The Hong Kong Polytechnic University
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- Qin Haotong on pushing AI model compression to the limit
DeepTech interviewed Haotong Qin, an assistant professor at Hong Kong Polytechnic University, about his research on model efficiency, including cost, inference latency, and retaining capability under limited resources. Qin described BiLLM, an ICML 2024 study that compressed the average weight bit width of a pretrained language model to about 1.08 bits while retaining some language capability. He also said that lower latency in physical-system deployments can make compressed models more useful in real-world decision-making.
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