processing (NLP). Instead of directly training on a downstream task, lang…