6 citations · 9 across the 6 of their papers we have counts for
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cs.LG2024
Integer Scale: A Free Lunch for Faster Fine-grained Quantization of LLMs
Qingyuan Li, Ran Meng, Yiduo Li +5
We introduce Integer Scale, a novel post-training quantization scheme for large language models that effectively resolves the inference bottleneck in current fine-grained quantizat…
cs.LG2023
A Speed Odyssey for Deployable Quantization of LLMs
Qingyuan Li, Ran Meng, Yiduo Li +6
The large language model era urges faster and less costly inference. Prior model compression works on LLMs tend to undertake a software-centric approach primarily focused on the si…
cs.LG2019★ 3 cited
SCARLET-NAS: Bridging the Gap between Stability and Scalability in Weight-sharing Neural Architecture Search
Xiangxiang Chu, Bo Zhang, Qingyuan Li +2
To discover powerful yet compact models is an important goal of neural architecture search. Previous two-stage one-shot approaches are limited by search space with a fixed depth. I…