Building LLMs from Scratch: A Deep Dive into Tokenization and Data Pipelines
Published:Jan 14, 2026 01:00
•1 min read
•Zenn LLM
Analysis
This article series targets a crucial aspect of LLM development, moving beyond pre-built models to understand underlying mechanisms. Focusing on tokenization and data pipelines in the first volume is a smart choice, as these are fundamental to model performance and understanding. The author's stated intention to use PyTorch raw code suggests a deep dive into practical implementation.
Key Takeaways
- •The article series aims to build an LLM from scratch using PyTorch.
- •Vol. 1 focuses on tokenization and data pipelines, core components of LLMs.
- •The series emphasizes understanding the 'why' and 'how' of LLM functionality.
Reference
“The series will build LLMs from scratch, moving beyond the black box of existing trainers and AutoModels.”