Show HN: Distilled 0.6B text-to-SQL model (github.com)

A new breakthrough in AI has emerged with the introduction of the Distilled 0.6B text-to-SQL model, enabling users to convert natural language questions into SQL queries using a small, local model. This innovation allows for execution of SQL queries directly on a user's machine without the need for cloud services or API keys, promoting full data privacy. The fine-tuned Qwen3-4B model has demonstrated remarkable accuracy, matching the performance of a much larger 685B model while being 170 times smaller, and the 0.6B variant achieves 74% accuracy, making it suitable for resource-constrained environments. This advancement is significant for the AI/ML community as it illustrates the efficacy of fine-tuning smaller models for specific tasks like SQL generation, which can often be challenging for off-the-shelf models. The trained models address common SQL pitfalls, such as invalid syntax and incorrect use of database schemas, and can generate executable queries that return accurate results swiftly. The approach employed offers a replicable methodology for other narrow AI tasks, emphasizing the potential for smaller, localized AI deployments to deliver powerful outcomes without sacrificing performance or privacy.