-State-of-the-art Machine Learning for the web. Run 🤗 Transformers directly in your browser, with no need for a server!
+
+
State-of-the-art Machine Learning for the Web
+
+
+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.
- - 🖼️ **Computer Vision**: image classification, object detection, and segmentation.
- - 🗣️ **Audio**: automatic speech recognition and audio classification.
- - 🐙 **Multimodal**: zero-shot image classification.
+ - 🖼️ **Computer Vision**: image classification, object detection, segmentation, and depth estimation.
+ - 🗣️ **Audio**: automatic speech recognition, audio classification, and text-to-speech.
+ - 🐙 **Multimodal**: embeddings, zero-shot audio classification, zero-shot image classification, and zero-shot object detection.
Transformers.js uses [ONNX Runtime](https://onnxruntime.ai/) to run models in the browser. The best part about it, is that you can easily [convert](#convert-your-models-to-onnx) your pretrained PyTorch, TensorFlow, or JAX models to ONNX using [🤗 Optimum](https://github.com/huggingface/optimum#onnx--onnx-runtime).
For more information, check out the full [documentation](https://huggingface.co/docs/transformers.js).
+## Installation
+
+
+To install via [NPM](https://www.npmjs.com/package/@huggingface/transformers), run:
+```bash
+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
+
+```
+
+
## Quick tour
@@ -69,12 +79,12 @@ out = pipe('I love transformers!')
```javascript
-import { pipeline } from '@xenova/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}]
```
@@ -86,46 +96,57 @@ 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',
+});
+```
-## Installation
-
+For more information, check out the [WebGPU guide](https://huggingface.co/docs/transformers.js/guides/webgpu).
-To install via [NPM](https://www.npmjs.com/package/@xenova/transformers), run:
-```bash
-npm i @xenova/transformers
-```
+> [!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).
-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
-
+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](https://huggingface.co/docs/transformers.js/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',
+});
```
## Examples
-Want to jump straight in? Get started with one of our sample applications/templates:
+Want to jump straight in? Get started with one of our sample applications/templates, which can be found [here](https://github.com/huggingface/transformers.js-examples).
| Name | Description | Links |
|-------------------|----------------------------------|-------------------------------|
| Whisper Web | Speech recognition w/ Whisper | [code](https://github.com/xenova/whisper-web), [demo](https://huggingface.co/spaces/Xenova/whisper-web) |
| Doodle Dash | Real-time sketch-recognition game | [blog](https://huggingface.co/blog/ml-web-games), [code](https://github.com/xenova/doodle-dash), [demo](https://huggingface.co/spaces/Xenova/doodle-dash) |
-| Code Playground | In-browser code completion website | [code](https://github.com/xenova/transformers.js/tree/main/examples/code-completion/), [demo](https://huggingface.co/spaces/Xenova/ai-code-playground) |
-| Semantic Image Search (client-side) | Search for images with text | [code](https://github.com/xenova/transformers.js/tree/main/examples/semantic-image-search-client/), [demo](https://huggingface.co/spaces/Xenova/semantic-image-search-client) |
-| Semantic Image Search (server-side) | Search for images with text (Supabase) | [code](https://github.com/xenova/transformers.js/tree/main/examples/semantic-image-search/), [demo](https://huggingface.co/spaces/Xenova/semantic-image-search) |
-| Vanilla JavaScript | In-browser object detection | [video](https://scrimba.com/scrim/cKm9bDAg), [code](https://github.com/xenova/transformers.js/tree/main/examples/vanilla-js/), [demo](https://huggingface.co/spaces/Scrimba/vanilla-js-object-detector) |
-| React | Multilingual translation website | [code](https://github.com/xenova/transformers.js/tree/main/examples/react-translator/), [demo](https://huggingface.co/spaces/Xenova/react-translator) |
-| Text to speech (client-side) | In-browser speech synthesis | [code](https://github.com/xenova/transformers.js/tree/main/examples/text-to-speech-client/), [demo](https://huggingface.co/spaces/Xenova/text-to-speech-client) |
-| Browser extension | Text classification extension | [code](https://github.com/xenova/transformers.js/tree/main/examples/extension/) |
-| Electron | Text classification application | [code](https://github.com/xenova/transformers.js/tree/main/examples/electron/) |
-| Next.js (client-side) | Sentiment analysis (in-browser inference) | [code](https://github.com/xenova/transformers.js/tree/main/examples/next-client/), [demo](https://huggingface.co/spaces/Xenova/next-example-app) |
-| Next.js (server-side) | Sentiment analysis (Node.js inference) | [code](https://github.com/xenova/transformers.js/tree/main/examples/next-server/), [demo](https://huggingface.co/spaces/Xenova/next-server-example-app) |
-| Node.js | Sentiment analysis API | [code](https://github.com/xenova/transformers.js/tree/main/examples/node/) |
-| Demo site | A collection of demos | [code](https://github.com/xenova/transformers.js/tree/main/examples/demo-site/), [demo](https://xenova.github.io/transformers.js/) |
+| Code Playground | In-browser code completion website | [code](https://github.com/huggingface/transformers.js/tree/main/examples/code-completion/), [demo](https://huggingface.co/spaces/Xenova/ai-code-playground) |
+| Semantic Image Search (client-side) | Search for images with text | [code](https://github.com/huggingface/transformers.js/tree/main/examples/semantic-image-search-client/), [demo](https://huggingface.co/spaces/Xenova/semantic-image-search-client) |
