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@@ -79,4 +79,18 @@ Work in progress.
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  ## Limitations
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  - The model is only runnable on NVIDIA GPUs with architectures sm_86 (Ampere: RTX 3090, A6000), sm_89 (Ada: RTX 4090), and sm_80 (A100). See this [issue](https://github.com/mit-han-lab/nunchaku/issues/1) for more details.
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- - You may observe some slight differences from the BF16 models in details.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ## Limitations
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  - The model is only runnable on NVIDIA GPUs with architectures sm_86 (Ampere: RTX 3090, A6000), sm_89 (Ada: RTX 4090), and sm_80 (A100). See this [issue](https://github.com/mit-han-lab/nunchaku/issues/1) for more details.
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+ - You may observe some slight differences from the BF16 models in details.
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+
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+ ### Citation
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+
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+ If you find this model useful or relevant to your research, please cite
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+
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+ ```bibtex
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+ @article{
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+ li2024svdquant,
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+ title={SVDQuant: Absorbing Outliers by Low-Rank Components for 4-Bit Diffusion Models},
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+ author={Li*, Muyang and Lin*, Yujun and Zhang*, Zhekai and Cai, Tianle and Li, Xiuyu and Guo, Junxian and Xie, Enze and Meng, Chenlin and Zhu, Jun-Yan and Han, Song},
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+ journal={arXiv preprint arXiv:2411.05007},
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+ year={2024}
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+ }
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+ ```