DB-GPT-Hub
This project utilizes Large Language Models (LLMs) to improve Text-to-SQL parsing efficiency and accuracy. It achieves notable accuracy improvements in complex database queries by employing Supervised Fine-Tuning (SFT) and using datasets like Spider. With support for various models, including CodeLlama and Baichuan2, it minimizes hardware demands through QLoRA. A valuable resource for developers, the initiative offers comprehensive instructions for data preprocessing, model training, prediction, and evaluation.
