8 citations · 11 across the 11 of their papers we have counts for
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cs.CV2024
PTQ4VM: Post-Training Quantization for Visual Mamba
Younghyun Cho, Changhun Lee, Seonggon Kim +1
Visual Mamba is an approach that extends the selective space state model, Mamba, to vision tasks. It processes image tokens sequentially in a fixed order, accumulating information…
cs.CL2024★ 1 cited
QEFT: Quantization for Efficient Fine-Tuning of LLMs
Changhun Lee, Jun-gyu Jin, Younghyun Cho +1
With the rapid growth in the use of fine-tuning for large language models (LLMs), optimizing fine-tuning while keeping inference efficient has become highly important. However, thi…
cs.CV2024★ 1 cited
HLQ: Fast and Efficient Backpropagation via Hadamard Low-rank Quantization
Seonggon Kim, Eunhyeok Park
With the rapid increase in model size and the growing importance of various fine-tuning applications, lightweight training has become crucial. Since the backward pass is twice as e…