@lenml/llama2-tokenizer

⚠️ IMPORTANT UPDATE ⚠️

Our library @lenml/llama2-tokenizer has been deprecated. We are excited to introduce our new library @lenml/tokenizers as its replacement, offering a broader set of features and an enhanced experience.

Why switch to @lenml/tokenizers?

• Fully Compatible with transformers.js Interfaces: Seamlessly supports all interfaces defined in transformers.js, making migration and integration effortless.
• Support for a Wide Range of Models: Regardless of which model you need, our new library supports it, ensuring broader coverage.
• Rich Feature Implementation: Includes a complete implementation of chat templates and normalizers to better serve your text processing and tokenization needs.

check out lenML/tokenizers.

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🦙Llama2 Tokenizer for JavaScript

Llama2 Tokenizer is a TypeScript library for tokenizing and encoding text using the Llama2 vocabulary.

Suitable for browser and nodejs environment.

online playground: https://lenml.github.io/llama-tokenizer-playground/

(vocab: llama2)

Features

• fast
• API like Llama2Tokenizer (python)
• typescript
• 95% test coverage

support models

• llama2
• mistral
• zephyr
• vicuna
• baichuan2
• chatglm3
• internlm2
• yi
• ...

Why llama2 ?

llama2's vocab is different from llama1, so a new tokenizer needs to be defined to adapt to llama2's vocab

Packages

Library Name	Description	Compatibility
@lenml/llama2-tokenizer	Tokenizer library for text segmentation
@lenml/llama2-tokenizer-vocab-llama2	Vocabulary for llama2	mistral, zephyr, vicuna, llama2
@lenml/llama2-tokenizer-vocab-baichuan2	Vocabulary for baichuan2	baichuan2
@lenml/llama2-tokenizer-vocab-chatglm3	Vocabulary for chatglm3	chatglm3
@lenml/llama2-tokenizer-vocab-internlm2	Vocabulary for internlm2	internlm2
@lenml/llama2-tokenizer-vocab-yi	Vocabulary for yi	yi
@lenml/llama2-tokenizer-vocab-falcon	Vocabulary for falcon (🚧WIP)	falcon (🚧WIP)
@lenml/llama2-tokenizer-vocab-neox	Vocabulary for neox (🚧WIP)	neox, RWKV (🚧WIP)
@lenml/llama2-tokenizer-vocab-emoji	a vocab demo (🚧WIP)	🚧WIP

This table lists the name of each library, its description, and its compatibility.

Installation

npm install @lenml/llama2-tokenizer

install vocab

npm install @lenml/llama2-tokenizer-vocab-llama2
# npm install @lenml/llama2-tokenizer-vocab-baichuan2
# npm install @lenml/llama2-tokenizer-vocab-chatglm3
# npm install @lenml/llama2-tokenizer-vocab-falcon
# npm install @lenml/llama2-tokenizer-vocab-internlm2
# npm install @lenml/llama2-tokenizer-vocab-yi

Usage

Importing the Tokenizer

import { Llama2Tokenizer } from "@lenml/llama2-tokenizer";
import { load_vocab } from "@lenml/llama2-tokenizer-vocab-llama2"

Creating an Instance

const tokenizer = new Llama2Tokenizer();
const vocab_model = load_vocab();
tokenizer.install_vocab(vocab_model);

Tokenizing Text

const text = "你好，世界！";
const tokens = tokenizer.tokenize(text);
console.log(tokens);
// Output: ["你", "好", "，", "世", "界", "！"]

Encoding Text

const text = "你好，世界！";
const ids = tokenizer.encode(text);
console.log(ids);
// Output: [2448, 1960, 8021, 1999, 1039, 8013]

Decoding IDs

const ids = [2448, 1960, 8021, 1999, 1039, 8013];
const decodedText = tokenizer.decode(ids);
console.log(decodedText);
// Output: "你好，世界！"

Adding Special Tokens

tokenizer.add_special_token("<ok>");
tokenizer.add_special_tokens(["<|im_start|>", "<|im_end|>"]);

It is not recommended to use [XX] (like [CLS] or [PAD]) as a special token for this pattern, as it can easily lead to conflicts. Because "_[" is also a usable token, it is difficult to be compatible with this bad case without adjusting the word list order.

Getting Vocabulary

const vocabulary = tokenizer.get_vocab();
console.log(vocabulary);
// Output: { "你": 2448, "好": 1960, "，": 8021, "世": 1999, "界": 1039, "！": 8013, ... }

Additional Features

• vocab_size: Get the total vocabulary size.
• max_id: Get the maximum token ID.
• convert_tokens_to_string: Convert a sequence of tokens to a single string.
• convert_tokens_to_ids: Convert a sequence of tokens to a sequence of IDs.
• convert_ids_to_tokens: Convert a sequence of IDs to a sequence of tokens.

Example

import { Llama2Tokenizer } from "@lenml/llama2-tokenizer";
import { load_vocab } from "@lenml/llama2-tokenizer-vocab-llama2"

const main = async () => {
  const tokenizer = new Llama2Tokenizer();
  const vocab_model = load_vocab();
  tokenizer.install_vocab(vocab_model);
  console.log(tokenizer.tokenize("你好，世界！"));
  console.log(tokenizer.encode("你好，世界！"));
  console.log(tokenizer.decode([29383, 29530, 28924, 30050, 29822, 29267]));
};

main();

Benchmark

We conducted a benchmark test to measure the performance of the Llama2 Tokenizer in tokenizing a given text for a specified number of iterations. The results for 1000 iterations are as follows:

Input Text:

Click to expand

🌸🍻🍅🍓🍒🏁🚩🎌🏴🏳️🏳️‍🌈
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Results:

Benchmark Results (1000 iterations):
Total Time: 0.88822 seconds
Average Time per Iteration: 0.00089 seconds

TODOs

• [x] support llama2 vocab
• [x] support chatglm vocab
• [x] support baichuan vocab
• [x] support yi vocab
• [x] support internlm2 vocab
• [ ] support RWKV(neox) vocab
• [ ] support falcon
• [ ] Chat Template

How to build

read this

License

This project is licensed under the MIT License - see the LICENSE file for details.