SHAPESHIFTER is an AI performance enhancement tool designed to improve boundary efficiency and tensor continuity in machine learning models.
✔ Transversal Edge Optimization – Enhances tensor propagation through adaptive boundary conditions.
✔ Manifold-Aware Processing – Maintains internal coherence across dimensional representations.
✔ Seamless Data Flow – Reduces loss, stabilizes outputs, and preserves high-dimensional integrity.
"A system that learns to shape itself will always outperform one that forces structure upon it."
SHAPESHIFTER is designed for AI researchers, ML engineers, and advanced deep learning applications looking to optimize data integrity, structure, and flow.
- Install dependencies:
pip install -r requirements.txt- (Optional) run the unit tests to verify the environment:
pytest -q- Launch the demo:
python shapeshifter.pyAfter running, open the provided Gradio URL in your browser to explore the layer wrapping demo and text-generation example.
Automated tests run on every push and pull request using GitHub Actions. The
workflow installs the dependencies from requirements.txt and executes the unit
tests with pytest to ensure the core wrapping logic works across environments.
For a visual overview of how the main script works, see FLOW.md. It contains a Mermaid diagram describing the interactions between the wrapping layer, the demo utilities, and the Gradio interface.