Merge branch 'merge' into bricks

README.md

@@ -101,7 +101,7 @@ npm i @xenova/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/transformers@2.16.1';
+    import { pipeline } from 'https://cdn.jsdelivr.net/npm/@xenova/transformers@2.17.1';
 </script>
 ```
 
@@ -134,7 +134,7 @@ 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/transformers@2.16.1/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/@xenova/transformers@2.17.1/dist/), which should work out-of-the-box. You can customize this as follows:
 
 
 ### Settings
@@ -198,7 +198,6 @@ You can refine your search by selecting the task you're interested in (e.g., [te
 
 | Task                     | ID | Description | Supported? |
 |--------------------------|----|-------------|------------|
-| [Conversational](https://huggingface.co/tasks/conversational)           | `conversational`  | Generating conversational text that is relevant, coherent and knowledgable given a prompt. | ❌ |
 | [Fill-Mask](https://huggingface.co/tasks/fill-mask)                     | `fill-mask`   | Masking some of the words in a sentence and predicting which words should replace those masks. | ✅ [(docs)](https://huggingface.co/docs/transformers.js/api/pipelines#module_pipelines.FillMaskPipeline)<br>[(models)](https://huggingface.co/models?pipeline_tag=fill-mask&library=transformers.js) |
 | [Question Answering](https://huggingface.co/tasks/question-answering)   | `question-answering`   | Retrieve the answer to a question from a given text. | ✅ [(docs)](https://huggingface.co/docs/transformers.js/api/pipelines#module_pipelines.QuestionAnsweringPipeline)<br>[(models)](https://huggingface.co/models?pipeline_tag=question-answering&library=transformers.js) |
 | [Sentence Similarity](https://huggingface.co/tasks/sentence-similarity) | `sentence-similarity`  | Determining how similar two texts are. | ✅ [(docs)](https://huggingface.co/docs/transformers.js/api/pipelines#module_pipelines.FeatureExtractionPipeline)<br>[(models)](https://huggingface.co/models?pipeline_tag=feature-extraction&library=transformers.js) |
@@ -319,6 +318,7 @@ You can refine your search by selecting the task you're interested in (e.g., [te
 1. **[MMS](https://huggingface.co/docs/transformers/model_doc/mms)** (from Facebook) released with the paper [Scaling Speech Technology to 1,000+ Languages](https://arxiv.org/abs/2305.13516) by Vineel Pratap, Andros Tjandra, Bowen Shi, Paden Tomasello, Arun Babu, Sayani Kundu, Ali Elkahky, Zhaoheng Ni, Apoorv Vyas, Maryam Fazel-Zarandi, Alexei Baevski, Yossi Adi, Xiaohui Zhang, Wei-Ning Hsu, Alexis Conneau, Michael Auli.
 1. **[MobileBERT](https://huggingface.co/docs/transformers/model_doc/mobilebert)** (from CMU/Google Brain) released with the paper [MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices](https://arxiv.org/abs/2004.02984) by Zhiqing Sun, Hongkun Yu, Xiaodan Song, Renjie Liu, Yiming Yang, and Denny Zhou.
 1. **[MobileViT](https://huggingface.co/docs/transformers/model_doc/mobilevit)** (from Apple) released with the paper [MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer](https://arxiv.org/abs/2110.02178) by Sachin Mehta and Mohammad Rastegari.
+1. **[MobileViTV2](https://huggingface.co/docs/transformers/model_doc/mobilevitv2)** (from Apple) released with the paper [Separable Self-attention for Mobile Vision Transformers](https://arxiv.org/abs/2206.02680) by Sachin Mehta and Mohammad Rastegari.
 1. **[MPNet](https://huggingface.co/docs/transformers/model_doc/mpnet)** (from Microsoft Research) released with the paper [MPNet: Masked and Permuted Pre-training for Language Understanding](https://arxiv.org/abs/2004.09297) by Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu.
 1. **[MPT](https://huggingface.co/docs/transformers/model_doc/mpt)** (from MosaiML) released with the repository [llm-foundry](https://github.com/mosaicml/llm-foundry/) by the MosaicML NLP Team.
 1. **[MT5](https://huggingface.co/docs/transformers/model_doc/mt5)** (from Google AI) released with the paper [mT5: A massively multilingual pre-trained text-to-text transformer](https://arxiv.org/abs/2010.11934) by Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, Colin Raffel.

docs/snippets/2_installation.snippet

@@ -7,6 +7,6 @@ npm i @xenova/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/transformers@2.16.1';
+    import { pipeline } from 'https://cdn.jsdelivr.net/npm/@xenova/transformers@2.17.1';
 </script>
 ```

docs/snippets/4_custom-usage.snippet

@@ -1,6 +1,6 @@
 
 
-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/transformers@2.16.1/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/@xenova/transformers@2.17.1/dist/), which should work out-of-the-box. You can customize this as follows:
 
