README.md

---
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](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@huggingface/transformers) using:
```bash
npm i @huggingface/transformers
```


**Example:** Text-generation w/ `EXAONE-3.5-2.4B-Instruct`:

```js
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);
```

<details>

<summary>See example output</summary>

```
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!
```

</details>

---

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](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).