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AnyMAL: An Efficient and Scalable Any-Modality Augmented Language Model
Seungwhan Moon
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Andrea Madotto
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Zhaojiang Lin
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Tushar Nagarajan
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Matt Smith
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Shashank Jain
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Chun-Fu Yeh
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Prakash Murugesan
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Peyman Heidari
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Yue Liu
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Kavya Srinet
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Babak Damavandi
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Anuj Kumar
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Paper Details:
Month: November
Year: 2024
Location: Miami, Florida, US
Venue:
EMNLP |
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https://github.com/huggingface/transformers
https://www.nvidia.com/en-us/data-center/a100/
https://www.nvidia.com/en-us/data-center/h100/
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