Quoting SparseGPT, by Elias Frantar and Dan Alistarh

We show for the first time that large-scale generative pretrained transformer (GPT) family models can be pruned to at least 50% sparsity in one-shot, without any retraining, at minimal loss of accuracy. [...] We can execute SparseGPT on the largest available open-source models, OPT-175B and BLOOM-176B, in under 4.5 hours, and can reach 60% unstructured sparsity with negligible increase in perplexity: remarkably, more than 100 billion weights from these models can be ignored at inference time.

— SparseGPT, by Elias …

accuracy ai bloom dan family generative generativeai gpt homebrewllms least llms loss opt-175b scale show sparsity transformer