QLoRA makes large language models cheaper to fine-tune by storing the base model in 4 bits and training small, higher-precision adapters. This guide explains LoRA, NormalFloat, double quantization, and paged optimizers, then puts the Guanaco chatbot results in context.

View comments.

CoLT5 processes every input token with lightweight layers and gives selected tokens additional, higher-capacity computation. This guide explains its learned routing, light and heavy branches, faster decoding, and experiments with inputs up to 64k tokens.

View comments.

LoRA adapts a pretrained model by training small, low-rank updates while keeping its original weights fixed. This guide explains the two-matrix construction, the memory and storage savings, the conditions for merging adapters, and the connection to QLoRA.

View comments.