Code LLMs

Code LLMs are large language models trained or fine-tuned specifically to read, understand, and generate source code.

Unlike general-purpose models, they learn from vast code repositories, grasp programming syntax and structure, and power the tools that complete, explain, and refactor code. Also known as: Code Models, Code-Specialized LLMs

Authors 5 articles 53 min total read

What this topic covers

  • Foundations — Code LLMs look like ordinary language models, but their training and architecture are tuned for the structure of source code.
  • Implementation — These guides walk through running and adapting a code model on your own hardware.
  • What's changing — Code models and frontier general-purpose models keep leapfrogging each other.
  • Risks & limits — Code LLMs learn from enormous amounts of publicly scraped code, raising hard questions about licensing, attribution, and ownership.

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1

Understand the Fundamentals

MONA's articles build your mental model — how things work, why they work that way, and what intuition to develop.

Concepts covered

Code LLMsFill-in-the-MiddleLLMTransformer ArchitectureSubword TokenizationFine Tuning
2

Build with Code LLMs

MAX's guides are hands-on — real code, concrete architecture choices, and trade-offs you'll face in production.

Tools & techniques

Code LLMs
4

Risks and Considerations

ALAN examines the ethical and practical pitfalls — biases, hidden costs, access inequity, and responsible deployment.

Risks & metrics

Code LLMs