+| Semantic Image Search (server-side) | Search for images with text (Supabase) | [code](https://github.com/huggingface/transformers.js/tree/main/examples/semantic-image-search/), [demo](https://huggingface.co/spaces/Xenova/semantic-image-search) |
+| Vanilla JavaScript | In-browser object detection | [video](https://scrimba.com/scrim/cKm9bDAg), [code](https://github.com/huggingface/transformers.js/tree/main/examples/vanilla-js/), [demo](https://huggingface.co/spaces/Scrimba/vanilla-js-object-detector) |
+| React | Multilingual translation website | [code](https://github.com/huggingface/transformers.js/tree/main/examples/react-translator/), [demo](https://huggingface.co/spaces/Xenova/react-translator) |
+| Text to speech (client-side) | In-browser speech synthesis | [code](https://github.com/huggingface/transformers.js/tree/main/examples/text-to-speech-client/), [demo](https://huggingface.co/spaces/Xenova/text-to-speech-client) |
+| Browser extension | Text classification extension | [code](https://github.com/huggingface/transformers.js/tree/main/examples/extension/) |
+| Electron | Text classification application | [code](https://github.com/huggingface/transformers.js/tree/main/examples/electron/) |
+| Next.js (client-side) | Sentiment analysis (in-browser inference) | [code](https://github.com/huggingface/transformers.js/tree/main/examples/next-client/), [demo](https://huggingface.co/spaces/Xenova/next-example-app) |
+| Next.js (server-side) | Sentiment analysis (Node.js inference) | [code](https://github.com/huggingface/transformers.js/tree/main/examples/next-server/), [demo](https://huggingface.co/spaces/Xenova/next-server-example-app) |
+| Node.js | Sentiment analysis API | [code](https://github.com/huggingface/transformers.js/tree/main/examples/node/) |
+| Demo site | A collection of demos | [code](https://github.com/huggingface/transformers.js/tree/main/examples/demo-site/), [demo](https://xenova.github.io/transformers.js/) |
Check out the Transformers.js [template](https://huggingface.co/new-space?template=static-templates%2Ftransformers.js) on Hugging Face to get started in one click!
@@ -134,13 +155,12 @@ 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/transformers@2.17.2/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/transformers@3.1.1/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/';
@@ -156,7 +176,7 @@ For a full list of available settings, check out the [API Reference](https://hug
### Convert your models to ONNX
-We recommend using our [conversion script](https://github.com/xenova/transformers.js/blob/main/scripts/convert.py) to convert your PyTorch, TensorFlow, or JAX models to ONNX in a single command. Behind the scenes, it uses [🤗 Optimum](https://huggingface.co/docs/optimum) to perform conversion and quantization of your model.
+We recommend using our [conversion script](https://github.com/huggingface/transformers.js/blob/main/scripts/convert.py) to convert your PyTorch, TensorFlow, or JAX models to ONNX in a single command. Behind the scenes, it uses [🤗 Optimum](https://huggingface.co/docs/optimum) to perform conversion and quantization of your model.
```bash
python -m scripts.convert --quantize --model_id
@@ -186,7 +206,7 @@ For the full list of supported architectures, see the [Optimum documentation](ht
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)).
@@ -200,7 +220,7 @@ You can refine your search by selecting the task you're interested in (e.g., [te
|--------------------------|----|-------------|------------|
| [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) [(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) [(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) [(models)](https://huggingface.co/models?pipeline_tag=feature-extraction&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) [(models)](https://huggingface.co/models?pipeline_tag=sentence-similarity&library=transformers.js) |
| [Summarization](https://huggingface.co/tasks/summarization) | `summarization` | Producing a shorter version of a document while preserving its important information. | ✅ [(docs)](https://huggingface.co/docs/transformers.js/api/pipelines#module_pipelines.SummarizationPipeline) [(models)](https://huggingface.co/models?pipeline_tag=summarization&library=transformers.js) |
| [Table Question Answering](https://huggingface.co/tasks/table-question-answering) | `table-question-answering` | Answering a question about information from a given table. | ❌ |
| [Text Classification](https://huggingface.co/tasks/text-classification) | `text-classification` or `sentiment-analysis` | Assigning a label or class to a given text. | ✅ [(docs)](https://huggingface.co/docs/transformers.js/api/pipelines#module_pipelines.TextClassificationPipeline) [(models)](https://huggingface.co/models?pipeline_tag=text-classification&library=transformers.js) |
@@ -260,7 +280,7 @@ You can refine your search by selecting the task you're interested in (e.g., [te
| Task | ID | Description | Supported? |
|--------------------------|----|-------------|------------|
-| [Reinforcement Learning](https://huggingface.co/tasks/reinforcement-learning) | n/a | Learning from actions by interacting with an environment through trial and error and receiving rewards (negative or positive) as feedback. | ❌ |
+| [Reinforcement Learning](https://huggingface.co/tasks/reinforcement-learning) | n/a | Learning from actions by interacting with an environment through trial and error and receiving rewards (negative or positive) as feedback. | ✅ |
@@ -281,13 +301,16 @@ You can refine your search by selecting the task you're interested in (e.g., [te
1. **[CLIPSeg](https://huggingface.co/docs/transformers/model_doc/clipseg)** (from University of Göttingen) released with the paper [Image Segmentation Using Text and Image Prompts](https://arxiv.org/abs/2112.10003) by Timo Lüddecke and Alexander Ecker.
