---
datasets:
- xlangai/spider
- birdsql/bird23-train-filtered
- seeklhy/SynSQL-2.5M
language:
- en
base_model:
- Qwen/Qwen3.5-0.8B
---
# 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

| Dataset | Source Paper | Samples Used | Notes | Links |
| :--- | :--- | :---: | :--- | :--- |
| **Spider** | [Spider: A Large-Scale Human-Labeled Dataset...](https://arxiv.org/abs/1809.08887) | 7,000 | All training split. | [Link Google Drive Donwload](https://drive.google.com/file/d/1403EGqzIDoHMdQF4c9Bkyl7dZLZ5Wt6J/view?usp=sharing) |
| **BIRD23-Train-Filtered** | [A BIg Bench for Large-Scale Database Grounded Text-to-SQLs](https://arxiv.org/abs/2305.03111) | 6,626 | Used subset `bird23-train-filtered`. | [HuggingFace Dataset](https://huggingface.co/datasets/birdsql/bird23-train-filtered) |
| **SynSQL-2.5M (Filtered)** | [OmniSQL: Synthesizing High-quality Text-to-SQL Data at Scale](https://arxiv.org/abs/2503.02240) | 7,000 |  Filtering by *question style* dan *SQL complexity*. | [HuggingFace Dataset](https://huggingface.co/datasets/seeklhy/SynSQL-2.5M)<br>[OmniSQL Official Repo](https://github.com/RUCKBReasoning/OmniSQL) |
| **Total** | | **20,626** |  | [NafishZaldinanda/text2sql-omnisql-style](https://huggingface.co/datasets/NafishZaldinanda/text2sql-omnisql-style) |


#### SynSQL-2.5M Filtering Configuration

| Criteria | Value |
|-----------|--------|
| Question Style | Formal, Colloquial, Imperative, Interrogative, Descriptive, Concise |
| Simple | 700 |
| Moderate | 2,800 |
| Complex | 2,800 |
| Highly Complex | 700 |
| Total Samples | 7,000 |

#### Instruction Prompt

````TEXT
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

| Parameter | Value |
| :--- | :--- |
| **Quantization** | 4-bit |
| **LoRA Rank (r)** | 32 |
| **LoRA Alpha** | 64 |
| **LoRA Dropout** | 0.0 |
| **Bias** | none |
| **Trainable Parameters** | 12.78M |
| **Percentage of Trainable Parameters** | 2.22% |
| **Target Modules** | `q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `up_proj`, `down_proj` |

### Training Configuration

| Parameter | Value |
|------------|--------|
| Base Model | Qwen3.5-0.8B |
| Total Dataset | 20626 |
| Epoch | 1 |
| Max Sequence Length | 8704 |
| Learning Rate | 1e-5 |
| Scheduler | Cosine |
| Warmup Ratio | 10% |
| Optimizer | adam_torch_fused |
| Max Gradient Norm | 0.5 |
| Batch Size | 1 |
| Gradient Accumulation Steps | 8 |
| Hardware | NVIDIA RTX 4000 SFF Ada |
| Available VRAM | 20 GB |
| Peak VRAM Usage | ~19 GB |
| Training Time | 7 Hours 36 Minutes |

### Training Results

| Metric | Value |
|---------|-------:|
| Final Train Loss | 0.262 |
| Final Validation Loss | 0.218 |

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

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

### Overall Performance
| Metric | Value |
| :--- | ---: |
| **Accuracy** | **21.3%** |
| Correct | 106 |
| Wrong | 152 |
| Execution Error | 240 |

### Performance by Difficulty
| Difficulty | Correct / Total | Accuracy |
| :--- | :---: | :---: |
| Simple | 51 / 148 | 34.5% |
| Moderate | 47 / 250 | 18.8% |
| Challenging | 8 / 102 | 7.8% |

---

## 2. Fine-Tuned Model (QLoRA)

### Overall Performance
| Metric | Value |
| :--- | ---: |
| **Accuracy** | **18.3%** |
| Correct | 91 |
| Wrong | 171 |
| Execution Error | 236 |

### Performance by Difficulty
| Difficulty | Correct / Total | Accuracy |
| :--- | :---: | :---: |
| Simple | 57 / 148 | 38.5% |
| Moderate | 26 / 250 | 10.4% |
| Challenging | 8 / 102 | 7.8% |

---

## 3. Head-to-Head Comparison

| Metric | Base Model | Fine-Tuned (QLoRA) | Selisih |
| :--- | :---: | :---: | :---: |
| **Overall Accuracy** | **21.3%** | 18.3% | -3.0% |
| **Simple** | 34.5% | **38.5%** | +4.0% |
| **Moderate** | **18.8%** | 10.4% | -8.4% |
| **Challenging** | 7.8% | 7.8% | 0.0% |
| **Execution Error** | 240 | **236** | -4 |