Train Your Own LLM: A Deep Dive with Ruby - DEV Community

Ruby, renowned for its elegant syntax and focus on developer happiness [3], has carved a strong niche in web development, powering major platforms like Shopify, GitHub, and Airbnb [3]. Ruby on Rails, its flagship framework, emphasizes "convention over configuration," enabling rapid development and prototyping, making it ideal for startups and building Minimum Viable Products (MVPs) [1]. The framework provides robust backend capabilities, handling business logic, databases (like PostgreSQL, MySQL, and improved SQLite in Rails 8), user authentication, and increasingly sophisticated frontend interactions via tools like Hotwire [1]. However, the domain of ML, especially the computationally intensive task of training LLMs, presents different challenges. Python's dominance stems from its rich ecosystem of mature, highly optimized libraries like TensorFlow, PyTorch, and Scikit-learn [1]. While Ruby possesses strengths in text processing and web integration [5], its native ML ecosystem is less extensive. Libraries exist, but often rely on bindings to underlying C/C++ libraries or necessitate integration with Python tools [2].