Qwen3.5-0.8B-Text2SQL

Qwen3.5-0.8B Text2SQL

Supervised Fine-Tuning (SFT) for Natural Language to SQL Generation

Fine-tuning Qwen3.5-0.8B using Spider, BIRD23, and SynSQL-2.5M datasets with QLoRA + Unsloth.

Repository Project: https://github.com/MuhammadNafishZaldinanda/finetuning-text2sql

Dataset

Dialect: SQLite

SynSQL-2.5M Filtering Configuration

Instruction Prompt

Task Overview:
You are a data science expert. Below, you are provided with a database schema and a natural language question. Your task is to understand the schema and generate a valid SQL query to answer the question.

Database Engine:
SQLite

Database Schema:
{db_details}
This schema describes the database's structure, including tables, columns, primary keys, foreign keys, and any relevant relationships or constraints.

Question:
{evidence}{question}

Instructions:
- Make sure you only output the information that is asked in the question. If the question asks for a specific column, make sure to only include that column in the SELECT clause, nothing more.
- The generated query should return all of the information asked in the question without any missing or extra information.
- Before generating the final SQL query, please think through the steps of how to write the query.

Output Format:
In your answer, please enclose the generated SQL query in a code block:
```sql
-- Your SQL query
```

Take a deep breath and think step by step to find the correct SQL query.

LoRA Configuration

Training Configuration

Training Results

Model Performance Evaluation: Base vs. Fine-Tuned (Qwen3.5-0.8B)

1. Base Model (Qwen3.5-0.8B)

Overall Performance

Performance by Difficulty

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2. Fine-Tuned Model (QLoRA)

Overall Performance

Performance by Difficulty

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3. Head-to-Head Comparison