@lenml/tokenizers

This is the central repository for the @lenml/tokenizers project, which provides tokenization libraries for various machine learning models.

this repo fork from huggingface/transformers.js

Tokenizer Arena / Playground

Explore our Tokenizer Arena / Playground! This interactive platform allows you to utilize various tokenizers from our @lenml/tokenizers library. Easily load and compare different tokenizers, seeing how they perform with diverse text inputs. Whether you're a professional developer or a machine learning enthusiast, this playground is perfect for gaining insights into the tokenization process of different models and experimenting with their functionalities.

click to arena page

When should I use this instead of transformers.js?

Firstly, the interface and the actual code of the Tokenizer object are completely identical to those in transformers.js. However, when loading a tokenizer with this library, you're allowed to create your model directly from a JSON object without the need for internet access, and without relying on Hugging Face (hf) servers, or local files.

Therefore, this library becomes more convenient when you need to operate offline and only require the use of a tokenizer without the need for ONNX models.

Packages

Below is a table showcasing all available packages, the models they support, and their respective locations within the repository:

Package Name	Supported Model(s)	Repository Link	NPM Page
tokenizers (core)	N/A (Core Tokenization Library)	@lenml/tokenizers
llama3_1	Llama 3.1	@lenml/tokenizer-llama3_1
llama2	Llama 2 (mistral, zephyr, vicuna)	@lenml/tokenizer-llama2
llama3	Llama 3	@lenml/tokenizer-llama3
gpt4o	GPT-4o	@lenml/tokenizer-gpt4o
gpt4	GPT-4	@lenml/tokenizer-gpt4
gpt35turbo	GPT-3.5 Turbo	@lenml/tokenizer-gpt35turbo
gpt35turbo16k	GPT-3.5 Turbo 16k	@lenml/tokenizer-gpt35turbo16k
gpt3	GPT-3	@lenml/tokenizer-gpt3
gemma	Gemma	@lenml/tokenizer-gemma
claude	Claude 2/2.1/2.5/3/3.5	@lenml/tokenizer-claude
claude1	Claude 1	@lenml/tokenizer-claude1
gpt2	GPT-2	@lenml/tokenizer-gpt2
baichuan2	Baichuan 2	@lenml/tokenizer-baichuan2
chatglm3	ChatGLM 3	@lenml/tokenizer-chatglm3
command_r_plus	Command-R-Plus	@lenml/tokenizer-command_r_plus
internlm2	InternLM 2	@lenml/tokenizer-internlm2
qwen1_5	Qwen 1.5	@lenml/tokenizer-qwen1_5
yi	Yi	@lenml/tokenizer-yi
text_davinci002	Text-Davinci-002	@lenml/tokenizer-text_davinci002
text_davinci003	Text-Davinci-003	@lenml/tokenizer-text_davinci003
text_embedding_ada002	Text-Embedding-Ada-002	@lenml/tokenizer-text_embedding_ada002
gemma2	Gemma 2 / gemini-1.0-pro / gemini-1.5-pro / gemini-1.5-flash / gemini-nano	@lenml/tokenizer-gemma2
gemini	Gemma 2 / gemini-1.0-pro / gemini-1.5-pro / gemini-1.5-flash / gemini-nano	@lenml/tokenizer-gemini
qwen2_5	Qwen 2.5	@lenml/tokenizer-qwen2_5
aya_expanse	Aya Expanse	@lenml/tokenizer-aya_expanse
llama3_2	Llama 3.2	@lenml/tokenizer-llama3_2
mistral_nemo	Mistral Nemo	@lenml/tokenizer-mistral_nemo

In addition to the pre-packaged models listed above, you can also utilize the interfaces in @lenml/tokenizers to load models independently.

Usage

install

npm/yarn/pnpm

npm install @lenml/tokenizers

ESM

<script type="importmap">
  {
    "imports": {
      "@lenml/tokenizers": "https://www.unpkg.com/@lenml/tokenizers@latest/dist/main.mjs"
    }
  }
</script>
<script type="module">
  import { TokenizerLoader, tokenizers } from "@lenml/tokenizers";
  console.log('@lenml/tokenizers: ',tokenizers);
</script>

load tokenizer

from json

import { TokenizerLoader } from "@lenml/tokenizers";
const tokenizer = TokenizerLoader.fromPreTrained({
    tokenizerJSON: { /* ... */ },
    tokenizerConfig: { /* ... */ }
});

