Introduction
When working with Large Language Models (LLMs), developers frequently encounter tokenizers—AutoTokenizer, tiktoken, SentencePiece, or WordPiece. A natural question arises:
Why isn’t there a standalone “tokenization model,” like there are embedding and transformer models?
This article explains why tokenization is not treated as a neural model, how tokenizers actually work, and why they are always tightly coupled with language models.
What Tokenization Really Is
Tokenization is the process of converting raw text into discrete units called tokens, which can be:
Example:
"I am learning NLP"
→ ["I", "am", "learn", "ing", "NLP"]
These tokens are later mapped to token IDs, which are numerical indices used by neural networks.
Why Tokenization Is Not a Neural Model
Tokenization Is Deterministic, Not Predictive
A neural model:
Learns patterns
Makes predictions
Produces probabilities
A tokenizer:
Input: "ChatGPT"
Output tokens: always the same
There is no inference, no gradients, and no learning during runtime.
Tokenizers Are Algorithms + Lookup Tables
Modern tokenizers are built using algorithms such as:
| Algorithm | Used By |
|---|
| BPE (Byte Pair Encoding) | GPT, RoBERTa |
| WordPiece | BERT |
| SentencePiece | T5, LLaMA |
| Byte-level BPE | GPT-2 |
These consist of:
A merge rules file
A vocabulary file
A normalization pipeline
This is fundamentally different from neural network weights.
Tokenization Happens Before the Model
LLM pipelines look like this:
Text- Tokenizer (rules + vocab)- Token IDs- Embedding Layer- Transformer Layers- Output
The tokenizer is outside the neural network graph.
Since it doesn’t participate in training or inference, it isn’t treated as a "model".
Why Tokenizers Are Bundled with Models
Each LLM is trained using a specific tokenizer.
Token ID Dependency
If a model expects:
"hello" → token ID 15496
and you use a different tokenizer that produces:
"hello" → token ID 2091
The model breaks.
That’s why tokenizers are inseparable from their models.
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("gpt2")
You never download a tokenizer independently because:
Why We Can Train Tokenizers but Still Don’t Call Them Models
You can train a tokenizer:
from tokenizers import Tokenizer, models, trainers
tokenizer = Tokenizer(models.BPE())
trainer = trainers.BpeTrainer(vocab_size=5000)
But this training:
It simply builds a compressed vocabulary based on frequency.
Thus, it’s considered preprocessing, not modeling.
Role of AutoTokenizer
AutoTokenizer is a factory, not a model.
AutoTokenizer.from_pretrained("bert-base-uncased")
What it does:
Reads tokenizer configuration
Loads correct algorithm (BPE, WordPiece, etc.)
Loads vocabulary and merges
Applies normalization rules
It abstracts tokenizer differences, so developers don’t have to manage them manually.
Why Embedding Models Exist but Tokenization Models Don’t
| Feature | Tokenizer | Embedding Model |
|---|
| Learns from data | No | Yes |
| Uses neural network | No | Yes |
| Trainable with loss | No | Yes |
| Produces vectors | No | Yes |
| Deterministic | Yes | No |
Embedding models learn semantic meaning, which requires neural networks.
Tokenization does not.
Key Takeaways
Tokenization is not a neural task
It is deterministic preprocessing
Tokenizers are algorithm + vocabulary, not models
They are tightly coupled to LLMs
AutoTokenizer exists for convenience, not learning
Final Thought
Tokenization is to LLMs what a compiler is to a program It prepares the input—but it doesn’t think.
That’s why there is no standalone tokenization-only model in modern NLP.