- 31 Aug, 2024 1 commit
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Robert Shaw authored
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- 30 Aug, 2024 1 commit
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afeldman-nm authored
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- 27 Aug, 2024 1 commit
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Megha Agarwal authored
Co-authored-by:Alexander Matveev <alexm@neuralmagic.com>
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- 21 Aug, 2024 1 commit
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Cyrus Leung authored
Co-authored-by:
Woosuk Kwon <woosuk.kwon@berkeley.edu> Co-authored-by:
Fei <dfdfcai4@gmail.com>
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- 04 Aug, 2024 1 commit
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youkaichao authored
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- 20 Jul, 2024 1 commit
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Travis Johnson authored
Signed-off-by:Travis Johnson <tsjohnso@us.ibm.com>
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- 11 Jul, 2024 1 commit
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Robert Shaw authored
Co-authored-by:Zifei Tong <zifeitong@gmail.com>
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- 02 Jul, 2024 1 commit
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Murali Andoorveedu authored
Signed-off-by:Muralidhar Andoorveedu <muralidhar.andoorveedu@centml.ai>
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- 15 Jun, 2024 1 commit
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Cyrus Leung authored
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- 11 Jun, 2024 1 commit
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Nick Hill authored
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- 05 Jun, 2024 1 commit
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zifeitong authored
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- 29 May, 2024 1 commit
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Junichi Sato authored
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- 28 May, 2024 1 commit
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Cyrus Leung authored
Co-authored-by:Roger Wang <ywang@roblox.com>
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- 18 May, 2024 1 commit
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SangBin Cho authored
Currently we need to call rotary embedding kernel for each LoRA, which makes it hard to serve multiple long context length LoRA. Add batched rotary embedding kernel and pipe it through. It replaces the rotary embedding layer to the one that is aware of multiple cos-sin-cache per scaling factors. Follow up of https://github.com/vllm-project/vllm/pull/3095/files
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- 03 May, 2024 1 commit
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Cade Daniel authored
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- 26 Apr, 2024 1 commit
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SangBin Cho authored
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- 23 Apr, 2024 1 commit
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SangBin Cho authored
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- 21 Apr, 2024 1 commit
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GeauxEric authored
Co-authored-by:
Yun Ding <yunding@nvidia.com> Co-authored-by:
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- 16 Apr, 2024 1 commit
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Cade Daniel authored
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