- 01 Oct, 2024 1 commit
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Lily Liu authored
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- 28 Sep, 2024 1 commit
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Varun Sundar Rabindranath authored
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- 27 Sep, 2024 1 commit
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Varun Sundar Rabindranath authored
Co-authored-by:Varun Sundar Rabindranath <varun@neuralmagic.com>
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- 25 Sep, 2024 1 commit
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Travis Johnson authored
Signed-off-by:Travis Johnson <tsjohnso@us.ibm.com>
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- 03 Sep, 2024 1 commit
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Alexander Matveev authored
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- 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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- 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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- 11 Jun, 2024 1 commit
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Nick Hill authored
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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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- 16 Apr, 2024 1 commit
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Cade Daniel authored
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