Instructions to use Xenova/LaMini-Neo-125M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers.js
How to use Xenova/LaMini-Neo-125M with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('text-generation', 'Xenova/LaMini-Neo-125M');
|
Download README.md from Xenova/LaMini-Neo-125M: direct link, hf CLI and curl.
- Browser
- Download file 1.07 kB
-
https://huggingface.co/Xenova/LaMini-Neo-125M/resolve/main/README.md
- Command line
-
hf download hf://Xenova/LaMini-Neo-125M/README.md
-
curl -L -o README.md https://huggingface.co/Xenova/LaMini-Neo-125M/resolve/main/README.md
1.07 kB
metadata
base_model: MBZUAI/LaMini-Neo-125M
library_name: transformers.js
https://huggingface.co/MBZUAI/LaMini-Neo-125M 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.
import { pipeline } from '@huggingface/transformers';
const generator = await pipeline('text-generation', 'Xenova/LaMini-Neo-125M');
const output = await generator('Once upon a time, there was', { max_new_tokens: 10 });
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).