This project serves as a comprehensive educational guide for building Large Language Models (LLMs) from the ground up. It demonstrates how to transform raw text into numerical data using a Tokenizer, construct the core GPT Architecture and modern variants like Llama and Qwen, and implement efficient training loops to teach the model to generate coherent text.
LLMs-from-scratch is organized as connected concepts and components. Start broad, then drill down chapter by chapter.
This project serves as a comprehensive educational guide for building Large Language Models (LLMs) from the ground up. It demonstrates how to transform raw text into numerical data using a Tokenizer, construct the core GPT Architecture and modern variants like Llama and Qwen, and implement efficient training loops to teach the model to generate coherent text.
Source Repository: https://github.com/rasbt/LLMs-from-scratch
Follow sequentially or jump to any topic. Start with Tokenizer (Byte Pair Encoding).
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