- 20 Sep, 2024 1 commit
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Jiaxin Shan authored
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- 06 Sep, 2024 1 commit
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Jiaxin Shan authored
Co-authored-by:Jee Jee Li <pandaleefree@gmail.com>
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- 19 Aug, 2024 1 commit
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SangBin Cho 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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- 09 Jul, 2024 1 commit
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Swapnil Parekh authored
Co-authored-by:
Swapnil Parekh <swapnilp@ibm.com> Co-authored-by:
Joe G <joseph.granados@h2o.ai> Co-authored-by:
Antoni Baum <antoni.baum@protonmail.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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- 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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