The Essential Guide to Tokenization for Large Language Models

Published Feb 22, 202413 min read

Why Tokenization Matters

Think of tokenization as the process of translating text into the language that large language models (LLMs) understand — numbers. Just like we need special tools to translate between different languages, tokenization is the bridge between human language and AI. Unfortunately, this process is complex and can be a major source of weirdness and limitations in LLMs.

Key Ideas

  • Tokens: The fundamental building blocks of language for LLMs. They can be whole words, parts of words, or even single characters.
  • Tokenization as Translation: Imagine tokenization as turning a sentence into a series of numerical codes that a computer can process.
  • Why it's tricky: Different languages, special characters, and even things like spaces or punctuation can create issues.

Tokenization Challenges (and Why You Should Care)

If you've ever experienced any of these LLM oddities, the culprit is likely poor tokenization:

  • Trouble with basic spelling or arithmetic
  • Difficulty handling non-English languages
  • Confusing behavior when presented with code
  • Strange tangents and meltdowns triggered by seemingly normal topics

The way we tokenize text has a massive impact on what an LLM can and cannot do well.

Tokenization by Example

Let's break it down with some practical examples using the Tik Tokenizer web app:

  • Simple Sentences: "Hello world" might be split into multiple tokens, including spaces.
  • Arithmetic: Tokenization may split "127 + 677" into several separate tokens, making it difficult for the LLM to process the calculation.
  • Case Sensitivity: "Egg" and "egg" could be completely different tokens, forcing the LLM to figure out they mean the same thing.
  • *Non-English Languages: Tokenization often creates longer sequences for non-English languages, making things harder for the AI.

Diving into Code: Tokenizing Python

  • Wasteful Whitespace: Indentation in Python can be tokenized as individual spaces, bloating up sequences and making code harder for the LLM to understand. Newe tokenizers are getting better at this!

The Solution: Byte Pair Encoding (BPE)

BPE is the algorithm behind most modern tokenizers. It works by finding common pairs of bytes in your training data and compressing them into single tokens. This creates a balance between vocabulary size and efficiency, allowing LLMs to process text more effectively.

Let's Get Practical: Building Your Own BPE Tokenizer

We won't get into the nitty-gritty code here, but the process is surprisingly straightforward:

  1. Gather Text Data: The more, the better! This shapes your vocabulary.
  2. Encode with UTF-8: Transform the text into raw bytes.
  3. Train the BPE Algorithm: It'll learn the common pairs to compress.
  4. Create a Vocabulary: The list of all possible tokens.

Key Takeaways

  • Tokenization is an often overlooked but critical part of working with LLMs.
  • A well-designed tokenizer balances vocabulary size, efficiency, and the ability to handle different languages and text types.
  • Understanding tokenization helps you debug weird LLM behavior and make smarter choices when working with language models.

Mastering Tokenization: The Key to Understanding LLMs

Decoding the BPE Algorithm

The Byte Pair Encoding algorithm might seem complex, but it follows a simple idea:

  • Start with Raw Bytes: A 256-character vocabulary representing individual bytes.
  • Find Common Pairs: Identify the most frequent consecutive pairs of bytes in your text data.
  • Compress: Replace the common pairs with a new single token (assigned a new number) to shorten the sequence.
  • Repeat: Find the next most common pair, compress, and repeat until you reach your desired vocabulary size.

Example Imagine you start with the sequence "AAABCD".

  1. Iteration 1: 'AA' is most common. Replace all 'AA' with new token 'Z'. Sequence becomes "ZABCD".
  2. Iteration 2: 'AB' is most common. Replace all 'AB' with 'Y'. Sequence becomes "ZYCD".

Why This Matters

BPE lets us balance vocabulary size with efficiency. Here's the impact:

  • Efficient Sequences: Compressed text means shorter sequences for the LLM to process, making training and prediction faster.
  • Multilingual Support: BPE helps LLMs represent non-English languages better, improving performance across the board.

Training Your Tokenizer

  1. Gather Text: The more data, the better. This shapes your vocabulary.
  2. Encode with UTF-8: Transform raw text into bytes.
  3. Train the BPE Algorithm: It learns how to compress your data.
  4. Create a Vocabulary: The final list of all possible tokens.

Encoding and Decoding

Once we have a tokenizer, we can convert between text and the tokens LLMs understand.

  • Decoding: Takes tokens and gives you back the original text. This is how we understand the LLM's output.
  • Encoding: Prepares text to be fed into the LLM.

Key Takeaways

  • Tokenization and the BPE algorithm are not as scary as they sound! It's just clever compression.
  • A well-crafted tokenizer is essential for LLM efficiency and language understanding.
  • This knowledge helps you avoid weird errors and make better decisions when using LLMs.