1. **[CodeGen](https://huggingface.co/docs/transformers/model_doc/codegen)** (from Salesforce) released with the paper [A Conversational Paradigm for Program Synthesis](https://arxiv.org/abs/2203.13474) by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong.
1. **[CodeLlama](https://huggingface.co/docs/transformers/model_doc/llama_code)** (from MetaAI) released with the paper [Code Llama: Open Foundation Models for Code](https://ai.meta.com/research/publications/code-llama-open-foundation-models-for-code/) by Baptiste Rozière, Jonas Gehring, Fabian Gloeckle, Sten Sootla, Itai Gat, Xiaoqing Ellen Tan, Yossi Adi, Jingyu Liu, Tal Remez, Jérémy Rapin, Artyom Kozhevnikov, Ivan Evtimov, Joanna Bitton, Manish Bhatt, Cristian Canton Ferrer, Aaron Grattafiori, Wenhan Xiong, Alexandre Défossez, Jade Copet, Faisal Azhar, Hugo Touvron, Louis Martin, Nicolas Usunier, Thomas Scialom, Gabriel Synnaeve.
+1. **[Cohere](https://huggingface.co/docs/transformers/main/model_doc/cohere)** (from Cohere) released with the paper [Command-R: Retrieval Augmented Generation at Production Scale]() by Cohere.
1. **[ConvBERT](https://huggingface.co/docs/transformers/model_doc/convbert)** (from YituTech) released with the paper [ConvBERT: Improving BERT with Span-based Dynamic Convolution](https://arxiv.org/abs/2008.02496) by Zihang Jiang, Weihao Yu, Daquan Zhou, Yunpeng Chen, Jiashi Feng, Shuicheng Yan.
1. **[ConvNeXT](https://huggingface.co/docs/transformers/model_doc/convnext)** (from Facebook AI) released with the paper [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545) by Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, Saining Xie.
1. **[ConvNeXTV2](https://huggingface.co/docs/transformers/model_doc/convnextv2)** (from Facebook AI) released with the paper [ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders](https://arxiv.org/abs/2301.00808) by Sanghyun Woo, Shoubhik Debnath, Ronghang Hu, Xinlei Chen, Zhuang Liu, In So Kweon, Saining Xie.
1. **[DeBERTa](https://huggingface.co/docs/transformers/model_doc/deberta)** (from Microsoft) released with the paper [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://arxiv.org/abs/2006.03654) by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen.
1. **[DeBERTa-v2](https://huggingface.co/docs/transformers/model_doc/deberta-v2)** (from Microsoft) released with the paper [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://arxiv.org/abs/2006.03654) by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen.
+1. **[Decision Transformer](https://huggingface.co/docs/transformers/model_doc/decision_transformer)** (from Berkeley/Facebook/Google) released with the paper [Decision Transformer: Reinforcement Learning via Sequence Modeling](https://arxiv.org/abs/2106.01345) by Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas, Igor Mordatch.
1. **[DeiT](https://huggingface.co/docs/transformers/model_doc/deit)** (from Facebook) released with the paper [Training data-efficient image transformers & distillation through attention](https://arxiv.org/abs/2012.12877) by Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, Hervé Jégou.
1. **[Depth Anything](https://huggingface.co/docs/transformers/main/model_doc/depth_anything)** (from University of Hong Kong and TikTok) released with the paper [Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data](https://arxiv.org/abs/2401.10891) by Lihe Yang, Bingyi Kang, Zilong Huang, Xiaogang Xu, Jiashi Feng, Hengshuang Zhao.
+1. **Depth Pro** (from Apple) released with the paper [Depth Pro: Sharp Monocular Metric Depth in Less Than a Second](https://arxiv.org/abs/2410.02073) by Aleksei Bochkovskii, Amaël Delaunoy, Hugo Germain, Marcel Santos, Yichao Zhou, Stephan R. Richter, Vladlen Koltun.
1. **[DETR](https://huggingface.co/docs/transformers/model_doc/detr)** (from Facebook) released with the paper [End-to-End Object Detection with Transformers](https://arxiv.org/abs/2005.12872) by Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, Sergey Zagoruyko.
