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https://github.com/openai/tiktoken

tiktoken is a fast BPE tokeniser for use with OpenAI's models.
https://github.com/openai/tiktoken

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tiktoken is a fast BPE tokeniser for use with OpenAI's models.

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# ⏳ 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:

```

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 directly instead of accessing private attributes

# See openai_public.py for examples of arguments for specific encodings

enc = tiktoken.Encoding(

    # If you're changing the set of special tokens, make sure to use a different name

    # It should be clear from the name what behaviour to expect.

    name="cl100k_im",

    pat_str=cl100k_base._pat_str,

    mergeable_ranks=cl100k_base._mergeable_ranks,

    special_tokens={

        **cl100k_base._special_tokens,

        "<|im_start|>": 100264,

        "<|im_end|>": 100265,

    }

)

```



**Use the `tiktoken_ext` plugin mechanism to register your `Encoding` objects with `tiktoken`.**



This is only useful if you need `tiktoken.get_encoding` to find your encoding, otherwise prefer

option 1.



To do this, you'll need to create a namespace package under `tiktoken_ext`.



Layout your project like this, making sure to omit the `tiktoken_ext/__init__.py` file:

```

my_tiktoken_extension

├── tiktoken_ext

│   └── my_encodings.py

└── setup.py

```



`my_encodings.py` should be a module that contains a variable named `ENCODING_CONSTRUCTORS`.

This is a dictionary from an encoding name to a function that takes no arguments and returns

arguments that can be passed to `tiktoken.Encoding` to construct that encoding. For an example, see

`tiktoken_ext/openai_public.py`. For precise details, see `tiktoken/registry.py`.



Your `setup.py` should look something like this:

```python

from setuptools import setup, find_namespace_packages



setup(

    name="my_tiktoken_extension",

    packages=find_namespace_packages(include=['tiktoken_ext*']),

    install_requires=["tiktoken"],

    ...

)

```



Then simply `pip install ./my_tiktoken_extension` and you should be able to use your

custom encodings! Make sure **not** to use an editable install.