# 🦅 Ftel-Text2SQL

**Ftel-Text2SQL** is a Text-to-SQL model developed by FPT Telecom. Fine-tuned specifically for complex, cross-domain database querying, it achieves high execution accuracy on challenging benchmarks like BIRD-SQL.

## 🎯 BirdSQL results
- **Dev set:** 71.19
- **Test set:** 72.78

## 🚀 Model Details
- **Base Architecture:** [Qwen3-Coder-30B-A3B-Instruct](https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct)
- **Developer:** FPT Telecom
- **Language(s):** English
- **License:** Apache 2.0
- **Task:** Text-to-SQL (Code Generation)

## 🧠 Training Methodology
This model was trained using a robust two-stage pipeline designed to enhance both structural understanding and logical reasoning for SQL generation:

### Stage 1: Supervised Fine-Tuning (SFT)
In the first phase, the base Qwen2.5-Coder model underwent SFT using high-quality, curated Text-to-SQL datasets. This stage focused on teaching the model complex database schemas, foreign key relationships, and advanced SQL dialect nuances (such as window functions and complex joins).

### Stage 2: Group Relative Policy Optimization (GRPO)
To further align the model's reasoning capabilities, we implemented a reinforcement learning phase using **GRPO** (Group Relative Policy Optimization). 
Unlike standard RLHF which relies on human preference models, our GRPO implementation utilized **execution-based reward functions**. The model was rewarded based on:
1. **Execution Correctness:** Whether the generated SQL query executed successfully without syntax errors.
2. **Result Equivalence:** Whether the executed output matched the ground truth execution results.

This dual-stage approach significantly reduced syntax hallucination and improved the model's multi-step reasoning capabilities for real-world database queries.

## 💻 Usage & Prompt Format
This model inherits the ChatML format from the Qwen2.5 family. We highly recommend using a **Few-shot** prompting strategy with Self-Consistency (Majority Voting) for optimal performance.

### Example Inference Code (vLLM)
```python
from vllm import LLM, SamplingParams

llm = LLM(model="YourUsername/Ftel-Text2SQL", tensor_parallel_size=2)
sampling_params = SamplingParams(temperature=0.0, max_tokens=512)

# Standard ChatML Prompting
prompt = """<|im_start|>system
You are an expert SQL developer. Generate a valid SQL query based on the given schema and question.
<|im_end|>
<|im_start|>user
Schema: [Your Database Schema Here]
Question: How many customers made a purchase in 2023?
<|im_end|>
<|im_start|>assistant
"""

outputs = llm.generate([prompt], sampling_params)
print(outputs[0].outputs[0].text)