1. **[DINOv2](https://huggingface.co/docs/transformers/model_doc/dinov2)** (from Meta AI) released with the paper [DINOv2: Learning Robust Visual Features without Supervision](https://arxiv.org/abs/2304.07193) by Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, Mahmoud Assran, Nicolas Ballas, Wojciech Galuba, Russell Howes, Po-Yao Huang, Shang-Wen Li, Ishan Misra, Michael Rabbat, Vasu Sharma, Gabriel Synnaeve, Hu Xu, Hervé Jegou, Julien Mairal, Patrick Labatut, Armand Joulin, Piotr Bojanowski.
1. **[DistilBERT](https://huggingface.co/docs/transformers/model_doc/distilbert)** (from HuggingFace), released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same method has been applied to compress GPT2 into [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/research_projects/distillation), RoBERTa into [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/research_projects/distillation), Multilingual BERT into [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/research_projects/distillation) and a German version of DistilBERT.
@@ -300,39 +323,74 @@ You can refine your search by selecting the task you're interested in (e.g., [te
1. **[Falcon](https://huggingface.co/docs/transformers/model_doc/falcon)** (from Technology Innovation Institute) by Almazrouei, Ebtesam and Alobeidli, Hamza and Alshamsi, Abdulaziz and Cappelli, Alessandro and Cojocaru, Ruxandra and Debbah, Merouane and Goffinet, Etienne and Heslow, Daniel and Launay, Julien and Malartic, Quentin and Noune, Badreddine and Pannier, Baptiste and Penedo, Guilherme.
1. **FastViT** (from Apple) released with the paper [FastViT: A Fast Hybrid Vision Transformer using Structural Reparameterization](https://arxiv.org/abs/2303.14189) by Pavan Kumar Anasosalu Vasu, James Gabriel, Jeff Zhu, Oncel Tuzel and Anurag Ranjan.
1. **[FLAN-T5](https://huggingface.co/docs/transformers/model_doc/flan-t5)** (from Google AI) released in the repository [google-research/t5x](https://github.com/google-research/t5x/blob/main/docs/models.md#flan-t5-checkpoints) by Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Albert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdhery, Sharan Narang, Gaurav Mishra, Adams Yu, Vincent Zhao, Yanping Huang, Andrew Dai, Hongkun Yu, Slav Petrov, Ed H. Chi, Jeff Dean, Jacob Devlin, Adam Roberts, Denny Zhou, Quoc V. Le, and Jason Wei
+1. **Florence2** (from Microsoft) released with the paper [Florence-2: Advancing a Unified Representation for a Variety of Vision Tasks](https://arxiv.org/abs/2311.06242) by Bin Xiao, Haiping Wu, Weijian Xu, Xiyang Dai, Houdong Hu, Yumao Lu, Michael Zeng, Ce Liu, Lu Yuan.
+1. **[Gemma](https://huggingface.co/docs/transformers/main/model_doc/gemma)** (from Google) released with the paper [Gemma: Open Models Based on Gemini Technology and Research](https://blog.google/technology/developers/gemma-open-models/) by the Gemma Google team.
+1. **[Gemma2](https://huggingface.co/docs/transformers/main/model_doc/gemma2)** (from Google) released with the paper [Gemma2: Open Models Based on Gemini Technology and Research](https://blog.google/technology/developers/google-gemma-2/) by the Gemma Google team.
1. **[GLPN](https://huggingface.co/docs/transformers/model_doc/glpn)** (from KAIST) released with the paper [Global-Local Path Networks for Monocular Depth Estimation with Vertical CutDepth](https://arxiv.org/abs/2201.07436) by Doyeon Kim, Woonghyun Ga, Pyungwhan Ahn, Donggyu Joo, Sehwan Chun, Junmo Kim.
1. **[GPT Neo](https://huggingface.co/docs/transformers/model_doc/gpt_neo)** (from EleutherAI) released in the repository [EleutherAI/gpt-neo](https://github.com/EleutherAI/gpt-neo) by Sid Black, Stella Biderman, Leo Gao, Phil Wang and Connor Leahy.
1. **[GPT NeoX](https://huggingface.co/docs/transformers/model_doc/gpt_neox)** (from EleutherAI) released with the paper [GPT-NeoX-20B: An Open-Source Autoregressive Language Model](https://arxiv.org/abs/2204.06745) by Sid Black, Stella Biderman, Eric Hallahan, Quentin Anthony, Leo Gao, Laurence Golding, Horace He, Connor Leahy, Kyle McDonell, Jason Phang, Michael Pieler, USVSN Sai Prashanth, Shivanshu Purohit, Laria Reynolds, Jonathan Tow, Ben Wang, Samuel Weinbach
1. **[GPT-2](https://huggingface.co/docs/transformers/model_doc/gpt2)** (from OpenAI) released with the paper [Language Models are Unsupervised Multitask Learners](https://blog.openai.com/better-language-models/) by Alec Radford*, Jeffrey Wu*, Rewon Child, David Luan, Dario Amodei** and Ilya Sutskever**.
1. **[GPT-J](https://huggingface.co/docs/transformers/model_doc/gptj)** (from EleutherAI) released in the repository [kingoflolz/mesh-transformer-jax](https://github.com/kingoflolz/mesh-transformer-jax/) by Ben Wang and Aran Komatsuzaki.
