LLaMA-3.2-3B的资源高效微调用于医学链式思维推理
Resource-Efficient Fine-Tuning of LLaMA-3.2-3B for Medical Chain-of-Thought Reasoning
AI总结：
本文提出一种基于LoRA/QLoRA的资源高效微调方法，在受限GPU环境下增强LLaMA-3.2-3B的医学链式思维推理，内存减少60%且保持推理能力。
摘要：
大型语言模型（LLM）如GPT-4和LLaMA展现了卓越的推理能力，但微调需要大量计算资源。本文提出了一种针对LLaMA-3.2-3B的资源高效微调方法，在受限的GPU和内存设置下增强医学链式思维推理。使用参数高效微调技术（如LoRA和QLoRA），我们在公开可用的医学推理数据集上调整基础模型。该模型在推理连贯性和事实准确性上有所提升，同时与标准全量微调相比，内存使用减少高达60%。实验评估表明，轻量级适配能在医学问答任务中保持强大的推理能力。这项工作突出了在低资源研究环境中部署LLM的实用策略，并为平衡医学AI系统的效率和领域专业化提供了见解。
英文摘要：
Large Language Models (LLMs) such as GPT-4 and LLaMA have demonstrated remarkable reasoning abilities but require significant computational resources for fine-tuning. This paper presents a resource-efficient fine-tuning approach for LLaMA-3.2-3B to enhance medical chain-of-thought reasoning while operating under constrained GPU and memory settings. Using parameter-efficient tuning techniques such as LoRA and QLoRA, we adapt the base model on publicly available medical reasoning datasets. The model achieves improved reasoning coherence and factual accuracy while reducing memory usage by up to 60% compared to standard full fine-tuning. Experimental evaluation demonstrates that lightweight adaptations can retain strong reasoning capability in medical question-answering tasks. This work highlights practical strategies for deploying LLMs in low-resource research environments and provides insights into balancing efficiency and domain specialization for medical AI systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
