How Do LLMs Like Llama Match Token Numbers to Words?

When exploring Large Language Models (LLMs) like Llama, a common question arises: How exactly does the model know what each numeric token represents in terms of actual words? Let's break down this fascinating aspect of language models.

What's a Token, Anyway?

Tokens are numeric representations of words or parts of words used by language models. Instead of processing plain text directly, models convert sentences into sequences of numbers for efficient processing. Every word or subword is assigned a unique numeric identifier, called a token.

Where Does Llama Store This Mapping?

When you download an open-source model like Llama, the relationship between tokens and actual words is stored explicitly in a file named tokenizer.model. This file comes packaged alongside the model's weights and configuration files.

A typical directory structure looks like this:

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llama/
├── tokenizer.model     # Token mapping stored here
├── params.json
└── model_weights/
    ├── ...

This tokenizer file isn't plain text—it's stored in a binary format, commonly using SentencePiece, a popular tokenization system.

How Can You View the Token Mapping?

You can quickly access the token-to-word mapping by loading the tokenizer programmatically. Here's a straightforward method using Python and SentencePiece:

Quick Python Example:

First, install the library:

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pip install sentencepiece

Then, load the tokenizer and view tokens:

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import sentencepiece as spm

# Load the tokenizer
sp = spm.SentencePieceProcessor()
sp.load('tokenizer.model')

# Display mappings for the first 10 tokens
for token_id in range(10):
    token_text = sp.id_to_piece(token_id)
    print(f"Token {token_id}: '{token_text}'")

Running this script will print something similar to:

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Token 0: '<unk>'
Token 1: '<s>'
Token 2: '</s>'
Token 3: '▁the'
Token 4: '▁to'
Token 5: '▁and'
...

Using Hugging Face to Explore Tokens

If you're accessing Llama through Hugging Face, you have another simple way to explore tokens:

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from transformers import LlamaTokenizer

# Load tokenizer from Hugging Face
tokenizer = LlamaTokenizer.from_pretrained('meta-llama/Llama-2-7b')

# Get ID of a word
token_id = tokenizer.convert_tokens_to_ids('the')
print(f"Token ID for 'the': {token_id}")

# Retrieve word by token ID
token_word = tokenizer.convert_ids_to_tokens(42)
print(f"Token word for ID 42: '{token_word}'")

Why is Token Mapping Stored Separately?

Token mapping files are separate because the mapping doesn't change frequently after the model is trained. This separation simplifies model deployment, ensures consistency across various implementations, and makes customization easier.

The numeric-token-to-word relationship is stored explicitly in tokenizer files like tokenizer.model, making it easy for anyone to explore how models like Llama interpret and generate language. Next time you work with an open-source model, you'll know exactly where and how to find this critical information!