Yuk050/gemma-3-1b-text-to-sql-model
Hugging Face
TEXT GENERATIONConcurrency Cost:1Model Size:1BQuant:BF16Ctx Length:32kPublished:Jun 26, 2025License:gemmaArchitecture:Transformer0.0K Warm

The Yuk050/gemma-3-1b-text-to-sql-model is a 1 billion parameter Gemma 3 family model, fine-tuned by Yuk050 using QLoRA for efficient text-to-SQL generation. This model translates natural language questions and database schemas into executable SQL queries, making it suitable for applications requiring natural language interaction with databases. It leverages a decoder-only transformer architecture and was trained on the gretelai/synthetic_text_to_sql dataset, covering diverse SQL complexity levels and 100 distinct domains.

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Model Overview

This model is a fine-tuned version of Google's gemma-3-1b large language model, specifically optimized for the text-to-SQL task. It utilizes Quantized Low-Rank Adaptation (QLoRA) for efficient fine-tuning, enabling deployment on systems with limited computational resources. Its core function is to convert natural language questions and provided database schemas into executable SQL queries.

Key Capabilities

  • Text-to-SQL Generation: Translates natural language prompts and database schemas into SQL queries.
  • Efficient Fine-tuning: Uses QLoRA with unsloth for reduced computational overhead during training.
  • Broad SQL Coverage: Trained on a synthetic dataset covering 100 distinct domains and a wide range of SQL complexities, including aggregations, joins, subqueries, and window functions.
  • Conversational Format: Processes input in a conversational style, expecting schema and prompt from the user.

Intended Use Cases

This model is primarily intended for research and development in text-to-SQL generation. It can be integrated into:

  • Business intelligence tools.
  • Data analysis platforms.
  • Conversational AI agents requiring database interaction.

Limitations

  • Performance may vary on real-world, noisy, or highly complex database schemas due to the synthetic nature of its training data.
  • May struggle with intricate schemas or ambiguous natural language queries.
  • Primarily trained on standard SQL syntax, potentially requiring adaptation for specific SQL dialects (e.g., PostgreSQL, MySQL).
  • Requires thorough validation and human oversight for sensitive or mission-critical systems.