权重量化 (Weight Quantization):只压缩模型的参数(权重 W)。 激活量化 (Activation Quantization):只压缩模型的中间计算结果(激活值 X)。 权重和激活都量化 (Weight & Activation Quantization):双管齐下,效果更猛。 量化的方式: PTQ (Post-Training Quantization):模型训练好之后再进行量化。优点是方便快捷,不需要...
AWQ: Activation-aware Weight Quantization for On-device LLM Compression and acceleration 代码地址:github.com/mit-han-lab/ 作者讲解视频:youtube.com/watch? 摘要 abstract 动机 大模型应用功能领域广阔 边缘侧设备上的大模型应用发展迅猛 在边缘设备上运行llm不仅承诺减少延迟和改善用户体验,而且还与对用户隐私...
and Han S. AWQ: Activation-aware weight quantization for llm compression and acceleration. MLSys, 2024.概随着模型的参数量的增加, 推理成本也在显著增加, 本文提出一种量化方法: AWQ 量化, 以缓解这一问题. 其主要贡献在于对于"重要"权重的特殊处理, 以及 per-channel 的 scaling....
Efficient and accurate low-bit weight quantization (INT3/4) for LLMs, supporting instruction-tuned models and multi-modal LMs.The current release supports:AWQ search for accurate quantization. Pre-computed AWQ model zoo for LLMs (LLaMA, Llama2, OPT, CodeLlama, StarCoder, Vicuna, VILA, LL...
AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration [Paper][Slides][Video] Efficient and accurate low-bit weight quantization (INT3/4) for LLMs, supporting instruction-tuned models and multi-modal LMs. The current release supports: AWQ search for accurate quantization. ...
AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration Paper:AWQ on arXiv Code:AWQ on GitHub Organization: MIT Highlight: Optimal Alpha Scaling: Focuses on determining the optimal alpha value for scaling weights prior to quantization....
Implementation of Convolutional Neural Networks in Memristor Crossbar Arrays with Binary Activation and Weight Quantization weight quantizationbinary activation functionmemristor crossbar arrayneuromorphic computingconvolutional neural networkWe propose a hardware-friendly architecture of a... More by Hyungjin Kim...
AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration code:github.com/tylerbmit-ha 摘要 大规模语言模型 (LLMs) 已经改变了众多人工智能应用。端上 LLM 正变得愈发重要:在边缘设备本地运行 LLM 可以降低云计算成本并保护用户隐私。然而,天文学计算量级以及有限的硬件资源带来了重大的部...
"AWQ: Activation-aware Weight Quantization for On-Device LLM Compression and Acceleration"论文阅读 9iM 4 人赞同了该文章 此前的GPTQ训练后量化方法会过度拟合校准数据集,破坏了大语言模型的通用性和泛化性。本工作提出了激活值感知的权重量化方法,它仅使用很少的校准数据进行统计分析,因此不会破坏大语言模型...
Efficient and accurate low-bit weight quantization (INT3/4) for LLMs, supporting instruction-tuned models and multi-modal LMs.The current release supports:AWQ search for accurate quantization. Pre-computed AWQ model zoo for LLMs (Llama-1/2/3, OPT, CodeLlama, StarCoder, Vicuna, VILA, LL...