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await inference.translation({
  model: 't5-base',
  inputs: 'My name is Wolfgang and I live in Berlin'
})

await inference.textToImage({
  model: 'stabilityai/stable-diffusion-2',
  inputs: 'award winning high resolution photo of a giant tortoise/((ladybird)) hybrid, [trending on artstation]',
  parameters: {
    negative_prompt: 'blurry',
  }
})

Hugging Face JS libraries

This is a collection of JS libraries to interact with the Hugging Face API, with TS types included.

With more to come, like @huggingface/endpoints to manage your HF Endpoints!

We use modern features to avoid polyfills and dependencies, so the libraries will only work on modern browsers / Node.js >= 18 / Bun / Deno.

The libraries are still very young, please help us by opening issues!

Installation

From NPM

To install via NPM, you can download the libraries as needed:

npm install @huggingface/inference
npm install @huggingface/hub

Then import the libraries in your code:

import { HfInference } from "@huggingface/inference";
import { createRepo, commit, deleteRepo, listFiles } from "@huggingface/hub";
import type { RepoId, Credentials } from "@huggingface/hub";

From CDN or Static hosting

You can run our packages with vanilla JS, without any bundler, by using a CDN or static hosting. Using ES modules, i.e. <script type="module">, you can import the libraries in your code:


<script type="module">
    import { HfInference } from 'https://cdn.jsdelivr.net/npm/@huggingface/inference@2.0.0/+esm';
    import { createRepo, commit, deleteRepo, listFiles } from "https://cdn.jsdelivr.net/npm/@huggingface/hub@0.5.0/+esm";
</script>

Usage examples

Get your HF access token in your account settings.

@huggingface/inference examples 

import { HfInference } from "@huggingface/inference";

const HF_ACCESS_TOKEN = "hf_...";

const inference = new HfInference(HF_ACCESS_TOKEN);

await inference.translation({
  model: 't5-base',
  inputs: 'My name is Wolfgang and I live in Berlin'
})

await inference.textToImage({
  model: 'stabilityai/stable-diffusion-2',
  inputs: 'award winning high resolution photo of a giant tortoise/((ladybird)) hybrid, [trending on artstation]',
  parameters: {
    negative_prompt: 'blurry',
  }
})

await inference.imageToText({
  data: await (await fetch('https://picsum.photos/300/300')).blob(),
  model: 'nlpconnect/vit-gpt2-image-captioning',  
})

// Using your own inference endpoint: https://hf.co/docs/inference-endpoints/
const gpt2 = inference.endpoint('https://xyz.eu-west-1.aws.endpoints.huggingface.cloud/gpt2');
const { generated_text } = await gpt2.textGeneration({inputs: 'The answer to the universe is'});

@huggingface/hub examples 

import { createRepo, uploadFile, deleteFiles } from "@huggingface/hub";

const HF_ACCESS_TOKEN = "hf_...";

await createRepo({
  repo: "my-user/nlp-model", // or {type: "model", name: "my-user/nlp-test"},
  credentials: {accessToken: HF_ACCESS_TOKEN}
});

await uploadFile({
  repo: "my-user/nlp-model",
  credentials: {accessToken: HF_ACCESS_TOKEN},
  // Can work with native File in browsers
  file: {
    path: "pytorch_model.bin",
    content: new Blob(...) 
  }
});

await deleteFiles({
  repo: {type: "space", name: "my-user/my-space"}, // or "spaces/my-user/my-space"
  credentials: {accessToken: HF_ACCESS_TOKEN},
  paths: ["README.md", ".gitattributes"]
});

There are more features of course, check each library’s README!

Formatting & testing 

pnpm install

pnpm -r format:check
pnpm -r lint:check
pnpm -r test

Building 

pnpm -r build

This will generate ESM and CJS javascript files in packages/*/dist, eg packages/inference/dist/index.mjs.