然而,在重建过程中可能会过度拟合校准集,从而扭曲学习到的域外特征(图 8),这是有问题的,因为 LLM 是通用模型。 在本文中,我们提出了一种针对 LLM 的硬件友好的低比特权重独有量化的激活感知权重量化方法(Activation-aware Weight Quantization, AWQ)。我们的方法基于这样一个观察:对于 LLM 的性能来说,并不是所...
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...
# 论文速览 AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration 链接问题: 庞大的模型大小增加了在硬件上提供服务(内存大小)的难度,并减慢了标记生成的速度(内存带宽)。举例来说,GPT-3模型有1750亿个参数,使用FP16表示需要350GB的内存,而最新的H100 GPU只有96GB的内存,更不用说边缘设...
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....
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...
@article{lin2023awq, title={AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration}, author={Lin, Ji and Tang, Jiaming and Tang, Haotian and Yang, Shang and Dang, Xingyu and Han, Song}, journal={arXiv}, year={2023} } ...
@article{lin2023awq, title={AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration}, author={Lin, Ji and Tang, Jiaming and Tang, Haotian and Yang, Shang and Dang, Xingyu and Han, Song}, journal={arXiv}, year={2023} } ...
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, OPT, Vicuna, LLaVA; load to generate quantized w...