The article also provides usage examples for the `BasicTokenizer` and `GPT4Tokenizer`, demonstrating how to train, encode, and decode text. It also mentions that the repository includes a script, train.py, that trains the two major tokenizers on a specific input text and saves the vocabulary to disk for visualization. The article concludes by mentioning future improvements, such as writing more optimized Python, C, or Rust versions, renaming `GPT4Tokenizer` to `GPTTokenizer`, handling special tokens, and creating a `LlamaTokenizer`. The repository is licensed under MIT.
Key takeaways:
- The repository contains minimal and clean code for the Byte Pair Encoding (BPE) algorithm used in Language Model (LLM) tokenization, popularized by the GPT-2 paper from OpenAI.
- There are two Tokenizers in this repository, which can perform the primary functions of a Tokenizer: training the tokenizer vocabulary and merges on a given text, encoding from text to tokens, and decoding from tokens to text.
- The repository includes different implementations of the BPE algorithm, including BasicTokenizer, RegexTokenizer, and GPT4Tokenizer, each with unique features and uses.
- The code is thoroughly commented, and usage examples are provided at the bottom of each file. The repository also includes tests using the pytest library, and future improvements are planned, such as optimization and handling of special tokens.