DIALOGPT is a large-scale, tunable neural network model designed for generating conversational responses. Trained on 147 million conversation-like exchanges from Reddit comment chains spanning from 2005 to 2017, DIALOGPT extends the Hugging Face PyTorch transformer to achieve near-human performance in single-turn dialogue settings. The model outperforms strong baseline systems in generating more relevant, contentful, and context-consistent responses. The pre-trained model and training pipeline are publicly released to facilitate research in neural response generation and the development of more intelligent open-domain dialogue systems. DIALOGPT addresses the challenges of conversational neural response generation by capturing the joint distribution of target and source sentences, enabling diverse and context-specific responses. The model is evaluated on a public benchmark dataset (DSTC-7) and a new 6K multi-reference test dataset, achieving state-of-the-art results in both automatic and human evaluations. The release includes an open-source training pipeline and the ability to adapt the model to new dialogue datasets.DIALOGPT is a large-scale, tunable neural network model designed for generating conversational responses. Trained on 147 million conversation-like exchanges from Reddit comment chains spanning from 2005 to 2017, DIALOGPT extends the Hugging Face PyTorch transformer to achieve near-human performance in single-turn dialogue settings. The model outperforms strong baseline systems in generating more relevant, contentful, and context-consistent responses. The pre-trained model and training pipeline are publicly released to facilitate research in neural response generation and the development of more intelligent open-domain dialogue systems. DIALOGPT addresses the challenges of conversational neural response generation by capturing the joint distribution of target and source sentences, enabling diverse and context-specific responses. The model is evaluated on a public benchmark dataset (DSTC-7) and a new 6K multi-reference test dataset, achieving state-of-the-art results in both automatic and human evaluations. The release includes an open-source training pipeline and the ability to adapt the model to new dialogue datasets.