1. **[GPTBigCode](https://huggingface.co/docs/transformers/model_doc/gpt_bigcode)** (from BigCode) released with the paper [SantaCoder: don't reach for the stars!](https://arxiv.org/abs/2301.03988) by Loubna Ben Allal, Raymond Li, Denis Kocetkov, Chenghao Mou, Christopher Akiki, Carlos Munoz Ferrandis, Niklas Muennighoff, Mayank Mishra, Alex Gu, Manan Dey, Logesh Kumar Umapathi, Carolyn Jane Anderson, Yangtian Zi, Joel Lamy Poirier, Hailey Schoelkopf, Sergey Troshin, Dmitry Abulkhanov, Manuel Romero, Michael Lappert, Francesco De Toni, Bernardo García del Río, Qian Liu, Shamik Bose, Urvashi Bhattacharyya, Terry Yue Zhuo, Ian Yu, Paulo Villegas, Marco Zocca, Sourab Mangrulkar, David Lansky, Huu Nguyen, Danish Contractor, Luis Villa, Jia Li, Dzmitry Bahdanau, Yacine Jernite, Sean Hughes, Daniel Fried, Arjun Guha, Harm de Vries, Leandro von Werra.
+1. **[Granite](https://huggingface.co/docs/transformers/main/model_doc/granite)** (from IBM) released with the paper [Power Scheduler: A Batch Size and Token Number Agnostic Learning Rate Scheduler](https://arxiv.org/abs/2408.13359) by Yikang Shen, Matthew Stallone, Mayank Mishra, Gaoyuan Zhang, Shawn Tan, Aditya Prasad, Adriana Meza Soria, David D. Cox, Rameswar Panda.
+1. **[GroupViT](https://huggingface.co/docs/transformers/model_doc/groupvit)** (from UCSD, NVIDIA) released with the paper [GroupViT: Semantic Segmentation Emerges from Text Supervision](https://arxiv.org/abs/2202.11094) by Jiarui Xu, Shalini De Mello, Sifei Liu, Wonmin Byeon, Thomas Breuel, Jan Kautz, Xiaolong Wang.
1. **[HerBERT](https://huggingface.co/docs/transformers/model_doc/herbert)** (from Allegro.pl, AGH University of Science and Technology) released with the paper [KLEJ: Comprehensive Benchmark for Polish Language Understanding](https://www.aclweb.org/anthology/2020.acl-main.111.pdf) by Piotr Rybak, Robert Mroczkowski, Janusz Tracz, Ireneusz Gawlik.
+1. **[Hiera](https://huggingface.co/docs/transformers/model_doc/hiera)** (from Meta) released with the paper [Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles](https://arxiv.org/pdf/2306.00989) by Chaitanya Ryali, Yuan-Ting Hu, Daniel Bolya, Chen Wei, Haoqi Fan, Po-Yao Huang, Vaibhav Aggarwal, Arkabandhu Chowdhury, Omid Poursaeed, Judy Hoffman, Jitendra Malik, Yanghao Li, Christoph Feichtenhofer.
1. **[Hubert](https://huggingface.co/docs/transformers/model_doc/hubert)** (from Facebook) released with the paper [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://arxiv.org/abs/2106.07447) by Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, Abdelrahman Mohamed.
+1. **[I-JEPA](https://huggingface.co/docs/transformers/model_doc/ijepa)** (from Meta) released with the paper [Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture](https://arxiv.org/abs/2301.08243) by Mahmoud Assran, Quentin Duval, Ishan Misra, Piotr Bojanowski, Pascal Vincent, Michael Rabbat, Yann LeCun, Nicolas Ballas.
+1. **[Idefics3](https://huggingface.co/docs/transformers/model_doc/idefics3)** (from Hugging Face) released with the paper [Building and better understanding vision-language models: insights and future directions](https://arxiv.org/abs/2408.12637) by Hugo Laurençon, Andrés Marafioti, Victor Sanh, Léo Tronchon.
+1. **JAIS** (from Core42) released with the paper [Jais and Jais-chat: Arabic-Centric Foundation and Instruction-Tuned Open Generative Large Language Models](https://arxiv.org/pdf/2308.16149) by Neha Sengupta, Sunil Kumar Sahu, Bokang Jia, Satheesh Katipomu, Haonan Li, Fajri Koto, William Marshall, Gurpreet Gosal, Cynthia Liu, Zhiming Chen, Osama Mohammed Afzal, Samta Kamboj, Onkar Pandit, Rahul Pal, Lalit Pradhan, Zain Muhammad Mujahid, Massa Baali, Xudong Han, Sondos Mahmoud Bsharat, Alham Fikri Aji, Zhiqiang Shen, Zhengzhong Liu, Natalia Vassilieva, Joel Hestness, Andy Hock, Andrew Feldman, Jonathan Lee, Andrew Jackson, Hector Xuguang Ren, Preslav Nakov, Timothy Baldwin, Eric Xing.
