🔥V3 dev publish @huggingface/transformers

.prettierignore

@@ -0,0 +1,8 @@
+# Ignore artifacts:
+.github
+dist
+docs
+examples
+scripts
+types
+*.md

.prettierrc

@@ -0,0 +1 @@
+{}

README.md

@@ -11,14 +11,14 @@
 </p>
 
 <p align="center">
-    <a href="https://www.npmjs.com/package/@xenova/transformers">
-        <img alt="NPM" src="https://img.shields.io/npm/v/@xenova/transformers">
+    <a href="https://www.npmjs.com/package/@huggingface/transformers">
+        <img alt="NPM" src="https://img.shields.io/npm/v/@huggingface/transformers">
     </a>
-    <a href="https://www.npmjs.com/package/@xenova/transformers">
-        <img alt="NPM Downloads" src="https://img.shields.io/npm/dw/@xenova/transformers">
+    <a href="https://www.npmjs.com/package/@huggingface/transformers">
+        <img alt="NPM Downloads" src="https://img.shields.io/npm/dw/@huggingface/transformers">
     </a>
-    <a href="https://www.jsdelivr.com/package/npm/@xenova/transformers">
-        <img alt="jsDelivr Hits" src="https://img.shields.io/jsdelivr/npm/hw/@xenova/transformers">
+    <a href="https://www.jsdelivr.com/package/npm/@huggingface/transformers">
+        <img alt="jsDelivr Hits" src="https://img.shields.io/jsdelivr/npm/hw/@huggingface/transformers">
     </a>
     <a href="https://github.com/xenova/transformers.js/blob/main/LICENSE">
         <img alt="License" src="https://img.shields.io/github/license/xenova/transformers.js?color=blue">
@@ -69,7 +69,7 @@ out = pipe('I love transformers!')
 <td>
 
 ```javascript
-import { pipeline } from '@xenova/transformers';
+import { pipeline } from '@huggingface/transformers';
 
 // Allocate a pipeline for sentiment-analysis
 let pipe = await pipeline('sentiment-analysis');
@@ -93,15 +93,15 @@ let pipe = await pipeline('sentiment-analysis', 'Xenova/bert-base-multilingual-u
 ## Installation
 
 
-To install via [NPM](https://www.npmjs.com/package/@xenova/transformers), run:
+To install via [NPM](https://www.npmjs.com/package/@huggingface/transformers), run:
 ```bash
-npm i @xenova/transformers
+npm i @huggingface/transformers
 ```
 
 Alternatively, you can use it in vanilla JS, without any bundler, by using a CDN or static hosting. For example, using [ES Modules](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Modules), you can import the library with:
 ```html
 <script type="module">
-    import { pipeline } from 'https://cdn.jsdelivr.net/npm/@xenova/[email protected].0';
+    import { pipeline } from 'https://cdn.jsdelivr.net/npm/@huggingface/[email protected].3';
 </script>
 ```
 
@@ -134,12 +134,12 @@ Check out the Transformers.js [template](https://huggingface.co/new-space?templa
 
 
 
-By default, Transformers.js uses [hosted pretrained models](https://huggingface.co/models?library=transformers.js) and [precompiled WASM binaries](https://cdn.jsdelivr.net/npm/@xenova/[email protected].0/dist/), which should work out-of-the-box. You can customize this as follows:
+By default, Transformers.js uses [hosted pretrained models](https://huggingface.co/models?library=transformers.js) and [precompiled WASM binaries](https://cdn.jsdelivr.net/npm/@huggingface/[email protected].3/dist/), which should work out-of-the-box. You can customize this as follows:
 
 ### Settings
 
 ```javascript
-import { env } from '@xenova/transformers';
+import { env } from '@huggingface/transformers';
 
