353 citations · 379 across the 21 of their papers we have counts for
4 papers · 1 filter
Zeroth-Order Fine-Tuning of LLMs with Extreme Sparsity
Wentao Guo, Jikai Long, Yimeng Zeng +9
Zeroth-order optimization (ZO) is a memory-efficient strategy for fine-tuning Large Language Models using only forward passes. However, the application of ZO fine-tuning in memory-…
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…
STAT: Shrinking Transformers After Training
Megan Flynn, Alexander Wang, Dean Edward Alvarez +2
We present STAT: a simple algorithm to prune transformer models without any fine-tuning. STAT eliminates both attention heads and neurons from the network, while preserving accurac…
QuIP#: Even Better LLM Quantization with Hadamard Incoherence and Lattice Codebooks
Albert Tseng, Jerry Chee, Qingyao Sun +2
Post-training quantization (PTQ) reduces the memory footprint of LLMs by quantizing their weights to low-precision. In this work, we introduce QuIP#, a weight-only PTQ method that…