A step-by-step educational tutorial for implementing a small-but-functional GPT-like large language model from scratch using PyTorch and Python. It is designed for learning the internal mechanics of LLMs, including data preparation, attention mechanisms, pretraining, and finetuning.
Project overview
Provides a guided implementation path that explains how large language models work internally by building one from the ground up without relying on external LLM libraries.
Project type
Model Runtime · Learning Resources
Use cases
Chat Assistants · Learning & Education
Deployment
Refer to project documentation
License
License pending
Best for
Developers, educators, and researchers seeking to understand the internal workings of large language models by implementing a GPT-like model from scratch using PyTorch.
Individuals who have a strong foundation in Python programming and are comfortable setting up a local Python environment.
Key capabilities
Offers step-by-step Jupyter notebooks and Python code for implementing a GPT model from scratch. The material covers text data processing, attention mechanisms, pretraining, and finetuning.
Limitations and risks
The project cannot accept contributions that extend the contents of the main chapter code to avoid deviations from the physical book.
Getting started
Clone the repository using 'git clone --depth 1 https://github.com/rasbt/LLMs-from-scratch.git', set up the Python environment by following the setup/README.md file, and then execute chapter notebooks such as ch02/01_main-chapter-code/ch02.ipynb.
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README: This repository contains the code for developing, pretraining, and finetuning a GPT-like LLM and is the official code repository for the book [Build a Large Language Model (From S…