 // Specify a custom location for models (defaults to '/models/').
 env.localModelPath = '/path/to/models/';

docs/scripts/build_readme.py

@@ -13,14 +13,14 @@
 </p>
 
 <p align="center">
-    <a href="https://www.npmjs.com/package/@xenova/transformers">
-        <img alt="NPM" src="https://img.shields.io/npm/v/@xenova/transformers">
+    <a href="https://www.npmjs.com/package/@huggingface/transformers">
+        <img alt="NPM" src="https://img.shields.io/npm/v/@huggingface/transformers">
     </a>
-    <a href="https://www.npmjs.com/package/@xenova/transformers">
-        <img alt="NPM Downloads" src="https://img.shields.io/npm/dw/@xenova/transformers">
+    <a href="https://www.npmjs.com/package/@huggingface/transformers">
+        <img alt="NPM Downloads" src="https://img.shields.io/npm/dw/@huggingface/transformers">
     </a>
-    <a href="https://www.jsdelivr.com/package/npm/@xenova/transformers">
-        <img alt="jsDelivr Hits" src="https://img.shields.io/jsdelivr/npm/hw/@xenova/transformers">
+    <a href="https://www.jsdelivr.com/package/npm/@huggingface/transformers">
+        <img alt="jsDelivr Hits" src="https://img.shields.io/jsdelivr/npm/hw/@huggingface/transformers">
     </a>
     <a href="https://github.com/xenova/transformers.js/blob/main/LICENSE">
         <img alt="License" src="https://img.shields.io/github/license/xenova/transformers.js?color=blue">

docs/snippets/1_quick-tour.snippet

@@ -23,7 +23,7 @@ out = pipe('I love transformers!')
 <td>
 
 ```javascript
-import { pipeline } from '@xenova/transformers';
+import { pipeline } from '@huggingface/transformers';
 
 // Allocate a pipeline for sentiment-analysis
 let pipe = await pipeline('sentiment-analysis');

docs/snippets/2_installation.snippet

@@ -1,12 +1,12 @@
 
-To install via [NPM](https://www.npmjs.com/package/@xenova/transformers), run:
+To install via [NPM](https://www.npmjs.com/package/@huggingface/transformers), run:
 ```bash
-npm i @xenova/transformers
+npm i @huggingface/transformers
 ```
 
 Alternatively, you can use it in vanilla JS, without any bundler, by using a CDN or static hosting. For example, using [ES Modules](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Modules), you can import the library with:
 ```html
 <script type="module">
-    import { pipeline } from 'https://cdn.jsdelivr.net/npm/@xenova/[email protected].0';
+    import { pipeline } from 'https://cdn.jsdelivr.net/npm/@huggingface/[email protected].3';
 </script>
 ```

docs/snippets/4_custom-usage.snippet

@@ -1,11 +1,11 @@
 
 
-By default, Transformers.js uses [hosted pretrained models](https://huggingface.co/models?library=transformers.js) and [precompiled WASM binaries](https://cdn.jsdelivr.net/npm/@xenova/[email protected].0/dist/), which should work out-of-the-box. You can customize this as follows:
+By default, Transformers.js uses [hosted pretrained models](https://huggingface.co/models?library=transformers.js) and [precompiled WASM binaries](https://cdn.jsdelivr.net/npm/@huggingface/[email protected].3/dist/), which should work out-of-the-box. You can customize this as follows:
 
 ### Settings
 
 ```javascript
-import { env } from '@xenova/transformers';
+import { env } from '@huggingface/transformers';
 
 // Specify a custom location for models (defaults to '/models/').
 env.localModelPath = '/path/to/models/';

docs/source/guides/node-audio-processing.md

@@ -26,11 +26,11 @@ This tutorial will be written as an ES module, but you can easily adapt it to us
 
 ## Getting started
 
-Let's start by creating a new Node.js project and installing Transformers.js via [NPM](https://www.npmjs.com/package/@xenova/transformers):
+Let's start by creating a new Node.js project and installing Transformers.js via [NPM](https://www.npmjs.com/package/@huggingface/transformers):
 
 ```bash
 npm init -y
-npm i @xenova/transformers
+npm i @huggingface/transformers
 ```
 
