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Shouda Liu

2 accepted papers

2024

Accurate LoRA-Finetuning Quantization of LLMs via Information Retention

ICML 2024oral

The LoRA-finetuning quantization of LLMs has been extensively studied to obtain accurate yet compact LLMs for deployment on resource-constrained hardware. However, existing methods cause the quantized LLM to severely degrade and even fail to benefit from the finetuning of LoRA. This paper proposes a…

2024

LSH-MoE: Communication-efficient MoE Training via Locality-Sensitive Hashing

NeurIPS 2024poster

Larger transformer models perform better on various downstream tasks but require more cost to scale up the model size. To efficiently enlarge models, the Mixture-of-Expert (MoE) architecture is widely adopted, which consists of a gate network and a series of experts and keep the training cost consta…

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