+1. **Janus** (from DeepSeek) released with the paper [Janus: Decoupling Visual Encoding for Unified Multimodal Understanding and Generation](https://arxiv.org/abs/2410.13848) Chengyue Wu, Xiaokang Chen, Zhiyu Wu, Yiyang Ma, Xingchao Liu, Zizheng Pan, Wen Liu, Zhenda Xie, Xingkai Yu, Chong Ruan, Ping Luo.
+1. **JinaCLIP** (from Jina AI) released with the paper [Jina CLIP: Your CLIP Model Is Also Your Text Retriever](https://arxiv.org/abs/2405.20204) by Andreas Koukounas, Georgios Mastrapas, Michael Günther, Bo Wang, Scott Martens, Isabelle Mohr, Saba Sturua, Mohammad Kalim Akram, Joan Fontanals Martínez, Saahil Ognawala, Susana Guzman, Maximilian Werk, Nan Wang, Han Xiao.
1. **[LongT5](https://huggingface.co/docs/transformers/model_doc/longt5)** (from Google AI) released with the paper [LongT5: Efficient Text-To-Text Transformer for Long Sequences](https://arxiv.org/abs/2112.07916) by Mandy Guo, Joshua Ainslie, David Uthus, Santiago Ontanon, Jianmo Ni, Yun-Hsuan Sung, Yinfei Yang.
1. **[LLaMA](https://huggingface.co/docs/transformers/model_doc/llama)** (from The FAIR team of Meta AI) released with the paper [LLaMA: Open and Efficient Foundation Language Models](https://arxiv.org/abs/2302.13971) by Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, Guillaume Lample.
1. **[Llama2](https://huggingface.co/docs/transformers/model_doc/llama2)** (from The FAIR team of Meta AI) released with the paper [Llama2: Open Foundation and Fine-Tuned Chat Models](https://ai.meta.com/research/publications/llama-2-open-foundation-and-fine-tuned-chat-models/XXX) by Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller, Cynthia Gao, Vedanuj Goswami, Naman Goyal, Anthony Hartshorn, Saghar Hosseini, Rui Hou, Hakan Inan, Marcin Kardas, Viktor Kerkez Madian Khabsa, Isabel Kloumann, Artem Korenev, Punit Singh Koura, Marie-Anne Lachaux, Thibaut Lavril, Jenya Lee, Diana Liskovich, Yinghai Lu, Yuning Mao, Xavier Martinet, Todor Mihaylov, Pushka rMishra, Igor Molybog, Yixin Nie, Andrew Poulton, Jeremy Reizenstein, Rashi Rungta, Kalyan Saladi, Alan Schelten, Ruan Silva, Eric Michael Smith, Ranjan Subramanian, Xiaoqing EllenTan, Binh Tang, Ross Taylor, Adina Williams, Jian Xiang Kuan, Puxin Xu, Zheng Yan, Iliyan Zarov, Yuchen Zhang, Angela Fan, Melanie Kambadur, Sharan Narang, Aurelien Rodriguez, Robert Stojnic, Sergey Edunov, Thomas Scialom.
+1. **[LLaVa](https://huggingface.co/docs/transformers/model_doc/llava)** (from Microsoft Research & University of Wisconsin-Madison) released with the paper [Visual Instruction Tuning](https://arxiv.org/abs/2304.08485) by Haotian Liu, Chunyuan Li, Yuheng Li and Yong Jae Lee.
+1. **[LLaVA-OneVision](https://huggingface.co/docs/transformers/model_doc/llava_onevision)** (from ByteDance & NTU & CUHK & HKUST) released with the paper [LLaVA-OneVision: Easy Visual Task Transfer](https://arxiv.org/abs/2408.03326) by Bo Li, Yuanhan Zhang, Dong Guo, Renrui Zhang, Feng Li, Hao Zhang, Kaichen Zhang, Yanwei Li, Ziwei Liu, Chunyuan Li
1. **[M2M100](https://huggingface.co/docs/transformers/model_doc/m2m_100)** (from Facebook) released with the paper [Beyond English-Centric Multilingual Machine Translation](https://arxiv.org/abs/2010.11125) by Angela Fan, Shruti Bhosale, Holger Schwenk, Zhiyi Ma, Ahmed El-Kishky, Siddharth Goyal, Mandeep Baines, Onur Celebi, Guillaume Wenzek, Vishrav Chaudhary, Naman Goyal, Tom Birch, Vitaliy Liptchinsky, Sergey Edunov, Edouard Grave, Michael Auli, Armand Joulin.
1. **[MarianMT](https://huggingface.co/docs/transformers/model_doc/marian)** Machine translation models trained using [OPUS](http://opus.nlpl.eu/) data by Jörg Tiedemann. The [Marian Framework](https://marian-nmt.github.io/) is being developed by the Microsoft Translator Team.
+1. **[MaskFormer](https://huggingface.co/docs/transformers/model_doc/maskformer)** (from Meta and UIUC) released with the paper [Per-Pixel Classification is Not All You Need for Semantic Segmentation](https://arxiv.org/abs/2107.06278) by Bowen Cheng, Alexander G. Schwing, Alexander Kirillov.
1. **[mBART](https://huggingface.co/docs/transformers/model_doc/mbart)** (from Facebook) released with the paper [Multilingual Denoising Pre-training for Neural Machine Translation](https://arxiv.org/abs/2001.08210) by Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer.
