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Seesaw: High-throughput LLM Inference via Model Re-sharding
To improve the efficiency of distributed large language model (LLM) inference, various parallelization strategies, such as tensor and pipeline parallelism, have been proposed.
CLLD: Contrastive Learning with Label Distance for Text Classification
Extensive experiments on public benchmarks and internal datasets demonstrate that our method improves the performance of pre-trained models on classification tasks.
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