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
Eric Schorn is a Technical Director on NCC Group's Cryptography Services team. He has been programming since 8-bit 6502 assembly was in vogue, designed high-performance CPUs at the the individual transistor level, led the overall marketing function for the $600M/year ARM processor division, and holds 14 US Patents. He co-founded a blockchain-oriented start up and has developed/deployed multiple web applications in the cloud.