1 citations · 1 across the 2 of their papers we have counts for
3 papers
cs.LG2024★ 1 cited
FlattenQuant: Breaking Through the Inference Compute-bound for Large Language Models with Per-tensor Quantization
Yi Zhang, Fei Yang, Shuang Peng +2
Large language models (LLMs) have demonstrated state-of-the-art performance across various tasks. However, the latency of inference and the large GPU memory consumption of LLMs res…
cs.CL2023
Holmes: Towards Distributed Training Across Clusters with Heterogeneous NIC Environment
Fei Yang, Shuang Peng, Ning Sun +5
Large language models (LLMs) such as GPT-3, OPT, and LLaMA have demonstrated remarkable accuracy in a wide range of tasks. However, training these models can incur significant expe…
cs.LG2023
Exploring Post-Training Quantization of Protein Language Models
Shuang Peng, Fei Yang, Ning Sun +3
Recent advancements in unsupervised protein language models (ProteinLMs), like ESM-1b and ESM-2, have shown promise in different protein prediction tasks. However, these models fac…