Update README.md

README.md

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-title: SQLGPT
-emoji: 📊
-colorFrom: blue
-colorTo: indigo
-sdk: docker
-pinned: false
-license: mit
+# 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.
+
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Happy querying with SQLGPT!