Machine Learning 103: Exploring LLM Code Generation

This executable blog post is the third in a series related to machine learning and explores code generation from a 16 billion parameter large language model (LLM). After a brief look under the hood at the LLM structure and parameter allocation, we generate a variety of Python functions and make observations related to code quality and security. Similar to OpenAI’s ChatGPT, Google’s Bard, Meta’s LLaMa and GitHub’s Copilot, we will see some fantastic capabilities and a few intriguing misses. The results demonstrate that human expertise remains crucial, whether it be in engineering the prompt or in evaluating the generated code for suitability.

The Jupyter-based notebook can be found here

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