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About this paper
Ilan Yaniv Zeisler, Sebastian Clancy, Pouriya Bayat, Saaim Raad, Ivan Kraskov, Matthew Xie, Vivian White, Spencer Perkins and 3 more
The authors evaluate QLoRA, which stores pretrained protein-language model weights in 4-bit precision and trains small low-rank adapters, as a memory-saving alternative to full fine-tuning. Across prediction tasks, many model–task pairs retain more than 90% of full-fine-tuning performance. Peak training GPU memory falls by an average of 63%, but savings vary by model and configuration.
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