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cs.CL2024★ 1 cited
MiLoRA: Efficient Mixture of Low-Rank Adaptation for Large Language Models Fine-tuning
Jingfan Zhang, Yi Zhao, Dan Chen +3
Low-rank adaptation (LoRA) and its mixture-of-experts (MOE) variants are highly effective parameter-efficient fine-tuning (PEFT) methods. However, they introduce significant latenc…
cs.CL2024★ 1 cited
PEDRO: Parameter-Efficient Fine-tuning with Prompt DEpenDent Representation MOdification
Tianfang Xie, Tianjing Li, Wei Zhu +2
Due to their substantial sizes, large language models (LLMs) are typically deployed within a single-backbone multi-tenant framework. In this setup, a single instance of an LLM back…
cs.AR2024
Efficient LLM inference solution on Intel GPU
Hui Wu, Yi Gan, Feng Yuan +8
Transformer based Large Language Models (LLMs) have been widely used in many fields, and the efficiency of LLM inference becomes hot topic in real applications. However, LLMs are u…