2 citations · 4 across the 2 of their papers we have counts for
2 papers
cs.LG2023★ 2 cited
FineQuant: Unlocking Efficiency with Fine-Grained Weight-Only Quantization for LLMs
Young Jin Kim, Rawn Henry, Raffy Fahim +1
Large Language Models (LLMs) have achieved state-of-the-art performance across various language tasks but pose challenges for practical deployment due to their substantial memory r…
cs.CL2022★ 2 cited
Who Says Elephants Can't Run: Bringing Large Scale MoE Models into Cloud Scale Production
Young Jin Kim, Rawn Henry, Raffy Fahim +1
Mixture of Experts (MoE) models with conditional execution of sparsely activated layers have enabled training models with a much larger number of parameters. As a result, these mod…