Instructions to use Xenova/codegen-350M-mono with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers.js
How to use Xenova/codegen-350M-mono with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('text-generation', 'Xenova/codegen-350M-mono');
| base_model: Salesforce/codegen-350M-mono | |
| library_name: transformers.js | |
| https://huggingface.co/Salesforce/codegen-350M-mono 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:** Code completion w/ `Xenova/codegen-350M-mono`. | |
| ```js | |
| import { pipeline } from "@huggingface/transformers"; | |
| // Create a text generation pipeline | |
| const generator = await pipeline("text-generation", "Xenova/codegen-350M-mono"); | |
| // Define the prompt | |
| const text = `def fib(n): | |
| """Calculates the nth Fibonacci number"""`; | |
| // Generate a response | |
| const output = await generator(text, { max_new_tokens: 45 }); | |
| console.log(output[0].generated_text); | |
| ``` | |
| --- | |
| 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`). |