Let’s Build the GPT Tokenizer: A Complete Guide to Tokenization in LLMs
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Build a complete guide to tokenization in LLMs based on Andrej Karpathy's video
Action Steps
- Watch Andrej Karpathy's 2h13m tokenizer video
- Read the translated book chapter on tokenization
- Implement key pieces of code from the chapter
- Experiment with different tokenization techniques for LLMs
Who Needs to Know This
NLP engineers and researchers on a team can benefit from understanding tokenization in LLMs to improve model performance and efficiency. This knowledge can also be useful for AI engineers and ML researchers working with language models.
Key Insight
💡 Tokenization is a key component of how LLMs work
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📚 Build your own GPT tokenizer with this complete guide!
Key Takeaways
Build a complete guide to tokenization in LLMs based on Andrej Karpathy's video
Full Article
18 months ago, Andrej Karpathy set a challenge : “Can you take my 2h13m tokenizer video and translate the video into the format of a book chapter”. We’ve done it, and the chapter is below, including key pieces of code inlined, and images from the video at key points (hyperlinked to the video timestamp). It’s a great video for learning this key piece of how LLMs work, and this new text version is great too.
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