Mastering Tokenization: Part 3

Beyond Byte Pair Encoding

We've covered the core of BPE, but real-world tokenizers used in advanced language models are more sophisticated. Let's dive into how GPT-2 and GPT-4 tokenizers enhance the basic algorithm.

Regular Expressions: Enforcing Structure

Naive BPE can create weird token combinations, like "dog." or "world!". GPT-2 addresses this with regular expressions (regex) to split text into chunks before tokenization.

  • The Pattern: A complex regex pattern splits text based on categories:

    • Letters
    • Numbers
    • Punctuation
    • Whitespace

  • Why It Matters: This prevents merging across categories (e.g., letters and punctuation), making tokens more meaningful.

  • Caveats: This approach is language-specific and can be inconsistent (e.g., handling different apostrophes or case-sensitivity).

GPT-4 and TikTokenizer

The TikTokenizer library from OpenAI provides official GPT-2 and GPT-4 tokenizers. Key changes in GPT-4 include:

  • Case-Insensitive Regex: Makes tokenization more consistent across different capitalization styles.
  • Number Handling: Limits merging of numbers to those with three digits or fewer to avoid overly long tokens.
  • Vocabulary Size Increase: Roughly doubled to 100K tokens for more efficient representation.

Special Tokens

Tokenizers add special tokens alongside those from BPE. These tokens have specific purposes:

  • End of Text (<|endoftext|>): Separates documents in training data and signals to the LLM that a new, unrelated piece of text is starting.

Understanding the Code

OpenAI released the GPT-2 tokenizer code (encoder.py). While the implementation has some quirks, the core ideas are identical to the BPE algorithm we've covered.

Key Takeaways

  • Advanced tokenizers add structure and special tokens to improve the meaningfulness of the token sequences.
  • Understanding these patterns will help you interpret LLM outputs and make better use of them.
  • We still don't have all the answers, as OpenAI hasn't fully documented their design choices.

Let me know if you want to dive deeper into the regex patterns themselves, or see even more examples of special tokens!

Absolutely! Here's a breakdown of the remaining sections, continuing the style of James Clear and providing a new focus on the practical implications for using LLMs effectively.

Demystifying Tokenization

Let's revisit those LLM quirks and break them down:

  • Why LLMs Struggle with Spelling and Arithmetic: It's all about the tokens. When we tokenize text, we're breaking it into chunks that optimize for efficient data handling, not necessarily accurate representation of words or numbers. Each token is treated independently by the LLM, leading to potential errors with tasks that depend on precise character sequences.

  • Non-English Languages and Tokenization: The more a language differs from English (and the datasets LLMs are primarily trained on), the more likely it is to be heavily broken up by the tokenizer. This can impact performance significantly, as smaller chunks provide less context for the LLM to work with.

  • Code and Python Woes: Indentation, a core part of Python syntax, is often tokenized as individual spaces. This disrupts the structure of the code, making it impossible for the LLM to grasp its meaning.

  • Those "Weird" Warnings: Tokenizers often split words on punctuation or other symbols. If you get a warning about trailing whitespace, it means the tokenizer is treating those "invisible" spaces as their own tokens. This might have no apparent consequence, or it could derail your output.

Key Takeaways

  • Tokenization strikes a balance between efficiency and information loss.
  • Tokenizers are language-dataset dependent, impacting non-English language performance.
  • Consider a specialized tokenizer for code or certain language types for better LLM handling of those tasks.

Beyond BPE: Exploring SentencePiece

SentencePiece is another popular tokenizer library. Here's what makes it different:

  • It Works on Code Points, Not Bytes: Instead of the BPE's byte-level operations, SentencePiece uses Unicode code points for merging. This provides advantages for multilingual or symbol-heavy use cases.
  • Fallback to Bytes: For rare or unmappable code points, it falls back to byte-level encoding, ensuring nothing gets lost.
  • Historical Clutter: SentencePiece carries some historical baggage in its design, which can be confusing, making clear documentation even more essential.

Choosing and Configuring Your Tokenizer: Your Path to LLM Success

The "right" tokenizer depends heavily on your goals:

  • Vocabulary Size: This fundamental choice impacts several factors:
    • Larger vocabularies: More nuanced distinctions, but longer sequences and harder to train well.
    • Smaller vocabularies: Faster, but less fine-grained results.
  • Special Tokens: Introduce these strategically for specific tasks like chat or fine-tuning. Remember, they'll increase model size.
  • Multilingual? Consider the trade-offs between standard vocabulary sizes and those tailored for your target languages.

Practical Tips

  • Experiment! Tokenization is not a one-size-fits-all problem.
  • Use Available Tools: TikTokenizer or libraries like SentencePiece will save you from building a tokenizer from scratch.
  • Inspect Your Outputs: Understand how your tokenizer works to troubleshoot issues and interpret LLM results.

