2 citations · 4 across the 10 of their papers we have counts for
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cs.LG2024
PTSBench: A Comprehensive Post-Training Sparsity Benchmark Towards Algorithms and Models
Zining Wnag, Jinyang Guo, Ruihao Gong +5
With the increased attention to model efficiency, post-training sparsity (PTS) has become more and more prevalent because of its effectiveness and efficiency. However, there remain…
cs.LG2024★ 2 cited
LLMC: Benchmarking Large Language Model Quantization with a Versatile Compression Toolkit
Ruihao Gong, Yang Yong, Shiqiao Gu +5
Recent advancements in large language models (LLMs) are propelling us toward artificial general intelligence with their remarkable emergent abilities and reasoning capabilities. Ho…
cs.LG2022★ 2 cited
Compressing Models with Few Samples: Mimicking then Replacing
Huanyu Wang, Junjie Liu, Xin Ma +3
Few-sample compression aims to compress a big redundant model into a small compact one with only few samples. If we fine-tune models with these limited few samples directly, models…