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cs.LG2026
L: Large Lookup Layers
Albert Tseng, Christopher De Sa
Modern sparse language models typically achieve sparsity through Mixture-of-Experts (MoE) layers, which dynamically route tokens to dense MLP "experts." However, dynamic hard routi…
cs.LG2026
Model-Preserving Adaptive Rounding
Albert Tseng, Zhaofeng Sun, Christopher De Sa
The goal of quantization is to produce a compressed model whose output distribution is as close to the original model's as possible. To do this tractably, most quantization algorit…
cs.LG2025
QTIP: Quantization with Trellises and Incoherence Processing
Albert Tseng, Qingyao Sun, David Hou +1
Post-training quantization (PTQ) reduces the memory footprint of LLMs by quantizing weights to low-precision datatypes. Since LLM inference is usually memory-bound, PTQ methods can…