distil-labs/distil-qwen3-0.6b-text2sql

Hugging Face
TEXT GENERATIONConcurrency Cost:1Model Size:0.8BQuant:BF16Ctx Length:32kPublished:Jan 14, 2026License:apache-2.0Architecture:Transformer0.0K Open Weights Warm

The distil-labs/distil-qwen3-0.6b-text2sql is a 0.6 billion parameter Qwen3-based model developed by Distil Labs, fine-tuned for converting natural language questions into SQL queries. Utilizing knowledge distillation from DeepSeek-V3, this compact model achieves strong Text2SQL performance, reaching 74% LLM-as-a-Judge accuracy, a 2x improvement over its base model. With a 40,960 token context length, it is optimized for lightweight and fast local deployment for SQL generation tasks.

Loading preview...

Distil-Qwen3-0.6B-Text2SQL Overview

This model, developed by Distil Labs, is a compact 0.6 billion parameter Qwen3-based model specifically fine-tuned for converting natural language questions into SQL queries. It leverages knowledge distillation from the much larger DeepSeek-V3 model, allowing it to achieve high performance at a significantly smaller size.

Key Capabilities & Performance

  • Text-to-SQL Conversion: Excels at transforming natural language questions into accurate SQL queries.
  • High Accuracy: Achieves 74% on LLM-as-a-Judge accuracy, demonstrating a 2x improvement over its base Qwen3-0.6B model and approaching the performance of the 685B parameter DeepSeek-V3 teacher model.
  • Efficiency: With only 0.6 billion parameters and a 40,960 token context length, it is designed for lightweight and fast inference, suitable for local and edge deployments.
  • SQL Feature Support: Handles a range of SQL complexities including SELECT, WHERE, COUNT, SUM, AVG, MAX, MIN, JOIN, GROUP BY, HAVING, ORDER BY, LIMIT, subqueries, and UNION.
  • Training: Fine-tuned on approximately 10,000 synthetic examples generated by DeepSeek-V3, ensuring robust performance.

Ideal Use Cases

  • Natural Language Interfaces: Building conversational interfaces for databases.
  • SQL Query Assistance: Providing automated SQL generation and autocompletion.
  • Database Chatbots: Powering chatbots for business intelligence and data exploration.
  • Educational Tools: Assisting in learning and practicing SQL.
  • Edge Deployment: Suitable for applications requiring on-device or resource-constrained inference due to its compact size.

Finetunes
1 models