L4Q: Parameter Efficient Quantization-Aware Fine-Tuning on Large Language Models
L4Q: Parameter Efficient Quantization-Aware Fine-Tuning on Large Language Models
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Ithaca: Cornell University Library, arXiv.org
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English
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Ithaca: Cornell University Library, arXiv.org
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Due to the high memory and computational costs associated with large language models (LLMs), model compression techniques such as quantization, which reduces inference costs, and parameter-efficient fine-tuning (PEFT) methods like Low-Rank Adaptation (LoRA), which reduce training costs, have gained significant popularity. This trend has spurred act...
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L4Q: Parameter Efficient Quantization-Aware Fine-Tuning on Large Language Models
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TN_cdi_proquest_journals_2923551056
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2923551056
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2331-8422
