gpt 4

Introduction

The GPT-4 Tokenizer by Xenova is a Hugging Face-compatible version adapted from OpenAI's tiktoken. It integrates seamlessly with Hugging Face libraries like Transformers, Tokenizers, and Transformers.js, allowing for versatile text processing and encoding capabilities.

Architecture

This tokenizer is designed to work efficiently with Hugging Face's Transformers ecosystem. It leverages the pre-trained model weights from the GPT2TokenizerFast library for Python and AutoTokenizer for JavaScript, providing accurate and fast tokenization.

Training

The GPT-4 Tokenizer is pre-trained and ready to use with the GPT2TokenizerFast library in Python and AutoTokenizer in JavaScript. Its training was adapted from OpenAI's tiktoken, ensuring compatibility with a wide range of applications requiring natural language processing.

Guide: Running Locally

To use the GPT-4 Tokenizer locally, follow these basic steps:

For Python

1. Install the transformers library:

   pip install transformers
   

2. Use the tokenizer in your Python code:

   from transformers import GPT2TokenizerFast
   
   tokenizer = GPT2TokenizerFast.from_pretrained('Xenova/gpt-4')
   assert tokenizer.encode('hello world') == [15339, 1917]
   

For JavaScript

1. Install transformers.js:

   npm install @xenova/transformers
   

2. Use the tokenizer in your JavaScript code:

   import { AutoTokenizer } from '@xenova/transformers';
   
   const tokenizer = await AutoTokenizer.from_pretrained('Xenova/gpt-4');
   const tokens = tokenizer.encode('hello world'); // [15339, 1917]
   

Suggestion

For optimal performance, especially for large-scale processing, consider using cloud GPUs from providers like AWS, GCP, or Azure.

License

The GPT-4 Tokenizer is distributed under the same license as the Hugging Face libraries, ensuring it is free to use and modify within the constraints of open-source licensing agreements.