Absolutely! Here's another pass through your final sections, in the style of James Clear with a strong emphasis on how understanding tokenization empowers you to use LLMs skillfully and avoid their pitfalls:

The Trouble with Tokenization

Tokenization might be dry and technical, but it's a force you need to reckon with when working with LLMs. Let's recap some key takeaways:

  • The Battle Against Jargon: Tokenization isn't intuitive. It creates its own universe of terms and logic, leaving users feeling lost and frustrated. Don't lose heart! This guide breaks down the jargon, allowing you to decode LLM outputs.
  • The Price of Efficiency: Tokenization is a necessary compromise. We trade some information loss for compression and computational efficiency. This delicate balance can lead to unexpected and sometimes humorous LLM failures.
  • The Power and the Pitfalls: Once you understand the inner workings of tokenization, you gain a deeper understanding of LLM behavior. You'll anticipate weird errors, explain seemingly random outputs, and make informed decisions about when to trust an LLM's response.

Practical Tips: Tools and Tactics to Master LLMs

Let's translate these insights into action!

  • Choose Your Tools Wisely:
    • For existing vocabularies, TikTokenizer is a winner – fast, efficient, and widely compatible.
    • When building your own tokenizer, prioritize clarity over historical complexity. SentencePiece works but demands extra caution.
    • The dream? A tool as seamless as TikTokenizer but with robust training capabilities – the best of both worlds!
  • Expect the Unexpected:
    • Simple spelling tasks? Arithmetic? Code comprehension? Remember, LLMs work on tokens, not the way you intuitively think. Be prepared for weird results.
    • Tokenization impacts performance on non-English languages significantly. Factor this into your expectations.
    • Be wary of those "trigger tokens" lurking in training data that can throw your LLM into a tailspin.
  • Embrace the Efficiency Ethos:
    • Think about the "token cost" of everything. Is there a more token-efficient way to encode your data (think YAML vs. JSON)? Can you streamline your prompts? Efficiency translates to better results and lower costs.

The Tokenization Mindset

Understanding tokenization is not an endpoint—it's a foundational mindset that will shape how you approach LLMs. It's about:

  • Decoding Errors: When something goes haywire, your first question should be "How is this tokenized?"
  • Embracing the Limitations: LLMs aren't magic; they are sophisticated pattern-matching machines with a tokenized worldview. Respect the constraints of this process.
  • Staying Ahead: Tokenization is a dynamic field. New research and tools will emerge. Keep learning to harness the full potential of LLMs.

Let me know if you'd like to explore a particular aspect of LLM errors or troubleshooting with this tokenization knowledge as your weapon!

Of course! Here's a breakdown of the final part, focused on the GPT-2 encoder code and its mysterious "spurious" layer. I'll aim for the clarity of James Clear and sprinkle in some intrigue about the inner workings of these LLMs.

Decoding the GPT-2 Encoder

Let's dissect that code snippet from OpenAI's GPT-2 encoder. While the exact purpose of the "spurious" layer might be a bit of a puzzle (even you find it so!), we can definitely break down the core mechanics of the code:

Pieces of the Puzzle:

  • Context Length: GPT-1 uses 512 tokens, upgraded to 1024 in later versions. This determines how much text the model can "remember" at a time. More is generally better, but it comes with computational costs.
  • The Encoding Process: The core functions (encode, decode, etc.) are all about the magic of BPE. They take your text, break it into tokens, and translate them into usable sequences for the LLM.
  • Byte Level Operations: Notice those byte_encoder and byte_decoder functions. This bit is a workaround. BPE works great most of the time, but what if a word is super rare and not in the vocabulary? That's where byte encoding steps in as a fallback.

The Mystery of the "Spurious" Layer

Now, what's the deal with that extra layer OpenAI adds? It could be a few things:

  • Compatibility: Maybe they're doing some preprocessing or cleanup to make the output compatible with a specific part of their LLM pipeline.
  • Legacy Holdover: It's possible this layer solved a problem in older versions of GPT or with a specific training dataset. Now, it just sticks around as harmless historical baggage.
  • Intentional Obfuscation: Less likely, but they could be adding a bit of "secret sauce" to make it harder for others to replicate their exact tokenizer.

The Big Takeaway

Even if we don't solve the mystery entirely, this code snippet highlights a key LLM truth:

  • It's Not Magic, It's Code: Under the hood, even the most cutting-edge LLMs are built from layers of code like this. Understanding those layers is key to interpreting (and fixing!) their quirky outputs.
  • The Human Touch: The choices made in these lines of codes – the use of byte encoding, the mystery layer – demonstrate that even LLMs with astronomical parameters are shaped by human decisions and biases.

Let's Dig Deeper!

Do you want to try unpacking the encode and decode functions in more detail? Or maybe we could go on a tangent about why those context windows are so important? Let me know your next area of curiosity!

Acknowledgment

This article is based on the great video from Andrej Karpathy and this tweet https://www.youtube.com/watch?v=zduSFxRajkE