
186.6K
AWIt’s Day 14 of building a LLM from scratch ✨
Most people think LLMs are complex because of code.
They’re complex because of configuration and scale.
Today I broke down the GPT-2 config that defines how the model thinks, remembers, and attends.
GPT-2 is just a set of numbers that define scale: vocab size, context length, embedding dimension, layers, and attention heads.
Breaking down the GPT-2 (124M) configuration: 50,257-token vocabulary, 1,024-token context, 768-dimensional embeddings, 12 transformer layers with 12 attention heads, dropout 0.1, and bias-free QKV projections. Understanding these parameters is key to scaling LLMs efficiently.
#deeplearning #generativeai #womenwhocode #largelanguagemodels
@awomanindatascience