 <Tip>
@@ -52,7 +52,7 @@ npm i wavefile
 Start by creating a new file called `index.js`, which will be the entry point for our application. Let's also import the necessary modules:
 
 ```js
-import { pipeline } from '@xenova/transformers';
+import { pipeline } from '@huggingface/transformers';
 import wavefile from 'wavefile';
 ```
 

docs/source/guides/private.md

@@ -28,7 +28,7 @@ Transformers.js will attach an Authorization header to requests made to the Hugg
 One way to do this is to call your program with the environment variable set. For example, let's say you have a file called `llama.js` with the following code:
 
 ```js
-import { AutoTokenizer } from '@xenova/transformers';
+import { AutoTokenizer } from '@huggingface/transformers';
 
 // Load tokenizer for a gated repository.
 const tokenizer = await AutoTokenizer.from_pretrained('meta-llama/Llama-2-7b-hf');

docs/source/pipelines.md

@@ -14,7 +14,7 @@ For the full list of available tasks/pipelines, check out [this table](#availabl
 Start by creating an instance of `pipeline()` and specifying a task you want to use it for. For example, to create a sentiment analysis pipeline, you can do:
 
 ```javascript
-import { pipeline } from '@xenova/transformers';
+import { pipeline } from '@huggingface/transformers';
 
 let classifier = await pipeline('sentiment-analysis');
 ```

docs/source/tutorials/next.md

@@ -42,11 +42,11 @@ On installation, you'll see various prompts. For this demo, we'll be selecting t
 
 ### Step 2: Install and configure Transformers.js
 
-You can install Transformers.js from [NPM](https://www.npmjs.com/package/@xenova/transformers) with the following command:
+You can install Transformers.js from [NPM](https://www.npmjs.com/package/@huggingface/transformers) with the following command:
 
 
 ```bash
-npm i @xenova/transformers
+npm i @huggingface/transformers
 ```
 
 We also need to update the `next.config.js` file to ignore node-specific modules when bundling for the browser:
@@ -76,7 +76,7 @@ module.exports = nextConfig
 Next, we'll create a new [Web Worker](https://developer.mozilla.org/en-US/docs/Web/API/Web_Workers_API/Using_web_workers) script where we'll place all ML-related code. This is to ensure that the main thread is not blocked while the model is loading and performing inference. For this application, we'll be using [`Xenova/distilbert-base-uncased-finetuned-sst-2-english`](https://huggingface.co/Xenova/distilbert-base-uncased-finetuned-sst-2-english), a ~67M parameter model finetuned on the [Stanford Sentiment Treebank](https://huggingface.co/datasets/sst) dataset. Add the following code to `./src/app/worker.js`:
 
 ```js
-import { pipeline, env } from "@xenova/transformers";
+import { pipeline, env } from "@huggingface/transformers";
 
 // Skip local model check
 env.allowLocalModels = false;
@@ -264,11 +264,11 @@ On installation, you'll see various prompts. For this demo, we'll be selecting t
 
 ### Step 2: Install and configure Transformers.js
 
-You can install Transformers.js from [NPM](https://www.npmjs.com/package/@xenova/transformers) with the following command:
+You can install Transformers.js from [NPM](https://www.npmjs.com/package/@huggingface/transformers) with the following command:
 
 
 ```bash
-npm i @xenova/transformers
+npm i @huggingface/transformers
 ```
 
 We also need to update the `next.config.js` file to prevent Webpack from bundling certain packages:
@@ -294,7 +294,7 @@ Next, let's set up our Route Handler. We can do this by creating two files in a
 1. `pipeline.js` - to handle the construction of our pipeline.
 
