- 26 Dec, 2024 1 commit
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Jee Jee Li authored
Signed-off-by:Jee Jee Li <pandaleefree@gmail.com>
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- 24 Dec, 2024 1 commit
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Jee Jee Li authored
Signed-off-by:
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- 12 Nov, 2024 1 commit
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Umesh authored
Signed-off-by:
Umesh Deshpande <udeshpa@us.ibm.com> Co-authored-by:
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- 11 Oct, 2024 1 commit
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Jee Jee Li authored
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- 03 Aug, 2024 1 commit
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Jee Jee Li authored
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- 22 Jul, 2024 1 commit
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Jiaxin Shan authored
Co-authored-by:Antoni Baum <antoni.baum@protonmail.com>
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- 21 Jun, 2024 1 commit
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Jee Li authored
Co-authored-by:
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- 10 Jun, 2024 1 commit
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Cyrus Leung authored
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- 07 Jun, 2024 1 commit
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Antoni Baum 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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- 27 Apr, 2024 1 commit
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Austin Veselka authored
Co-authored-by:
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- 29 Mar, 2024 1 commit
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Nick Hill authored
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- 23 Jan, 2024 1 commit
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Antoni Baum authored
Co-authored-by:
Chen Shen <scv119@gmail.com> Co-authored-by:
Shreyas Krishnaswamy <shrekris@anyscale.com> Co-authored-by:
Avnish Narayan <avnish@anyscale.com>
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