Merge branch 'main' into add-vslice

Author: xenova

.github/FUNDING.yml

@@ -10,7 +10,6 @@ jobs:
   build:
     uses: huggingface/doc-builder/.github/workflows/build_main_documentation.yml@main
     with:
-      repo_owner: xenova
       commit_sha: ${{ github.sha }}
       package: transformers.js
       path_to_docs: transformers.js/docs/source

.github/workflows/documentation.yml

@@ -11,7 +11,6 @@ jobs:
   build:
     uses: huggingface/doc-builder/.github/workflows/build_pr_documentation.yml@main
     with:
-      repo_owner: xenova
       commit_sha: ${{ github.sha }}
       pr_number: ${{ github.event.number }}
       package: transformers.js

.github/workflows/pr-documentation.yml

@@ -20,7 +20,7 @@ jobs:
 
     strategy:
       matrix:
-        node-version: [18.x, latest, node]
+        node-version: [18, 20, 22]
 
     steps:
       - uses: actions/checkout@v4

.github/workflows/tests.yml

@@ -2,6 +2,7 @@ __pycache__
 .vscode
 node_modules
 .cache
+.DS_STORE
 
 # Do not track build artifacts/generated files
 /dist

.gitignore

@@ -13,33 +13,23 @@
 </p>
 
 <p align="center">
-    <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/@huggingface/transformers">
-        <img alt="NPM Downloads" src="https://img.shields.io/npm/dw/@huggingface/transformers">
-    </a>
-    <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">
-    </a>
-    <a href="https://huggingface.co/docs/transformers.js/index">
-        <img alt="Documentation" src="https://img.shields.io/website/http/huggingface.co/docs/transformers.js/index.svg?down_color=red&down_message=offline&up_message=online">
-    </a>
+    <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/@huggingface/transformers"><img alt="NPM Downloads" src="https://img.shields.io/npm/dw/@huggingface/transformers"></a>
+    <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/huggingface/transformers.js/blob/main/LICENSE"><img alt="License" src="https://img.shields.io/github/license/huggingface/transformers.js?color=blue"></a>
+    <a href="https://huggingface.co/docs/transformers.js/index"><img alt="Documentation" src="https://img.shields.io/website/http/huggingface.co/docs/transformers.js/index.svg?down_color=red&down_message=offline&up_message=online"></a>
 </p>
 
 {intro}
 
-## Quick tour
-
-{quick_tour}
-
 ## Installation
 
 {installation}
 
+## Quick tour
+
+{quick_tour}
+
 ## Examples
 
 {examples}
@@ -52,7 +42,7 @@
 
 Here is the list of all tasks and architectures currently supported by Transformers.js.
 If you don't see your task/model listed here or it is not yet supported, feel free
-to open up a feature request [here](https://github.com/xenova/transformers.js/issues/new/choose).
+to open up a feature request [here](https://github.com/huggingface/transformers.js/issues/new/choose).
 
 To find compatible models on the Hub, select the "transformers.js" library tag in the filter menu (or visit [this link](https://huggingface.co/models?library=transformers.js)).
 You can refine your search by selecting the task you're interested in (e.g., [text-classification](https://huggingface.co/models?pipeline_tag=text-classification&library=transformers.js)).

README.md

@@ -1,5 +1,9 @@
 
-State-of-the-art Machine Learning for the web. Run 🤗 Transformers directly in your browser, with no need for a server!
+<h3 align="center">
+  <p>State-of-the-art Machine Learning for the Web</p>
+</h3>
+
+Run 🤗 Transformers directly in your browser, with no need for a server!
 
 Transformers.js is designed to be functionally equivalent to Hugging Face's [transformers](https://github.com/huggingface/transformers) python library, meaning you can run the same pretrained models using a very similar API. These models support common tasks in different modalities, such as:
   - 📝 **Natural Language Processing**: text classification, named entity recognition, question answering, language modeling, summarization, translation, multiple choice, and text generation.

docs/scripts/build_readme.py

@@ -26,9 +26,9 @@ out = pipe('I love transformers!')
 import { pipeline } from '@huggingface/transformers';
 
 // Allocate a pipeline for sentiment-analysis
-let pipe = await pipeline('sentiment-analysis');
+const pipe = await pipeline('sentiment-analysis');
 
-let out = await pipe('I love transformers!');
+const out = await pipe('I love transformers!');
 // [{'label': 'POSITIVE', 'score': 0.999817686}]
 ```
 
@@ -40,5 +40,32 @@ let out = await pipe('I love transformers!');
 You can also use a different model by specifying the model id or path as the second argument to the `pipeline` function. For example:
 ```javascript
 // Use a different model for sentiment-analysis
-let pipe = await pipeline('sentiment-analysis', 'Xenova/bert-base-multilingual-uncased-sentiment');
+const pipe = await pipeline('sentiment-analysis', 'Xenova/bert-base-multilingual-uncased-sentiment');
+```
+
+By default, when running in the browser, the model will be run on your CPU (via WASM). If you would like
+to run the model on your GPU (via WebGPU), you can do this by setting `device: 'webgpu'`, for example:
+```javascript
+// Run the model on WebGPU
+const pipe = await pipeline('sentiment-analysis', 'Xenova/distilbert-base-uncased-finetuned-sst-2-english', {
+  device: 'webgpu',
+});
+```
+
+For more information, check out the [WebGPU guide](/guides/webgpu).
+
+> [!WARNING]
+> The WebGPU API is still experimental in many browsers, so if you run into any issues,
+> please file a [bug report](https://github.com/huggingface/transformers.js/issues/new?title=%5BWebGPU%5D%20Error%20running%20MODEL_ID_GOES_HERE&assignees=&labels=bug,webgpu&projects=&template=1_bug-report.yml).
+
+In resource-constrained environments, such as web browsers, it is advisable to use a quantized version of
+the model to lower bandwidth and optimize performance. This can be achieved by adjusting the `dtype` option,
+which allows you to select the appropriate data type for your model. While the available options may vary
+depending on the specific model, typical choices include `"fp32"` (default for WebGPU), `"fp16"`, `"q8"`
+(default for WASM), and `"q4"`. For more information, check out the [quantization guide](../guides/dtypes).
+```javascript
+// Run the model at 4-bit quantization
+const pipe = await pipeline('sentiment-analysis', 'Xenova/distilbert-base-uncased-finetuned-sst-2-english', {
+  dtype: 'q4',
+});
 ```

docs/snippets/0_introduction.snippet

@@ -7,6 +7,6 @@ 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/@huggingface/[email protected].0-alpha.20';
+    import { pipeline } from 'https://cdn.jsdelivr.net/npm/@huggingface/[email protected].2';
 </script>
 ```