For this competition, we train a text-only LLM that can operate on 3D protein structures by representing them efficiently in a textual format. We use LFM2-2.6b as a memory-efficient yet capable base model, which supports training and inference with the long textual contexts for handling 3D protein representations.
Full description of approach: https://drive.google.com/file/d/1VyqfJerlXkEiFRH4KrddHR1DIuRmSQag/view?usp=sharing
id: crimson-heron-ice
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id: mellow-otter-frost
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id: azure-ibis-reed
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id: amber-vole-ash
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id: silver-lynx-rose
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id: wild-zebra-cloud
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RBX1
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id: pale-eagle-willow
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id: amber-cobra-birch
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id: quick-moth-pine
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id: vast-boar-willow
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id: rough-bear-topaz
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id: deep-yak-leaf
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id: soft-eagle-willow
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id: swift-otter-iron
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id: hollow-moth-snow
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id: steady-bison-iron
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id: frozen-yak-vine
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id: noble-panther-opal
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id: swift-swan-topaz
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id: soft-lynx-flint
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id: radiant-heron-ruby
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