Apache Groovy vs. Crystal vs. Java vs. StarCoder Comparison Chart

Crystal’s syntax is heavily inspired by Ruby’s, so it feels natural to read and easy to write, and has the added benefit of a lower learning curve for experienced Ruby devs. Crystal is statically type-checked, so any type errors will be caught early by the compiler rather than fail on runtime. Moreover, and to keep the language clean, Crystal has built-in type inference, so most type annotations are unneeded. All types are non-nilable in Crystal, and available variables are represented as a union between the type and nil. As a consequence, the compiler will automatically check for null references in compile time. Crystal’s answer to metaprogramming is a powerful macro system, which ranges from basic templating and AST inspection to types inspection and running arbitrary external programs.

StarCoder and StarCoderBase are Large Language Models for Code (Code LLMs) trained on permissively licensed data from GitHub, including from 80+ programming languages, Git commits, GitHub issues, and Jupyter notebooks. Similar to LLaMA, we trained a ~15B parameter model for 1 trillion tokens. We fine-tuned StarCoderBase model for 35B Python tokens, resulting in a new model that we call StarCoder. We found that StarCoderBase outperforms existing open Code LLMs on popular programming benchmarks and matches or surpasses closed models such as code-cushman-001 from OpenAI (the original Codex model that powered early versions of GitHub Copilot). With a context length of over 8,000 tokens, the StarCoder models can process more input than any other open LLM, enabling a wide range of interesting applications. For example, by prompting the StarCoder models with a series of dialogues, we enabled them to act as a technical assistant.