README.md

metadata

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 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:

   git clone https://github.com/awaistahseen009/SQLGPT
   

2. Install the Requirements:

   pip install -r requirements.txt
   

3. Download the Quantized Model Setup:

   Download the quantized model from Hugging Face.

4. Run the UI Interface:

   • Update the API request URL in App.jsx:

     // Change this line in App.jsx
     const apiUrl = "http://localhost:8000/query";
     

   • Start the server:

     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:

   git clone https://github.com/ggerganov/llama.cpp
   

2. Download the Quantized Model:

   Download the quantized model from Hugging Face.

3. Run the Model:

   In your terminal, execute the following command:

   ./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:

   ### 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.

Contributing

Contributions are welcome! Feel free to fork the project, make improvements, and submit a pull request.

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Happy querying with SQLGPT!