 
 ### Settings

docs/snippets/5_supported-tasks.snippet

@@ -5,7 +5,6 @@
 
 | Task                     | ID | Description | Supported? |
 |--------------------------|----|-------------|------------|
-| [Conversational](https://huggingface.co/tasks/conversational)           | `conversational`  | Generating conversational text that is relevant, coherent and knowledgable given a prompt. | ❌ |
 | [Fill-Mask](https://huggingface.co/tasks/fill-mask)                     | `fill-mask`   | Masking some of the words in a sentence and predicting which words should replace those masks. | ✅ [(docs)](https://huggingface.co/docs/transformers.js/api/pipelines#module_pipelines.FillMaskPipeline)<br>[(models)](https://huggingface.co/models?pipeline_tag=fill-mask&library=transformers.js) |
 | [Question Answering](https://huggingface.co/tasks/question-answering)   | `question-answering`   | Retrieve the answer to a question from a given text. | ✅ [(docs)](https://huggingface.co/docs/transformers.js/api/pipelines#module_pipelines.QuestionAnsweringPipeline)<br>[(models)](https://huggingface.co/models?pipeline_tag=question-answering&library=transformers.js) |
 | [Sentence Similarity](https://huggingface.co/tasks/sentence-similarity) | `sentence-similarity`  | Determining how similar two texts are. | ✅ [(docs)](https://huggingface.co/docs/transformers.js/api/pipelines#module_pipelines.FeatureExtractionPipeline)<br>[(models)](https://huggingface.co/models?pipeline_tag=feature-extraction&library=transformers.js) |

docs/snippets/6_supported-models.snippet

@@ -53,6 +53,7 @@
 1. **[MMS](https://huggingface.co/docs/transformers/model_doc/mms)** (from Facebook) released with the paper [Scaling Speech Technology to 1,000+ Languages](https://arxiv.org/abs/2305.13516) by Vineel Pratap, Andros Tjandra, Bowen Shi, Paden Tomasello, Arun Babu, Sayani Kundu, Ali Elkahky, Zhaoheng Ni, Apoorv Vyas, Maryam Fazel-Zarandi, Alexei Baevski, Yossi Adi, Xiaohui Zhang, Wei-Ning Hsu, Alexis Conneau, Michael Auli.
 1. **[MobileBERT](https://huggingface.co/docs/transformers/model_doc/mobilebert)** (from CMU/Google Brain) released with the paper [MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices](https://arxiv.org/abs/2004.02984) by Zhiqing Sun, Hongkun Yu, Xiaodan Song, Renjie Liu, Yiming Yang, and Denny Zhou.
 1. **[MobileViT](https://huggingface.co/docs/transformers/model_doc/mobilevit)** (from Apple) released with the paper [MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer](https://arxiv.org/abs/2110.02178) by Sachin Mehta and Mohammad Rastegari.
+1. **[MobileViTV2](https://huggingface.co/docs/transformers/model_doc/mobilevitv2)** (from Apple) released with the paper [Separable Self-attention for Mobile Vision Transformers](https://arxiv.org/abs/2206.02680) by Sachin Mehta and Mohammad Rastegari.
 1. **[MPNet](https://huggingface.co/docs/transformers/model_doc/mpnet)** (from Microsoft Research) released with the paper [MPNet: Masked and Permuted Pre-training for Language Understanding](https://arxiv.org/abs/2004.09297) by Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu.
 1. **[MPT](https://huggingface.co/docs/transformers/model_doc/mpt)** (from MosaiML) released with the repository [llm-foundry](https://github.com/mosaicml/llm-foundry/) by the MosaicML NLP Team.
 1. **[MT5](https://huggingface.co/docs/transformers/model_doc/mt5)** (from Google AI) released with the paper [mT5: A massively multilingual pre-trained text-to-text transformer](https://arxiv.org/abs/2010.11934) by Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, Colin Raffel.

docs/source/tutorials/vanilla-js.md

@@ -38,7 +38,7 @@ We’re also adding an empty `<div>` container for displaying the image, plus an
 
 </details>
 
-Next, add the following CSS rules in a `style.css` file and and link it to the HTML:
+Next, add the following CSS rules in a `style.css` file and link it to the HTML:
 
 ```css
 html,

examples/adaptive-retrieval/package.json

@@ -25,6 +25,6 @@
     "eslint-plugin-react-refresh": "^0.4.5",
     "postcss": "^8.4.33",
     "tailwindcss": "^3.4.1",
-    "vite": "^5.0.12"
+    "vite": "^5.1.7"
   }
 }

examples/code-completion/package.json

@@ -26,7 +26,7 @@
     "eslint-plugin-react-refresh": "^0.4.3",
     "postcss": "^8.4.31",
     "tailwindcss": "^3.3.3",
-    "vite": "^4.5.2"
+    "vite": "^4.5.3"
   },
   "overrides": {
     "protobufjs": "^7.2.4"

examples/cross-encoder/package.json

@@ -25,6 +25,6 @@
     "eslint-plugin-react-refresh": "^0.4.5",
     "postcss": "^8.4.33",
     "tailwindcss": "^3.4.1",
-    "vite": "^5.0.12"
+    "vite": "^5.1.7"
   }
 }

examples/depth-anything-client/package.json

@@ -9,7 +9,7 @@
     "preview": "vite preview"
   },
   "devDependencies": {
-    "vite": "^5.0.8"
+    "vite": "^5.0.13"
   },
   "dependencies": {
     "@xenova/transformers": "^2.14.1",

examples/remove-background-client/package.json

@@ -9,7 +9,7 @@
     "preview": "vite preview"
   },
   "devDependencies": {
-    "vite": "^5.0.12"
+    "vite": "^5.0.13"
   },
   "dependencies": {
     "@xenova/transformers": "^2.15.0"

examples/semantic-audio-search/package.json

@@ -9,7 +9,7 @@
     "preview": "vite preview"
   },
   "devDependencies": {
-    "vite": "^5.0.12"
+    "vite": "^5.0.13"
   },
   "dependencies": {
     "@xenova/transformers": "^2.10.1",