Text-to-SQL Fine-Tuning: Comprehensive README Guide

by Dimemap Team 52 views

Hey everyone! 👋 This is the comprehensive README file for our text-to-SQL fine-tuning project. This document serves as a guide for both new contributors and seasoned users. We'll dive deep into what our project aims to achieve, the technologies we're using, and how you can get involved. We're building something cool that translates natural language into SQL queries, and we're super excited to share it with you!

1. Project Title & Overview

Our project focuses on natural language to SQL conversion. Essentially, we're building a system that can understand your questions in plain English (or any other natural language) and then generate the corresponding SQL query to fetch the data you need. Think of it like a smart translator for databases!

We're using a fine-tuning approach called GRPO (Group Relative Policy Optimization). This is a fancy way of saying we're optimizing our model to generate better SQL queries, making them more accurate and reliable. We're leveraging the TRL (Transformer Reinforcement Learning) library's GRPO Trainer and Verifiers framework to achieve this. This framework provides the tools to train and evaluate our models efficiently.

Our goal is to create a robust and accurate text-to-SQL system that's easy to use and adaptable to various database schemas. We aim to provide a user-friendly way to interact with databases without needing to know SQL.

Key Goal:

  • Revolutionizing Data Access: By enabling users to query databases using natural language, we aim to make data more accessible to everyone, regardless of their technical expertise. This opens up opportunities for data-driven decision-making across various fields.

Approach:

  • Fine-tuning State-of-the-Art Models: We're fine-tuning powerful language models like Llama-3.1-8B/8B-Instruct to understand the nuances of natural language and generate accurate SQL queries.
  • Verifier-Based Reward System: A critical component of our approach is the verifier-based reward system. This system uses a set of verifiers to assess the quality of the generated SQL queries, ensuring they are syntactically correct, semantically accurate, and aligned with the intended meaning.
  • Efficient Training with QLoRA: We're employing QLoRA (Quantized Low-Rank Adaptation) to enable efficient fine-tuning on resource-intensive GPUs such as NVIDIA A100. This allows us to experiment with larger models and datasets without incurring excessive training costs.
  • Modular Configuration with Hydra: Hydra is used to manage and configure various aspects of the project, facilitating experimentation and parameter tuning.
  • Comprehensive Experiment Tracking with WandB: We are integrating with Weights & Biases (WandB) to track experiments, visualize metrics, and monitor the training process.

2. Key Features

Let's break down some of the awesome features of our project:

  • Fine-tuning Llama-3.1-8B/8B-Instruct models: We're leveraging the power of Llama-3.1-8B/8B-Instruct models, fine-tuning them to understand and generate SQL queries from natural language inputs. This means you can interact with the database using simple, everyday language.

  • Verifier-based reward system for SQL generation: This is a core differentiator. Our system uses a verifier-based approach to assess the quality of the generated SQL queries. Verifiers check for syntax correctness, semantic accuracy, and alignment with the original question. This ensures that the generated SQL is correct and provides accurate results.

  • QLoRA for efficient training on A100 GPUs: We use QLoRA to make the most of our hardware. QLoRA enables us to fine-tune large models like Llama-3.1-8B/8B-Instruct on NVIDIA A100 GPUs with lower memory requirements, which saves both time and resources.

  • Hydra-based configuration management: We use Hydra to manage our configurations. This helps in managing different settings, parameters, and experimental setups in a structured and organized manner.

  • WandB integration for experiment tracking: We're using Weights & Biases (WandB) to track our experiments. This helps in visualizing metrics, monitoring the training process, and comparing different experiments to achieve the best results.

3. Architecture Overview

Here's a high-level view of how everything fits together:

  • GRPO Training Pipeline: We follow a GRPO training pipeline where the language model generates SQL queries. These queries are then evaluated using a reward function that incorporates feedback from our verifiers.

  • Verifiers framework integration: We've integrated the Verifiers framework, which includes various components like environments, rubrics, and parsers. These components play a crucial role in evaluating the generated SQL queries and providing feedback to the model.

    • Environments: Simulate database environments to execute the generated SQL queries and validate their correctness.
    • Rubrics: Evaluate the correctness and relevance of the generated SQL queries. The rubrics consider factors such as SQL keyword detection and the valid SQL percentage.
    • Parsers: Validate the format and structure of the generated SQL queries.

  • Dataset: We're using the b-mc2/sql-create-context dataset from HuggingFace. This dataset provides the necessary training data for our model, allowing it to learn the patterns and relationships between natural language questions and SQL queries.

4. Project Structure

Let's take a quick look at how the project is organized:

  • Directory Layout: We'll provide a clear directory structure to help you navigate the project easily. This will include directories for the code, configurations, data, and any other relevant components.

  • Config Management (Hydra): We use Hydra for configuration management. This allows us to define different configurations for training, evaluation, and other tasks. The configurations are organized using a composite approach, making it easy to manage and experiment with different settings.

