Provides implementations of CoOp and CoCoOp, two prompt learning methods for adapting large vision-language models (CLIP) to downstream datasets without full model fine-tuning. The techniques learn trainable prompt embeddings that combine with frozen pre-trained model features, enabling parameter-efficient transfer learning to new domains. Supports various downstream classification tasks including ImageNet and domain-shifted variants.