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SpenseGPT introduces a practical one-shot pruning method for LLMs that enables both sparse and dense GEMMs during inference, improving efficiency.
Single-vector embedding models can be used to extract sparse latent terms, and BM25 can turn this vocabulary into a strong retriever.