My Workflow for Understanding LLM Architectures
Ahead of AI Sebastian Raschka, PhD
A researcher describes their manual workflow for understanding large language model architectures by starting with technical papers, then inspecting open-weight model config files and reference implementations in the Python transformers library to extract concrete architectural details. The approach relies on examining actual working code and configuration files from Hugging Face Model Hub rather than relying solely on published papers, which often lack sufficient detail. This hands-on method helps practitioners learn how these architectures work but does not apply to proprietary closed-weight models like ChatGPT or Claude.
Why it matters
A learning-oriented workflow for understanding new open-weight model releases
