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.
Recent articles
- Weeknotes: Llama 3, AI for Data Journalism, llm-evals and datasette-secrets - 23rd April 2024
- Options for accessing Llama 3 from the terminal using LLM - 22nd April 2024
- AI for Data Journalism: demonstrating what we can do with this stuff right now - 17th April 2024
- Three major LLM releases in 24 hours (plus weeknotes) - 10th April 2024
- Building files-to-prompt entirely using Claude 3 Opus - 8th April 2024
- Running OCR against PDFs and images directly in your browser - 30th March 2024