Tokenizer for OpenAI large language models.
The "@microsoft/tiktokenizer" npm package is a specialized tool designed to enhance the functionality and performance of large language models (LLMs) powered by OpenAI. As an implementation of the byte pair encoding (BPE) tokenizer, this module is essential for developers working with natural language processing and machine learning applications. It efficiently processes text by breaking it down into manageable tokens, which are crucial for the training and operation of language models. The package supports both Typescript and C# programming languages, offering a versatile toolkit that aligns with the open-sourced Rust implementation by OpenAI. This ensures that users benefit from a robust, community-vetted foundation for their language processing tasks.
To start using this powerful tokenizer, developers need only execute the command "npm install @microsoft/tiktokenizer" in their project environment. This simple installation process integrates the tokenizer into their existing projects, allowing for the rapid deployment of advanced text processing capabilities. Once installed, the tokenizer empowers developers to handle large volumes of text data more efficiently, enhancing the accuracy and responsiveness of their language models. The compatibility with multiple programming languages and adherence to OpenAI's established BPE methodology make it an indispensable tool for professionals aiming to leverage the full potential of cutting-edge LLMs.
The benefits of using "@microsoft/tiktokenizer" extend beyond its primary tokenization functionality. It also ensures that text data is processed in a way that maximizes the performance and scalability of OpenAI’s language models. This optimization is critical in applications requiring high levels of linguistic accuracy and nuance, such as AI-driven content creation, automated customer support, and sophisticated analytical tools. By streamlining the preprocessing phase of model training, the "@microsoft/tiktokenizer" package significantly reduces the time and resources needed to develop and deploy powerful language-based solutions, providing a competitive edge in various tech-driven industries.
A README file for the @microsoft/tiktokenizer code repository. View Code
This repo contains Typescript and C# implementation of byte pair encoding(BPE) tokenizer for OpenAI LLMs, it's based on open sourced rust implementation in the OpenAI tiktoken. Both implementation are valuable to run prompt tokenization in Nodejs and .NET environment before feeding prompt into a LLM.
Please follow README.
[!IMPORTANT] Users of
Microsoft.DeepDev.TokenizerLib
should migrate toMicrosoft.ML.Tokenizers
. The functionality inMicrosoft.DeepDev.TokenizerLib
has been added toMicrosoft.ML.Tokenizers
.Microsoft.ML.Tokenizers
is a tokenizer library being developed by the .NET team and going forward, the central place for tokenizer development in .NET. By usingMicrosoft.ML.Tokenizers
, you should see improved performance over existing tokenizer library implementations, includingMicrosoft.DeepDev.TokenizerLib
. A stable release ofMicrosoft.ML.Tokenizers
is expected alongside the .NET 9.0 release (November 2024). Instructions for migration can be found at https://github.com/dotnet/machinelearning/blob/main/docs/code/microsoft-ml-tokenizers-migration-guide.md.
We welcome contributions. Please follow this guideline.
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