from urls

import { TokenizerLoader } from "@lenml/tokenizers";
const sourceUrls = {
    tokenizerJSON: "https://huggingface.co/HuggingFaceH4/zephyr-7b-gemma-v0.1/resolve/main/tokenizer.json?download=true",
    tokenizerConfig: "https://huggingface.co/HuggingFaceH4/zephyr-7b-gemma-v0.1/resolve/main/tokenizer_config.json?download=true"
}
const tokenizer = await TokenizerLoader.fromPreTrainedUrls(sourceUrls);
// or from fetch
const tokenizer = TokenizerLoader.fromPreTrained({
    tokenizerJSON: await fetch(sourceUrls.tokenizerJSON).then(r => r.json()),
    tokenizerConfig: await fetch(sourceUrls.tokenizerConfig).then(r => r.json())
});

from pre-packaged tokenizer

import { fromPreTrained } from "@lenml/tokenizer-llama3";
const tokenizer = fromPreTrained();

chat template

const tokens = tokenizer.apply_chat_template(
  [
    {
      role: "system",
      content: "You are helpful assistant.",
    },
    {
      role: "user",
      content: "Hello, how are you?",
    },
  ]
) as number[];

const chat_content = tokenizer.decode(tokens);

console.log(chat_content);

output:

<|begin_of_text|><|start_header_id|>system<|end_header_id|>

You are helpful assistant.<|eot_id|><|start_header_id|>user<|end_header_id|>

Hello, how are you?<|eot_id|><|start_header_id|>assistant<|end_header_id|>

tokenizer api

console.log(
    "encode() => ",
    tokenizer.encode("Hello, my dog is cute", null, {
        add_special_tokens: true,
    })
);
console.log(
    "_encode_text() => ",
    tokenizer._encode_text("Hello, my dog is cute")
);

fully tokenizer api: transformer.js tokenizers document

get lightweight transformers.tokenizers

In the @lenml/tokenizers package, you can get a lightweight no-dependency implementation of tokenizers:

Since all dependencies related to huggingface have been removed in this library, although the implementation is the same, it is not possible to load models using the form hf_user/repo.

import { tokenizers } from "@lenml/tokenizers";

const {
    CLIPTokenizer,
    AutoTokenizer,
    CohereTokenizer,
    VitsTokenizer,
    WhisperTokenizer,
    // ...
} = tokenizers;

Manual Packaging

In some cases, you may need to use an older version of Node.js, so you might not be able to use pre-packaged packages. In such situations, you can manually package starting from the .ts files.

Here's a simple example:

import {
  tokenizerJSON,
  tokenizerConfig,
} from "@lenml/tokenizer-claude/src/data.ts";
import { TokenizerLoader } from "@lenml/tokenizers/src/main.ts";

export const tokenizer = TokenizerLoader.fromPreTrained({
  tokenizerConfig,
  tokenizerJSON,
});

Performance Benchmark Results

The following table summarizes the performance benchmarks for the Llama31 and GPT4o tokenizers across various datasets. The performance is measured in operations per second (ops/sec), indicating how efficiently each tokenizer processes the given input.

Tokenizer	Operation	Text	Performance (ops/sec)	Margin of Error (±%)	Sampled Runs
Llama31	encode	English	27,260	0.81%	86
Llama31	encode	Chinese	50,675	0.81%	89
Llama31	encode	French	22,836	0.58%	92
Llama31	encode	Code	17,677	0.30%	94
Llama31	decode	English	16,542	0.61%	90
Llama31	decode	Chinese	21,118	0.39%	90
Llama31	decode	French	12,994	0.24%	91
Llama31	decode	Code	10,350	2.80%	87
GPT4o	encode	English	31,618	0.74%	92
GPT4o	encode	Chinese	73,120	0.74%	92
GPT4o	encode	French	27,838	3.40%	91
GPT4o	encode	Code	19,589	3.05%	87
GPT4o	decode	English	24,723	0.73%	91
GPT4o	decode	Chinese	44,201	0.33%	92
GPT4o	decode	French	21,924	0.39%	90
GPT4o	decode	Code	15,785	0.55%	94

The benchmarking script used to generate these results can be found at ./packages/tests/benchmarks/main.ts. You can use this script to replicate the benchmarks and validate the performance metrics for yourself.

License

Apache-2.0