---
library_name: transformers
tags:
- text-to-sql
- falcon
- lora
- sql-generation
- natural-language-to-sql
- huggingface
---

# Model Card for Falcon SQL Generator (LoRA Fine-Tuned)

A lightweight Falcon-1B model fine-tuned using LoRA on Spider-style SQL generation examples. The model takes in a user query and schema context and generates corresponding SQL queries. It supports few-shot prompting and can be integrated with retrieval-based systems.

## Model Details

### Model Description

This model is a fine-tuned version of `tiiuae/falcon-rw-1b` using Parameter-Efficient Fine-Tuning (LoRA) for the text-to-SQL task. It is trained on custom Spider-style examples that map natural language questions to valid SQL queries over a provided schema context.

- **Developed by:** revanth kumar  
- **Finetuned by:** revanth kumar  
- **Model type:** Causal Language Model (`AutoModelForCausalLM`)  
- **Language(s):** English (natural language input) and SQL (structured query output)  
- **License:** Apache 2.0 (inherits from base Falcon model)  
- **Finetuned from:** `tiiuae/falcon-rw-1b`  

### Model Sources

- **Repository:** [https://huggingface.co/revanthkumarg/falcon-sql-lora](https://huggingface.co/revanthkumarg/falcon-sql-lora)  

## Uses

### Direct Use

This model can be directly used for natural language to SQL generation. It supports queries like:

> "List all employees earning more than 50000"  
→ `SELECT * FROM employees WHERE salary > 50000;`

It is suitable for:
- Low-code/no-code query interfaces
- Data analyst assistant tools
- SQL tutoring bots

### Downstream Use

This model can be integrated into retrieval-augmented systems, or further fine-tuned on enterprise-specific schema and query examples.

### Out-of-Scope Use

- It may not generalize well to highly complex, nested, or ambiguous queries.
- Should not be used in production environments involving sensitive financial or health data without further validation.

## Bias, Risks, and Limitations

- The model may generate incorrect or suboptimal SQL if the prompt is ambiguous or the schema context is incomplete.
- It inherits any limitations or biases from the Falcon base model.

### Recommendations

Users should validate generated queries before executing them on production databases. Schema consistency and prompt clarity are key to good performance.

## How to Get Started with the Model

You can load and run the model using the Hugging Face `transformers` library:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("revanthkumarg/falcon-sql-lora")
tokenizer = AutoTokenizer.from_pretrained("tiiuae/falcon-rw-1b")

prompt = "List all employees earning more than 50000:"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0]))