tiktoken

# ⏳ tiktoken

tiktoken is a fast [BPE](https://en.wikipedia.org/wiki/Byte_pair_encoding) tokeniser for use with
OpenAI's models.

```python
import tiktoken
enc = tiktoken.get_encoding("o200k_base")
assert enc.decode(enc.encode("hello world")) == "hello world"

# To get the tokeniser corresponding to a specific model in the OpenAI API:
enc = tiktoken.encoding_for_model("gpt-4o")
```

The open source version of `tiktoken` can be installed from [PyPI](https://pypi.org/project/tiktoken):
```
pip install tiktoken
```

The tokeniser API is documented in `tiktoken/core.py`.

Example code using `tiktoken` can be found in the
[OpenAI Cookbook](https://github.com/openai/openai-cookbook/blob/main/examples/How_to_count_tokens_with_tiktoken.ipynb).


## Performance

`tiktoken` is between 3-6x faster than a comparable open source tokeniser:

![image](https://raw.githubusercontent.com/openai/tiktoken/main/perf.svg)

Performance measured on 1GB of text using the GPT-2 tokeniser, using `GPT2TokenizerFast` from
`tokenizers==0.13.2`, `transformers==4.24.0` and `tiktoken==0.2.0`.


## Getting help

Please post questions in the [issue tracker](https://github.com/openai/tiktoken/issues).

If you work at OpenAI, make sure to check the internal documentation or feel free to contact
@shantanu.

## What is BPE anyway?

Language models don't see text like you and I, instead they see a sequence of numbers (known as tokens).
Byte pair encoding (BPE) is a way of converting text into tokens. It has a couple desirable
properties:
1) It's reversible and lossless, so you can convert tokens back into the original text
2) It works on arbitrary text, even text that is not in the tokeniser's training data
3) It compresses the text: the token sequence is shorter than the bytes corresponding to the
   original text. On average, in practice, each token corresponds to about 4 bytes.
4) It attempts to let the model see common subwords. For instance, "ing" is a common subword in
   English, so BPE encodings will often split "encoding" into tokens like "encod" and "ing"
   (instead of e.g. "enc" and "oding"). Because the model will then see the "ing" token again and
   again in different contexts, it helps models generalise and better understand grammar.

`tiktoken` contains an educational submodule that is friendlier if you want to learn more about
the details of BPE, including code that helps visualise the BPE procedure:
```python
from tiktoken._educational import *

# Train a BPE tokeniser on a small amount of text
enc = train_simple_encoding()

# Visualise how the GPT-4 encoder encodes text
enc = SimpleBytePairEncoding.from_tiktoken("cl100k_base")
enc.encode("hello world aaaaaaaaaaaa")
```


## Extending tiktoken

You may wish to extend `tiktoken` to support new encodings. There are two ways to do this.


**Create your `Encoding` object exactly the way you want and simply pass it around.**

```python
cl100k_base = tiktoken.get_encoding("cl100k_base")

# In production, load the arguments