Tokenization by Andrej Karpathy
In a recent YouTube tutorial, Andrej Karpathy—the wizard behind Tesla’s Autopilot and OpenAI’s GPT—unveiled the secrets of tokenization. Buckle up, tech-savvy professionals, because this isn’t your run-of-the-mill theoretical lecture. It’s a hands-on journey into the heart of language models.
So, what’s the deal with tokenization? Imagine it as the backstage choreographer for Large Language Models (LLMs). It translates between human-readable strings and the cryptic tokens that LLMs munch on. In this tutorial, we’re not just peeking behind the curtain; we’re building our own tokenizer from scratch.
Here’s the lowdown:
- Tokenizer’s Role: It’s the bridge between strings and tokens. Think of it as the Rosetta Stone for AI. We’ll delve into encoding strings, decoding tokens, and even throw in some Byte Pair Encoding (BPE) magic.
- Implementation Details: We’ll roll up our sleeves and count pairs, merge tokens, and manage vocabularies. It’s like assembling IKEA furniture, but for language nerds.
- Tokenization Issues: Brace yourself for quirks. We’ll explore regex differences between GPT-2 and GPT-4, and how tokenization impacts prompt compression. Spoiler alert: Tokenization isn’t always rainbows and unicorns.
- Alternatives: Meet SentencePiece, the cool kid on the block. Plus, we’ll dip our toes into multimodal tokenization with vector quantization. Because why stop at text when you can tokenize images, videos, and audio too?
So grab your code editor, channel your inner Karpathy, and let’s build the GPT Tokenizer. Your next NLP project will thank you!
Keep also in mind Andrej’s repo minbpe. A lightweight Python library offering clean, educational code for the Byte Pair Encoding (BPE) algorithm. Commonly used in large language model tokenization, BPE efficiently breaks down text into smaller units. This repository provides a basic, easy-to-understand implementation ideal for learning or integrating BPE into your NLP projects.
