Blenderbot 1B Distill

🚀 Chatbot Model for Open - domain Conversations

This project presents recipes for building high - performing open - domain chatbots, with publicly available models and code. It shows that appropriate training data and generation strategies can enable large - scale models to learn essential conversational skills.

🚀 Quick Start

No specific quick - start content is provided in the original document.

✨ Features

• Conversational Skills: The models can learn a variety of conversational skills such as providing engaging talking points, listening, asking and answering questions, and displaying knowledge, empathy and personality.
• Scalability: Variants of the models are built with 90M, 2.7B and 9.4B parameters.
• Public Availability: Both models and code are publicly available.
• Human Evaluation: The best models outperform existing approaches in multi - turn dialogue in terms of engagingness and humanness.

📚 Documentation

Model description

• Paper: Recipes for building an open - domain chatbot
• Original PARLAI Code: https://parl.ai/projects/recipes/

Abstract

Building open - domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are trained on gives improved results, we show that other ingredients are important for a high - performing chatbot. Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation. We show that large scale models can learn these skills when given appropriate training data and choice of generation strategy. We build variants of these recipes with 90M, 2.7B and 9.4B parameter neural models, and make our models and code publicly available. Human evaluations show our best models are superior to existing approaches in multi - turn dialogue in terms of engagingness and humanness measurements. We then discuss the limitations of this work by analyzing failure cases of our models.

📄 License

The model is released under the Apache 2.0 license.