text-to-sql-gemma7b

Optimizing NLP Models through Quantization and Accelerated Training

Project Description: Text-to-SQL Model Training with Code-Gemma-7B

In this project, I trained a text-to-SQL model using the Code-Gemma-7B model to facilitate SQL query generation from natural language inputs. The project involved several key steps and methodologies:

Objectives

1. Develop a robust text-to-SQL model: My goal was to generate accurate SQL queries based on user-provided English questions and a given database schema.
2. Optimize training using LORA and quantization: I aimed to enhance model efficiency and performance while reducing computational resource requirements.
3. Utilize Weights & Biases (W&B): I tracked and logged the model training processes and outcomes for transparency and reproducibility.

Methodologies

1. Model Selection and Training

• Model: I selected Code-Gemma-7B, a large language model fine-tuned for text-to-SQL tasks.
• Optimization Techniques:
  • LORA (Low-Rank Adaptation): I applied LORA to update only a small percentage (approximately 0.6%) of the model's parameters, significantly reducing training overhead.
  • Quantization: This helped reduce the model size and memory usage without a substantial loss in performance.

2. Training Process

• Environment: I utilized a Kaggle environment with limited GPU memory.
• Training Data: I employed a custom dataset for fine-tuning the model, containing various SQL schema definitions and corresponding queries.
• Epochs and Batch Size: I trained the model for 1 epoch due to memory constraints, experimenting with different batch sizes and sequence lengths to avoid out-of-memory errors.

3. Logging and Monitoring

• Weights & Biases: I integrated W&B for real-time tracking and logging of training metrics such as loss, runtime, and parameter updates.
• Model Evaluation: I evaluated the model's performance on test datasets, comparing generated SQL queries with original ones.

4. Model Deployment

• Model Saving and Uploading: I saved the trained model and uploaded it to the Hugging Face Hub for easy access and deployment.
• Inference Pipeline: I created a pipeline for generating SQL queries from natural language inputs using the fine-tuned model.

Results

• Training Efficiency: The training process, optimized with LORA and quantization, took nearly 2 hours for 1 epoch on the available GPU.
• Model Performance: While the model generated decent results, there is room for improvement with further training and parameter tuning.
• Generated Queries: The model produced SQL queries that, although not perfect, were reasonably accurate and could be further refined.

Future Work

• Extended Training: I plan to utilize more powerful GPUs (e.g., A100 on Google Colab) to increase the training data and epochs for better performance.
• Parameter Tuning: I will experiment with different LORA ranks, batch sizes, and sequence lengths to optimize model accuracy and efficiency.
• Model Evaluation and Testing: I will continue to evaluate the model on diverse datasets to ensure robustness and generalizability.

This project highlights the potential of large language models like Code-Gemma-7B for automating SQL query generation, offering significant time savings and accuracy improvements in database management and querying tasks.