  Contrastive Language-Image Pre-training (CLIP) has been shown to improve
zero-shot generalization capabilities of language and vision models. In this
paper, we extend CLIP for efficient knowledge distillation, by utilizing
embeddings as teachers. Typical knowledge distillation frameworks require
running forward passes through a teacher model, which is often prohibitive in
the case of billion or trillion parameter teachers. In these cases, using only
the embeddings of the teacher models to guide the distillation can yield
significant computational savings. Our preliminary findings show that
CLIP-based knowledge distillation with embeddings can outperform full scale
knowledge distillation using $9\times$ less memory and $8\times$ less training
time. Code available at: https://github.com/lnairGT/CLIP-Distillation/
