BitDelta: Your Fine-Tune May Only Be Worth One Bit
Authors: name: James Liu, name: Guangxuan Xiao, name: Kai Li, name: Jason D. Lee, name: Song Han, name: Tri Dao, name: Tianle Cai

Large Language Models (LLMs) are typically trained in two phases:
pre-training on large internet-scale datasets, and fine-tuning for downstream
tasks. Given the higher computational demand of pre-training, it's intuitive to
assume that fine-tuning adds less new information to the model, and is thus
more compressible. We explore this assumption by decomposing the weights of
fine-tuned models into their pre-trained components and an additional delta. We
introduce a simple method, BitDelta, which successfully quantizes this delta
down to 1 bit without compromising performance. This interesting finding not
only highlights the potential redundancy of information added during
fine-tuning, but also has significant implications for the multi-tenant serving
and multi-tenant storage of fine-tuned models. By enabling the use of a single
high-precision base model accompanied by multiple 1-bit deltas, BitDelta
dramatically reduces GPU memory requirements by more than 10x, which can also
be translated to enhanced generation latency in multi-tenant settings. We
validate BitDelta through experiments across Llama-2 and Mistral model
families, and on models up to 70B parameters, showcasing minimal performance
degradation over all tested settings.