  • Environment Modules: We've created reusable environment modules that encapsulate various functionalities, such as database connections, data loading, and evaluation metrics. This modular design helps to maintain a clean codebase and promotes code reuse.

5. Prerequisites

Before you dive in, make sure you have the following in place:

  • Python: Python version 3.9 or higher. We recommend using the latest stable release.

  • CUDA: Ensure that your system meets the CUDA requirements for GPU acceleration. This is essential for training the models efficiently. Check your GPU compatibility and install the appropriate CUDA drivers.

  • GPU Recommendation: We recommend using a powerful GPU like the NVIDIA A100. If you don't have access to a local GPU, consider using cloud platforms like Lambda Cloud.

  • HuggingFace Account & Token: You'll need a HuggingFace account and an access token to download the pre-trained models and datasets.

6. Quick Start

This section will provide a step-by-step guide to get you up and running quickly. However, this is a placeholder and will be filled in as we develop the project.

  • Installation: We'll provide clear instructions on how to install all the necessary dependencies and set up your environment.

  • Configuration Setup: We'll walk you through setting up the configuration files, including the paths to the models, datasets, and other settings.

  • Training Command Example: We'll provide a sample command to start the training process. You can customize the command to fit your specific needs.

  • Inference Example: We'll show you how to use the trained model to generate SQL queries from natural language input.

7. Configuration Management

We use Hydra to manage our configurations. Hydra allows us to define different settings, parameters, and experimental setups in a structured and organized manner. This section explains how it works:

  • Hydra Composite Config Structure: Hydra uses a composite config structure, which allows us to create multiple configuration files and combine them. This makes it easy to manage different settings for various tasks.

  • Config Directory Overview: The configuration files are organized in a specific directory structure. We'll provide an overview of the config directory and explain the purpose of each file.

  • .env File: We use an .env file to store environment variables, such as API keys and database credentials. This helps to keep sensitive information secure and separate from the code.

8. Key Components

Let's delve deeper into some of the key components that make our project tick:

  • Environment Types: We're considering both SingleTurnEnv and MultiTurnEnv. SingleTurnEnv handles single-turn queries, while MultiTurnEnv handles conversations. The choice depends on the specific use case and complexity of the interactions.

  • Rubrics: Our rubrics are designed to evaluate the generated SQL queries accurately. They focus on various aspects, such as Valid SQL Percentage and SQL keyword detection. These metrics help us assess the performance of our models.

  • Parsers: We use parsers to validate the format and structure of the generated SQL queries. The parsers ensure that the generated SQL is syntactically correct and can be executed without errors.

9. Training Infrastructure

We'll cover the infrastructure we're using to train and run the models:

  • Lambda Cloud Platform: We're using the Lambda Cloud platform, which provides powerful GPU instances for training. This allows us to train the models faster and with greater efficiency.

  • NVIDIA A100 GPU: We're leveraging the power of the NVIDIA A100 GPUs. These GPUs are designed for high-performance computing and are ideal for training deep learning models.

  • DeepSpeed/Accelerate Support: We plan to integrate DeepSpeed and Accelerate to enable distributed training and speed up the training process. These libraries help to scale the training across multiple GPUs, reducing the time required to train the models.

10. References

Here are some useful resources that can provide more context:

  • Verifiers Documentation: Check out the Verifiers documentation for more information on the framework and its features.

  • TRL Library: The TRL library provides the tools for reinforcement learning with transformers.

  • Related Papers/Resources: We'll include links to relevant research papers and other resources that inspired our work.

11. Roadmap (Placeholder)

This is a placeholder for our roadmap. We'll be updating it as we make progress.

  • Environment setup
  • Training pipeline
  • Evaluation framework
  • HuggingFace Hub deployment

12. Contributing & License

  • Contributing Guidelines: We welcome contributions from the community! Check out our contributing guidelines for information on how to get started.

  • License: We'll choose an appropriate open-source license. The license information will be provided here.

13. Acceptance Criteria

Here's what we're aiming for in this project:

  • Well-Structured README: The README should be well-structured with clear sections, making it easy to navigate and understand.

  • Technical Stack: The technical stack should be clearly described, including the libraries, frameworks, and tools used.

  • Project Goals and Approach: The project goals and approach should be clearly explained, including the natural language to SQL conversion goal and the verifier-based approach.

  • Configuration Management: Configuration management, using Hydra, should be documented.

  • Placeholder Sections: Placeholder sections for future implementation should be included, providing a roadmap for future development.

  • Clean and Professional Markdown Formatting: The Markdown formatting should be clean and professional, ensuring readability and visual appeal.

  • References to Verifiers Documentation: References to the Verifiers documentation should be included, providing more information about the framework.

That's it for now! We are excited to see this project come to life. Stay tuned for more updates! If you have any questions or want to contribute, feel free to reach out. Happy coding!