Overview:

This project focused on building a web application that served as a playground for fine-tuning foundation transformer models. Using a refined version of “self-play,” the platform was designed to test and investigate the efficiency of self-play with large language models (LLMs). The aim was to explore how self-play, traditionally used in reinforcement learning, could be adapted to improve the performance of generative models and their ability to learn from their own output.

Next.js: Used for server-side rendering and static website generation, providing a fast and scalable framework for the web application.

React: Built the user interface to allow users to interact with the fine-tuning processes easily.

Flask: Used on the back end to handle server-side operations, model interactions, and data processing.

Machine Learning Engineering: Involved the integration of transformer models and customizing their training via self-play algorithms.

End-to-End Development: Managed the full development cycle from human interface and experience design, back-end integration, to the deployment of the web application.

Model Fine-Tuning: Implemented algorithms that utilized self-play to fine-tune large language models, allowing the models to iteratively improve through autonomous interaction.

Performance Optimization: Ensured that both the web application and the transformer models were optimized for speed and efficiency, enabling real-time interaction and analysis.

Testing & Evaluation: Conducted multiple rounds of testing to evaluate the performance of self-play in improving model output and its impact on overall model efficiency.

Outcome:

The application successfully allowed users to experiment with transformer models, fine-tuning them using self-play in a controlled environment. This project provided valuable insights into the effectiveness of self-play with LLMs, with potential applications in enhancing the autonomy of language models in tasks like content generation, summarization, and natural language understanding.