171 lines
6.5 KiB
Plaintext
171 lines
6.5 KiB
Plaintext
Metadata-Version: 2.1
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Name: tiktoken
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Version: 0.8.0
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Summary: tiktoken is a fast BPE tokeniser for use with OpenAI's models
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Author: Shantanu Jain
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Author-email: shantanu@openai.com
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License: MIT License
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Copyright (c) 2022 OpenAI, Shantanu Jain
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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Project-URL: homepage, https://github.com/openai/tiktoken
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Project-URL: repository, https://github.com/openai/tiktoken
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Project-URL: changelog, https://github.com/openai/tiktoken/blob/main/CHANGELOG.md
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Requires-Python: >=3.9
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Description-Content-Type: text/markdown
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License-File: LICENSE
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Requires-Dist: regex>=2022.1.18
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Requires-Dist: requests>=2.26.0
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Provides-Extra: blobfile
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Requires-Dist: blobfile>=2; extra == "blobfile"
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# ⏳ tiktoken
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tiktoken is a fast [BPE](https://en.wikipedia.org/wiki/Byte_pair_encoding) tokeniser for use with
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OpenAI's models.
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```python
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import tiktoken
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enc = tiktoken.get_encoding("o200k_base")
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assert enc.decode(enc.encode("hello world")) == "hello world"
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# To get the tokeniser corresponding to a specific model in the OpenAI API:
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enc = tiktoken.encoding_for_model("gpt-4o")
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```
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The open source version of `tiktoken` can be installed from PyPI:
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```
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pip install tiktoken
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```
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The tokeniser API is documented in `tiktoken/core.py`.
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Example code using `tiktoken` can be found in the
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[OpenAI Cookbook](https://github.com/openai/openai-cookbook/blob/main/examples/How_to_count_tokens_with_tiktoken.ipynb).
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## Performance
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`tiktoken` is between 3-6x faster than a comparable open source tokeniser:
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Performance measured on 1GB of text using the GPT-2 tokeniser, using `GPT2TokenizerFast` from
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`tokenizers==0.13.2`, `transformers==4.24.0` and `tiktoken==0.2.0`.
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## Getting help
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Please post questions in the [issue tracker](https://github.com/openai/tiktoken/issues).
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If you work at OpenAI, make sure to check the internal documentation or feel free to contact
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@shantanu.
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## What is BPE anyway?
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Language models don't see text like you and I, instead they see a sequence of numbers (known as tokens).
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Byte pair encoding (BPE) is a way of converting text into tokens. It has a couple desirable
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properties:
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1) It's reversible and lossless, so you can convert tokens back into the original text
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2) It works on arbitrary text, even text that is not in the tokeniser's training data
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3) It compresses the text: the token sequence is shorter than the bytes corresponding to the
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original text. On average, in practice, each token corresponds to about 4 bytes.
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4) It attempts to let the model see common subwords. For instance, "ing" is a common subword in
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English, so BPE encodings will often split "encoding" into tokens like "encod" and "ing"
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(instead of e.g. "enc" and "oding"). Because the model will then see the "ing" token again and
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again in different contexts, it helps models generalise and better understand grammar.
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`tiktoken` contains an educational submodule that is friendlier if you want to learn more about
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the details of BPE, including code that helps visualise the BPE procedure:
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```python
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from tiktoken._educational import *
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# Train a BPE tokeniser on a small amount of text
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enc = train_simple_encoding()
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# Visualise how the GPT-4 encoder encodes text
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enc = SimpleBytePairEncoding.from_tiktoken("cl100k_base")
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enc.encode("hello world aaaaaaaaaaaa")
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```
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## Extending tiktoken
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You may wish to extend `tiktoken` to support new encodings. There are two ways to do this.
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**Create your `Encoding` object exactly the way you want and simply pass it around.**
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```python
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cl100k_base = tiktoken.get_encoding("cl100k_base")
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# In production, load the arguments directly instead of accessing private attributes
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# See openai_public.py for examples of arguments for specific encodings
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enc = tiktoken.Encoding(
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# If you're changing the set of special tokens, make sure to use a different name
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# It should be clear from the name what behaviour to expect.
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name="cl100k_im",
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pat_str=cl100k_base._pat_str,
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mergeable_ranks=cl100k_base._mergeable_ranks,
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special_tokens={
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**cl100k_base._special_tokens,
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"<|im_start|>": 100264,
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"<|im_end|>": 100265,
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}
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)
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```
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**Use the `tiktoken_ext` plugin mechanism to register your `Encoding` objects with `tiktoken`.**
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This is only useful if you need `tiktoken.get_encoding` to find your encoding, otherwise prefer
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option 1.
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To do this, you'll need to create a namespace package under `tiktoken_ext`.
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Layout your project like this, making sure to omit the `tiktoken_ext/__init__.py` file:
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```
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my_tiktoken_extension
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├── tiktoken_ext
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│ └── my_encodings.py
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└── setup.py
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```
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`my_encodings.py` should be a module that contains a variable named `ENCODING_CONSTRUCTORS`.
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This is a dictionary from an encoding name to a function that takes no arguments and returns
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arguments that can be passed to `tiktoken.Encoding` to construct that encoding. For an example, see
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`tiktoken_ext/openai_public.py`. For precise details, see `tiktoken/registry.py`.
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Your `setup.py` should look something like this:
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```python
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from setuptools import setup, find_namespace_packages
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setup(
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name="my_tiktoken_extension",
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packages=find_namespace_packages(include=['tiktoken_ext*']),
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install_requires=["tiktoken"],
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...
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)
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```
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Then simply `pip install ./my_tiktoken_extension` and you should be able to use your
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custom encodings! Make sure **not** to use an editable install.
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