README.md

---
library_name: transformers
tags:
 - transformers.js
 - tokenizers
---

## Why should you use this and not the titotken included in the orignal model?
Original tokenizer pad vocabulary to correct size with `<extra_N>` tokens but encoder never uses them causing inconsistency and deterimental to training code that may want to use the unused `<extra_N>` tokens.

modified from original code @ https://huggingface.co/Xenova/dbrx-instruct-tokenizer 

# DBRX Instruct Tokenizer

A 🤗-compatible version of the **DBRX Instruct** (adapted from [databricks/dbrx-instruct](https://huggingface.co/databricks/dbrx-instruct)). This means it can be used with Hugging Face libraries including [Transformers](https://github.com/huggingface/transformers), [Tokenizers](https://github.com/huggingface/tokenizers), and [Transformers.js](https://github.com/xenova/transformers.js).

## Example usage:

### Transformers/Tokenizers
```py
from transformers import GPT2TokenizerFast

tokenizer = GPT2TokenizerFast.from_pretrained('Xenova/dbrx-instruct-tokenizer')
assert tokenizer.encode('hello world') == [15339, 1917]
```

### Transformers.js
```js
import { AutoTokenizer } from '@xenova/transformers';

const tokenizer = await AutoTokenizer.from_pretrained('Xenova/dbrx-instruct-tokenizer');
const tokens = tokenizer.encode('hello world'); // [15339, 1917]
```