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The methods uniquely enable the pre-training of a 13B-parameter model on a single 80GB GPU, providing a scalable, efficient, and accessible approach to large-scale model optimization.
Results show the need for evaluations to account for the disjunction between what models represent internally, what they verbalize, and their steering, and the need for evaluations to account for the disjunction between what models represent internally, what they verbalize, and their steering.
FlowMeas is introduced, which uses a generative flow network to directly sample finite ensembles of shallow Clifford measurement circuits subject to a prescribed shot budget and hardware constraints, and establishes generative learning as a flexible and unified framework for quantum measurement design under practical r...
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