1. **[mBART-50](https://huggingface.co/docs/transformers/model_doc/mbart)** (from Facebook) released with the paper [Multilingual Translation with Extensible Multilingual Pretraining and Finetuning](https://arxiv.org/abs/2008.00401) by Yuqing Tang, Chau Tran, Xian Li, Peng-Jen Chen, Naman Goyal, Vishrav Chaudhary, Jiatao Gu, Angela Fan.
+1. **[MusicGen](https://huggingface.co/docs/transformers/model_doc/musicgen)** (from Meta) released with the paper [Simple and Controllable Music Generation](https://arxiv.org/abs/2306.05284) by Jade Copet, Felix Kreuk, Itai Gat, Tal Remez, David Kant, Gabriel Synnaeve, Yossi Adi and Alexandre Défossez.
+1. **[MGP-STR](https://huggingface.co/docs/transformers/model_doc/mgp-str)** (from Alibaba Research) released with the paper [Multi-Granularity Prediction for Scene Text Recognition](https://arxiv.org/abs/2209.03592) by Peng Wang, Cheng Da, and Cong Yao.
1. **[Mistral](https://huggingface.co/docs/transformers/model_doc/mistral)** (from Mistral AI) by The [Mistral AI](https://mistral.ai) team: Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed.
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. **MobileCLIP** (from Apple) released with the paper [MobileCLIP: Fast Image-Text Models through Multi-Modal Reinforced Training](https://arxiv.org/abs/2311.17049) by Pavan Kumar Anasosalu Vasu, Hadi Pouransari, Fartash Faghri, Raviteja Vemulapalli, Oncel Tuzel.
+1. **MobileLLM** (from Meta) released with the paper [MobileLLM: Optimizing Sub-billion Parameter Language Models for On-Device Use Cases](https://arxiv.org/abs/2402.14905) by Zechun Liu, Changsheng Zhao, Forrest Iandola, Chen Lai, Yuandong Tian, Igor Fedorov, Yunyang Xiong, Ernie Chang, Yangyang Shi, Raghuraman Krishnamoorthi, Liangzhen Lai, Vikas Chandra.
+1. **[MobileNetV1](https://huggingface.co/docs/transformers/model_doc/mobilenet_v1)** (from Google Inc.) released with the paper [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://arxiv.org/abs/1704.04861) by Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, Hartwig Adam.
+1. **[MobileNetV2](https://huggingface.co/docs/transformers/model_doc/mobilenet_v2)** (from Google Inc.) released with the paper [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://arxiv.org/abs/1801.04381) by Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen.
+1. **MobileNetV3** (from Google Inc.) released with the paper [Searching for MobileNetV3](https://arxiv.org/abs/1905.02244) by Andrew Howard, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, Mingxing Tan, Weijun Wang, Yukun Zhu, Ruoming Pang, Vijay Vasudevan, Quoc V. Le, Hartwig Adam.
+1. **MobileNetV4** (from Google Inc.) released with the paper [MobileNetV4 - Universal Models for the Mobile Ecosystem](https://arxiv.org/abs/2404.10518) by Danfeng Qin, Chas Leichner, Manolis Delakis, Marco Fornoni, Shixin Luo, Fan Yang, Weijun Wang, Colby Banbury, Chengxi Ye, Berkin Akin, Vaibhav Aggarwal, Tenghui Zhu, Daniele Moro, Andrew Howard.
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. **Moondream1** released in the repository [moondream](https://github.com/vikhyat/moondream) by vikhyat.
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. **[MPT](https://huggingface.co/docs/transformers/model_doc/mpt)** (from MosaicML) 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.
1. **[NLLB](https://huggingface.co/docs/transformers/model_doc/nllb)** (from Meta) released with the paper [No Language Left Behind: Scaling Human-Centered Machine Translation](https://arxiv.org/abs/2207.04672) by the NLLB team.
1. **[Nougat](https://huggingface.co/docs/transformers/model_doc/nougat)** (from Meta AI) released with the paper [Nougat: Neural Optical Understanding for Academic Documents](https://arxiv.org/abs/2308.13418) by Lukas Blecher, Guillem Cucurull, Thomas Scialom, Robert Stojnic.
+1. **[OLMo](https://huggingface.co/docs/transformers/master/model_doc/olmo)** (from Ai2) released with the paper [OLMo: Accelerating the Science of Language Models](https://arxiv.org/abs/2402.00838) by Dirk Groeneveld, Iz Beltagy, Pete Walsh, Akshita Bhagia, Rodney Kinney, Oyvind Tafjord, Ananya Harsh Jha, Hamish Ivison, Ian Magnusson, Yizhong Wang, Shane Arora, David Atkinson, Russell Authur, Khyathi Raghavi Chandu, Arman Cohan, Jennifer Dumas, Yanai Elazar, Yuling Gu, Jack Hessel, Tushar Khot, William Merrill, Jacob Morrison, Niklas Muennighoff, Aakanksha Naik, Crystal Nam, Matthew E. Peters, Valentina Pyatkin, Abhilasha Ravichander, Dustin Schwenk, Saurabh Shah, Will Smith, Emma Strubell, Nishant Subramani, Mitchell Wortsman, Pradeep Dasigi, Nathan Lambert, Kyle Richardson, Luke Zettlemoyer, Jesse Dodge, Kyle Lo, Luca Soldaini, Noah A. Smith, Hannaneh Hajishirzi.
