Lecture 12: The entire Data Preprocessing Pipeline of Large Language Models (LLMs)

Описание: In this lecture, we learn about the entire data processing pipeline of Large Language Models (LLMs).

In particular, we look at 4 aspects:

(1) Tokenization: Word based, Subword based (BPE tokenizer), Character based
(2) Token embeddings
(3) Positional embeddings
(4) Input embeddings = Token embeddings + Positional embeddings

The key reference book which this video series very closely follows is Build a Large Language Model from Scratch by Manning Publications. All schematics and their descriptions are borrowed from this incredible book!

This book serves as a comprehensive guide to understanding and building large language models, covering key concepts, techniques, and implementations.

Affiliate links for purchasing the book will be added soon. Stay tuned for updates!

0:00 Lecture agenda
3:55 Word based tokenizer
22:19 Special Context Tokens
29:20 Subword and character tokenizers
36:24 Byte Pair Encoder (BPE)
48:54 Dataloader and input-target pairs
01:03:53 Token embeddings
01:22:46 Positional embeddings
01:30:03 Create final input embeddings

Dataset link:
https://github.com/rasbt/LLMs-from-sc...

Google Colab Code: https://drive.google.com/file/d/1WxQo...

Python regular expression library: https://docs.python.org/3/library/re....

OpenAI Tiktoken library: https://github.com/openai/tiktoken

PyTorch Datasets and Dataloaders: https://pytorch.org/tutorials/beginne...

PyTorch embedding layer: https://pytorch.org/docs/stable/gener...
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