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title: SQLGPT
sdk: docker
emoji: 🚀
colorFrom: yellow
colorTo: indigo
short_description: Give the context of the table and ask question from model.
---
# SQLGPT
SQLGPT is a powerful model designed to generate SQL queries based on your table information and specific questions. Simply provide the context of your table, ask a question, and SQLGPT will generate the corresponding SQL query for you.
### Live
You can interact with it live here: `https://sqlgpt-hazel.vercel.app/`
But as its deployed on huggingface spaces with 1 thread available and its running on CPU so be patient ;) it can take time (secret!! it can take upto 1 min)
## Features
- **SQL Query Generation:** Input table details and your query; the model generates the appropriate SQL command.
- **Fine-Tunning:** The model is fine-tuned on Google's Gemma 2b using the dataset available [here](https://huggingface.co/datasets/b-mc2/sql-create-context) on Hugging Face.
- **Model Availability:** The model is available on both Kaggle and Hugging Face.
- **Quantization:** The finetunned model is being quantized to 4-bit in GGUF format using llama.cpp
## Getting Started
### Running the UI Interface on Unix Distributions (Linux, macOS)
1. **Clone the Repository:**
```bash
git clone https://github.com/awaistahseen009/SQLGPT
```
2. **Install the Requirements:**
```bash
pip install -r requirements.txt
```
3. **Download the Quantized Model Setup:**
Download the quantized model from [Hugging Face](https://huggingface.co/spaces/awais009/SQLGPT/tree/main).
4. **Run the UI Interface:**
- Update the API request URL in `App.jsx`:
```javascript
// Change this line in App.jsx
const apiUrl = "http://localhost:8000/query";
```
- Start the server:
```bash
uvicorn main:app
```
5. **Launch the UI:**
Run npm run dev in ui folder's terminal and
Open the UI in your browser to interact with the model.
on `http://localhost:8000`
### Windows Users
If you're using Windows, the `llama-cpp` package is not available, so you will need to follow these steps:
1. **Clone the llama.cpp Repository:**
```bash
git clone https://github.com/ggerganov/llama.cpp
```
2. **Download the Quantized Model:**
Download the quantized model from [Hugging Face](https://huggingface.co/spaces/awais009/SQLGPT/tree/main/quantized_model).
3. **Run the Model:**
In your terminal, execute the following command:
```bash
./llama.cpp/llama-cli -m ./quantized_model/sql_gpt_quantized.gguf -n 256 -p "### QUESTION:\n{question_here}\n\n### CONTEXT:\n{context_here}\n\n### [RESPONSE]:\n"
```
4. **Prompt Template:**
Use the following prompt template when interacting with the model:
```text
### QUESTION:
{question_here}
### CONTEXT:
{context_here}
### [RESPONSE]:
```
## Fine-Tuned and Quantization Files
You can download the fine-tuned model and quantization files from the [SQLGPT Fine Tune Material Repository](https://github.com/awaistahseen009/SQLGPTFineTuneMaterial).
## Contributing
Contributions are welcome! Feel free to fork the project, make improvements, and submit a pull request.
---
Happy querying with SQLGPT! |