We recently undertook the challenge of creating a chatbot for Argilla 2.0, and we chose to use distilabel to streamline the process. Distilabel is a powerful tool for data labeling and model distillation, which proved invaluable in training a responsive and accurate assistant.
The project involved curating a dataset of user queries and desired responses, then using distilabel to fine-tune a language model. This approach allowed us to quickly iterate on the chatbot's performance and tailor it to Argilla's specific domain.
Key steps included:
- Collecting real user questions from early testers
- Labeling data with distilabel's active learning features
- Distilling a smaller, faster model from a larger teacher model
- Deploying the chatbot within the Argilla interface
The result is a chatbot that understands context and provides helpful answers, significantly improving the user experience. We're excited about the potential of distilabel for similar projects.