README.md

metadata

base_model: Qwen/Qwen2.5-Coder-3B-Instruct
library_name: peft
pipeline_tag: text-generation
tags:
  - text-to-sql
  - qlora
  - unsloth
  - hinglish
datasets:
  - xlangai/spider

Text-to-SQL LoRA adapter (English + Hinglish)

LoRA adapter for Qwen/Qwen2.5-Coder-3B-Instruct, fine-tuned with QLoRA (Unsloth, rank 16, 1 epoch) on Spider train plus 600 Hinglish-translated questions. Trained on a single RTX 4060 8 GB laptop GPU.

Code, evaluation scripts and demo app: https://github.com/KRSNA444/text2sql-hinglish

Results (execution accuracy, same script and settings for all rows)

Model	English (Spider dev, 1,034 q)	Hinglish (200 q)
Base Qwen2.5-Coder-3B-Instruct	62.3%	46.5%
Fine-tuned, English only	72.2%	55.5%
This adapter (English + 600 Hinglish)	71.6%	66.0%

Prompt format

The model expects a system prompt, then a user message of the form:

Database schema:
CREATE TABLE singer (
  singer_id number,
  name text,
  PRIMARY KEY (singer_id)
);

Question: Kitne singers hain?

SQL:

See common.py in the GitHub repo for the exact system prompt and schema formatter.

Limitations

• Single run, single seed. Hinglish test questions were produced the same way as the Hinglish training questions (LLM-assisted translation), so the Hinglish number may be optimistic for real users.
• Trained and evaluated on Spider (small, clean schemas). Not tested on large real-world databases.
• Always run generated SQL read-only; the demo app allows only a single SELECT.

Licenses and credits

Dataset: Spider (Yu et al., 2018), CC BY-SA 4.0. Base model by Alibaba Qwen: check the base model's license before commercial use.