This repository aims to replicate the Zero Bubble technique based on the MLORA/Pipeline framework. The project explores innovative methods to improve model performance by leveraging the MLORA architecture and advanced data processing pipelines.
The Zero Bubble technique is an advanced method that minimizes performance gaps in machine learning models. By utilizing MLORA/Pipeline as the foundational framework, this repository focuses on:
- Implementing core components of the Zero Bubble technique.
- Testing and optimizing the pipeline for seamless integration.
- Achieving reproducible and reliable results.
src/: Source code for the MLORA/Pipeline-based Zero Bubble implementation.data/: Contains datasets or pointers to the datasets used in experiments.scripts/: Helper scripts for data preprocessing, model training, and evaluation.notebooks/: Jupyter notebooks for exploratory data analysis (EDA) and prototyping.tests/: Unit tests to ensure code reliability and correctness.
- Set up the MLORA framework within the repository.
- Develop and integrate the Zero Bubble algorithm.
- Validate implementation correctness with benchmark tests.
- Design the data processing pipeline.
- Incorporate MLORA optimizations into the pipeline.
- Optimize the pipeline for scalability and performance.
- Test the model on standard datasets.
- Evaluate the model's performance against baseline techniques.
- Fine-tune hyperparameters for optimal results.
- Add detailed code comments and function docstrings.
- Create a usage guide for setting up and running the project.
- Add performance results and plots to the repository.
- Python 3.8 or later
- MLORA library (Install using
pip install mlora) - Required dependencies (see
requirements.txt)
- Clone the repository:
git clone https://github.com/yourusername/zero-bubble-replication.git cd zero-bubble-replication