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Structured Pruning of Large Language Models
[article]
2019
arXiv
pre-print
Large language models have recently achieved state of the art performance across a wide variety of natural language tasks. Meanwhile, the size of these models and their latency have significantly increased, which makes their usage costly, and raises an interesting question: do language models need to be large? We study this question through the lens of model compression. We present a novel, structured pruning approach based on low rank factorization and augmented Lagrangian L0 norm
arXiv:1910.04732v1
fatcat:o2daer4ftraalg4jfnvssv6tgq