对低位大语言模型的调查:基础知识,系统和算法
Ruihao Gong1, Yifu Ding1, Zining Wang1
1Beihang University, 37 Xueyuan Road, Haidian District, 100191, Beijing, China.
概括
低位量化显著降低了大型语言模型 (LLM) 的内存和计算成本. 本调查探讨了方法,系统和算法,以使LLM更有效和实用,以便更广泛地部署.
科学领域:
- 人工智能的人工智能
- 计算机科学 计算机科学
- 机器学习 机器学习
背景情况:
- 大型语言模型 (LLM) 展示了先进的自然语言处理能力.
- 高内存和计算需求阻碍了LLMs的实际部署.
研究的目的:
- 为LLMs提供低位量子化方法的全面调查.
- 分析基本原则,系统实现和算法策略.
- 为提高LLM效率和适用性提供见解.
主要方法:
- 对低位LLM的基本概念和数据格式的审查.
- 分析用于硬件部署的框架和系统.
- 技术和工具包的分类,以提供高效的培训和推理.
主要成果:
- 低位量化减少了LLM的内存使用量和计算要求.
- 各种方法,系统和算法促进了高效的低位LLM部署.
- 该调查系统地涵盖了基本,系统和算法视角.
结论:
- 低位量化对于减轻LLM资源限制至关重要.
- 这项调查为未来对高效LLMs的研究提供了有价值的指导方针.
- 低位量化方面的进步将提高LLM在各种平台上的适用性.
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