metadata
license: other
license_name: exaone
license_link: LICENSE
language:
- en
- ko
tags:
- lg-ai
- exaone
- exaone-3.5
library_name: transformers.js
base_model: LGAI-EXAONE/EXAONE-3.5-2.4B-Instruct
https://huggingface.co/LGAI-EXAONE/EXAONE-3.5-2.4B-Instruct with ONNX weights to be compatible with Transformers.js.
Usage (Transformers.js)
If you haven't already, you can install the Transformers.js JavaScript library from NPM using:
npm i @huggingface/transformers
Example: Text-generation w/ EXAONE-3.5-2.4B-Instruct:
import { pipeline } from "@huggingface/transformers";
// Create a text generation pipeline
const generator = await pipeline(
"text-generation",
"onnx-community/EXAONE-3.5-2.4B-Instruct",
{ dtype: "q4f16" },
);
// Define the list of messages
const messages = [
{ role: "system", content: "You are a helpful assistant." },
{ role: "user", content: "Tell me a joke." },
];
// Generate a response
const output = await generator(messages, { max_new_tokens: 128 });
console.log(output[0].generated_text.at(-1).content);
See example output
Sure! Here's a light joke for you:
Why don't scientists trust atoms?
Because they make up everything!
I hope you found that amusing! If you want another one, feel free to ask!
Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using 🤗 Optimum and structuring your repo like this one (with ONNX weights located in a subfolder named onnx).