P-tuning is a parameter-efficient fine-tuning technique for pre-trained language models. It introduces learnable continuous embeddings as trainable prompts while keeping most model parameters frozen. The approach enables effective adaptation of large language models like GPT to downstream tasks with fewer trainable parameters, supporting few-shot learning scenarios. The repository includes code and datasets for LAMA and SuperGLUE benchmarks.