Showing cs.LGShow all
2 papers · 1 filter
cs.LG2025
Quamba2: A Robust and Scalable Post-training Quantization Framework for Selective State Space Models
Hung-Yueh Chiang, Chi-Chih Chang, Natalia Frumkin +3
State Space Models (SSMs) are emerging as a compelling alternative to Transformers because of their consistent memory usage and high performance. Despite this, scaling up SSMs on c…
cs.LG2024
Quamba: A Post-Training Quantization Recipe for Selective State Space Models
Hung-Yueh Chiang, Chi-Chih Chang, Natalia Frumkin +2
State Space Models (SSMs) have emerged as an appealing alternative to Transformers for large language models, achieving state-of-the-art accuracy with constant memory complexity wh…