     ```js
-    import { pipeline } from "@xenova/transformers";
+    import { pipeline } from "@huggingface/transformers";
 
     // Use the Singleton pattern to enable lazy construction of the pipeline.
     // NOTE: We wrap the class in a function to prevent code duplication (see below).

docs/source/tutorials/node.md

@@ -31,11 +31,11 @@ Although you can always use the [Python library](https://github.com/huggingface/
 
 ## Getting started
 
-Let's start by creating a new Node.js project and installing Transformers.js via [NPM](https://www.npmjs.com/package/@xenova/transformers):
+Let's start by creating a new Node.js project and installing Transformers.js via [NPM](https://www.npmjs.com/package/@huggingface/transformers):
 
 ```bash
 npm init -y
-npm i @xenova/transformers
+npm i @huggingface/transformers
 ```
 
 Next, create a new file called `app.js`, which will be the entry point for our application. Depending on whether you're using [ECMAScript modules](#ecmascript-modules-esm) or [CommonJS](#commonjs), you will need to do some things differently (see below).
@@ -66,7 +66,7 @@ import url from 'url';
 Following that, let's import Transformers.js and define the `MyClassificationPipeline` class.
 
 ```javascript
-import { pipeline, env } from '@xenova/transformers';
+import { pipeline, env } from '@huggingface/transformers';
 
 class MyClassificationPipeline {
   static task = 'text-classification';
@@ -107,7 +107,7 @@ class MyClassificationPipeline {
   static async getInstance(progress_callback = null) {
     if (this.instance === null) {
       // Dynamically import the Transformers.js library
-      let { pipeline, env } = await import('@xenova/transformers');
+      let { pipeline, env } = await import('@huggingface/transformers');
 
       // NOTE: Uncomment this to change the cache directory
       // env.cacheDir = './.cache';
@@ -195,7 +195,7 @@ Great! We've successfully created a basic HTTP server that uses Transformers.js
 
 ### Model caching
 
-By default, the first time you run the application, it will download the model files and cache them on your file system (in `./node_modules/@xenova/transformers/.cache/`). All subsequent requests will then use this model. You can change the location of the cache by setting `env.cacheDir`. For example, to cache the model in the `.cache` directory in the current working directory, you can add:
+By default, the first time you run the application, it will download the model files and cache them on your file system (in `./node_modules/@huggingface/transformers/.cache/`). All subsequent requests will then use this model. You can change the location of the cache by setting `env.cacheDir`. For example, to cache the model in the `.cache` directory in the current working directory, you can add:
 
 ```javascript
 env.cacheDir = './.cache';

docs/source/tutorials/react.md

@@ -44,10 +44,10 @@ You can stop the development server by pressing <kbd>Ctrl</kbd> + <kbd>C</kbd> i
 
 ## Step 2: Install and configure Transformers.js
 
-Now we get to the fun part: adding machine learning to our application! First, install Transformers.js from [NPM](https://www.npmjs.com/package/@xenova/transformers) with the following command:
+Now we get to the fun part: adding machine learning to our application! First, install Transformers.js from [NPM](https://www.npmjs.com/package/@huggingface/transformers) with the following command:
 
 ```bash
-npm install @xenova/transformers
+npm install @huggingface/transformers
 ```
 
 For this application, we will use the [Xenova/nllb-200-distilled-600M](https://huggingface.co/Xenova/nllb-200-distilled-600M) model, which can perform multilingual translation among 200 languages. Before we start, there are 2 things we need to take note of:
@@ -58,7 +58,7 @@ We can achieve both of these goals by using a [Web Worker](https://developer.moz
 
