Jinja

A minimalistic JavaScript implementation of the Jinja templating engine, specifically designed for parsing and rendering ML chat templates.

Usage

$3

First, install the jinja and hub packages:

``sh
npm i @huggingface/jinja
npm i @huggingface/hub
`

You can then load a tokenizer from the Hugging Face Hub and render a list of chat messages, as follows:

`js
import { Template } from "@huggingface/jinja";
import { downloadFile } from "@huggingface/hub";

const config = await (
await downloadFile({
repo: "mistralai/Mistral-7B-Instruct-v0.1",
path: "tokenizer_config.json",
})
).json();

const chat = [
{ role: "user", content: "Hello, how are you?" },
{ role: "assistant", content: "I'm doing great. How can I help you today?" },
{ role: "user", content: "I'd like to show off how chat templating works!" },
];

const template = new Template(config.chat_template);
const result = template.render({
messages: chat,
bos_token: config.bos_token,
eos_token: config.eos_token,
});
// "[INST] Hello, how are you? [/INST]I'm doing great. How can I help you today? [INST] I'd like to show off how chat templating works! [/INST]"
`

$3

First, install @huggingface/transformers:

`sh
npm i @huggingface/transformers
`

You can then render a list of chat messages using a tokenizer's apply_chat_template method.

`js
import { AutoTokenizer } from "@huggingface/transformers";

// Load tokenizer from the Hugging Face Hub
const tokenizer = await AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1");

// Define chat messages
const chat = [
{ role: "user", content: "Hello, how are you?" },
{ role: "assistant", content: "I'm doing great. How can I help you today?" },
{ role: "user", content: "I'd like to show off how chat templating works!" },
];

const text = tokenizer.apply_chat_template(chat, { tokenize: false });
// "[INST] Hello, how are you? [/INST]I'm doing great. How can I help you today? [INST] I'd like to show off how chat templating works! [/INST]"
`

Notice how the entire chat is condensed into a single string. If you would instead like to return the tokenized version (i.e., a list of token IDs), you can use the following:

`js
const input_ids = tokenizer.apply_chat_template(chat, { tokenize: true, return_tensor: false });
// [1, 733, 16289, 28793, 22557, 28725, 910, 460, 368, 28804, 733, 28748, 16289, 28793, 28737, 28742, 28719, 2548, 1598, 28723, 1602, 541, 315, 1316, 368, 3154, 28804, 2, 28705, 733, 16289, 28793, 315, 28742, 28715, 737, 298, 1347, 805, 910, 10706, 5752, 1077, 3791, 28808, 733, 28748, 16289, 28793]
``

For more information about chat templates, check out the transformers documentation.