Medical　dialogue　generation　relies　on　natural　language　generation　techniques　to　enable　online　medical　consultations.　Recently,　the　widespread　adoption　of　large-scale　models　in　the　field　of　natural　language　processing　has　facilitated　rapid　advancements　in　this　technology.　Existing　medical　dialogue　models　are　mostly　based　on　BERT　and　pre-trained　on　English　corpora,　but　there　is　a　lack　of　high-performing　models　on　the　task　of　Chinese　medical　dialogue　generation.　To　solve　the　above　problem,　this　paper　proposes　CMed-GPT,　which　is　the　GPT　pre-training　language　model　based　on　Chinese　medical　domain　text.　The　model　is　available　in　two　versions,　namely,　base　and　large,　with　corresponding　perplexity　values　of　8.64　and　8.01.　Additionally,　we　incorporate　lexical　and　entity　embeddings　into　the　dialogue　text　in　a　uniform　manner　to　meet　the　requirements　of　downstream　dialogue　generation　tasks.　By　applying　both　fine-tuning　and　p-tuning　to　CMed-GPT,　we　lowered　the　PPL　from　8.44　to　7.35.　This　study　not　only　confirms　the　exceptional　performance　of　the　CMed-GPT　model　in　generating　Chinese　biomedical　text　but　also　highlights　the　advantages　of　p-tuning　over　traditional　fine-tuning　with　prefix　prompts.　Furthermore,　we　validate　the　significance　of　incorporating　external　information　inmedical　dialogue　generation,　which　enhances　the　quality　of　dialogue　generation.