 1. Create a file called `worker.js` in the `src` directory. This script will do all the heavy-lifing for us, including loading and running of the translation pipeline. To ensure the model is only loaded once, we will create the `MyTranslationPipeline` class which use the [singleton pattern](https://en.wikipedia.org/wiki/Singleton_pattern) to lazily create a single instance of the pipeline when `getInstance` is first called, and use this pipeline for all subsequent calls:
     ```javascript
-    import { pipeline } from '@xenova/transformers';
+    import { pipeline } from '@huggingface/transformers';
 
     class MyTranslationPipeline {
       static task = 'translation';

docs/source/tutorials/vanilla-js.md

@@ -105,7 +105,7 @@ The `type="module"` attribute is important, as it turns our file into a [JavaScr
 Moving into `index.js`, let's import Transformers.js by adding the following line to the top of the file:
 
 ```js
-import { pipeline, env } from "https://cdn.jsdelivr.net/npm/@xenova/transformers@2.6.0";
+import { pipeline, env } from "https://cdn.jsdelivr.net/npm/@huggingface/transformers";
 ```
 
 Since we will be downloading the model from the Hugging Face Hub, we can skip the local model check by setting:

examples/segment-anything-client/.gitignore

@@ -0,0 +1 @@
+dist

examples/segment-anything-client/package.json

@@ -9,7 +9,7 @@
     "preview": "vite preview"
   },
   "dependencies": {
-    "@xenova/transformers": "^3.0.0"
+    "@huggingface/transformers": "^3.0.0-alpha.0"
   },
   "devDependencies": {
     "vite": "^5.2.9"

examples/segment-anything-client/vite.config.js

@@ -1,6 +1,18 @@
 import { defineConfig } from 'vite';
-export default defineConfig({
-  build: {
-    target: 'esnext'
-  }
+export default defineConfig(env => {
+  const config = {
+    build: {
+      target: 'esnext'
+    }
+  };
+
+  // TODO: Add this back when .wasm files are served locally
+  // if (env.mode === 'development') {
+  //   // The .wasm files are not correctly served using Vite in development mode.
+  //   // This is a workaround to exclude the onnxruntime-web package from Vite's optimization.
+  //   // See also: https://github.com/vitejs/vite/issues/8427
+  //   config.optimizeDeps = { exclude: ["onnxruntime-web"] };
+  // }
+
+  return config;
 });

examples/segment-anything-client/worker.js

@@ -1,4 +1,4 @@
-import { SamModel, AutoProcessor, RawImage, Tensor } from '@xenova/transformers';
+import { SamModel, AutoProcessor, RawImage, Tensor } from '@huggingface/transformers';
 
 // We adopt the singleton pattern to enable lazy-loading of the model and processor.
 export class SegmentAnythingSingleton {
@@ -7,21 +7,11 @@ export class SegmentAnythingSingleton {
     static processor;
 
     static getInstance() {
-        if (!this.model) {
-            this.model = SamModel.from_pretrained(this.model_id, {
-                dtype: {
-                    vision_encoder: 'fp16',
-                    prompt_encoder_mask_decoder: 'q8',
-                },
-                device: {
-                    vision_encoder: 'webgpu',
-                    prompt_encoder_mask_decoder: 'wasm',
-                }
-            });
-        }
-        if (!this.processor) {
-            this.processor = AutoProcessor.from_pretrained(this.model_id);
-        }
+        this.model ??= SamModel.from_pretrained(this.model_id, {
+            dtype: 'fp16',
+            device: 'webgpu',
+        });
+        this.processor ??= AutoProcessor.from_pretrained(this.model_id);
 
         return Promise.all([this.model, this.processor]);
     }

jest.config.mjs

@@ -121,9 +121,7 @@ export default {
   // rootDir: undefined,
 
   // A list of paths to directories that Jest should use to search for files in
-  roots: [
-    "./tests/"
-  ],
+  roots: ["./tests/"],
 
   // Allows you to use a custom runner instead of Jest's default test runner
   // runner: "jest-runner",
@@ -170,7 +168,7 @@ export default {
   // testRunner: "jest-circus/runner",
 
   // A map from regular expressions to paths to transformers
-  transform: {}
+  transform: {},
 
   // An array of regexp pattern strings that are matched against all source file paths, matched files will skip transformation
   // transformIgnorePatterns: [