+1. **[OLMo2](https://huggingface.co/docs/transformers/master/model_doc/olmo2)** (from Ai2) released with the blog [OLMo 2: The best fully open language model to date](https://allenai.org/blog/olmo2) by the Ai2 OLMo team.
+1. **OpenELM** (from Apple) released with the paper [OpenELM: An Efficient Language Model Family with Open-source Training and Inference Framework](https://arxiv.org/abs/2404.14619) by Sachin Mehta, Mohammad Hossein Sekhavat, Qingqing Cao, Maxwell Horton, Yanzi Jin, Chenfan Sun, Iman Mirzadeh, Mahyar Najibi, Dmitry Belenko, Peter Zatloukal, Mohammad Rastegari.
1. **[OPT](https://huggingface.co/docs/transformers/master/model_doc/opt)** (from Meta AI) released with the paper [OPT: Open Pre-trained Transformer Language Models](https://arxiv.org/abs/2205.01068) by Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen et al.
1. **[OWL-ViT](https://huggingface.co/docs/transformers/model_doc/owlvit)** (from Google AI) released with the paper [Simple Open-Vocabulary Object Detection with Vision Transformers](https://arxiv.org/abs/2205.06230) by Matthias Minderer, Alexey Gritsenko, Austin Stone, Maxim Neumann, Dirk Weissenborn, Alexey Dosovitskiy, Aravindh Mahendran, Anurag Arnab, Mostafa Dehghani, Zhuoran Shen, Xiao Wang, Xiaohua Zhai, Thomas Kipf, and Neil Houlsby.
1. **[OWLv2](https://huggingface.co/docs/transformers/model_doc/owlv2)** (from Google AI) released with the paper [Scaling Open-Vocabulary Object Detection](https://arxiv.org/abs/2306.09683) by Matthias Minderer, Alexey Gritsenko, Neil Houlsby.
+1. **[PaliGemma](https://huggingface.co/docs/transformers/main/model_doc/paligemma)** (from Google) released with the papers [PaliGemma: A versatile 3B VLM for transfer](https://arxiv.org/abs/2407.07726) and [PaliGemma 2: A Family of Versatile VLMs for Transfer](https://arxiv.org/abs/2412.03555) by the PaliGemma Google team.
+1. **[PatchTSMixer](https://huggingface.co/docs/transformers/main/model_doc/patchtsmixer)** (from IBM) released with the paper [TSMixer: Lightweight MLP-Mixer Model for Multivariate Time Series Forecasting](https://arxiv.org/abs/2306.09364) by Vijay Ekambaram, Arindam Jati, Nam Nguyen, Phanwadee Sinthong, Jayant Kalagnanam.
+1. **[PatchTST](https://huggingface.co/docs/transformers/main/model_doc/patchtst)** (from Princeton University, IBM) released with the paper [A Time Series is Worth 64 Words: Long-term Forecasting with Transformers](https://arxiv.org/abs/2211.14730) by Yuqi Nie, Nam H. Nguyen, Phanwadee Sinthong, Jayant Kalagnanam.
1. **[Phi](https://huggingface.co/docs/transformers/main/model_doc/phi)** (from Microsoft) released with the papers - [Textbooks Are All You Need](https://arxiv.org/abs/2306.11644) by Suriya Gunasekar, Yi Zhang, Jyoti Aneja, Caio César Teodoro Mendes, Allie Del Giorno, Sivakanth Gopi, Mojan Javaheripi, Piero Kauffmann, Gustavo de Rosa, Olli Saarikivi, Adil Salim, Shital Shah, Harkirat Singh Behl, Xin Wang, Sébastien Bubeck, Ronen Eldan, Adam Tauman Kalai, Yin Tat Lee and Yuanzhi Li, [Textbooks Are All You Need II: phi-1.5 technical report](https://arxiv.org/abs/2309.05463) by Yuanzhi Li, Sébastien Bubeck, Ronen Eldan, Allie Del Giorno, Suriya Gunasekar and Yin Tat Lee.
+1. **[Phi3](https://huggingface.co/docs/transformers/main/model_doc/phi3)** (from Microsoft) released with the paper [Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone](https://arxiv.org/abs/2404.14219) by Marah Abdin, Sam Ade Jacobs, Ammar Ahmad Awan, Jyoti Aneja, Ahmed Awadallah, Hany Awadalla, Nguyen Bach, Amit Bahree, Arash Bakhtiari, Harkirat Behl, Alon Benhaim, Misha Bilenko, Johan Bjorck, Sébastien Bubeck, Martin Cai, Caio César Teodoro Mendes, Weizhu Chen, Vishrav Chaudhary, Parul Chopra, Allie Del Giorno, Gustavo de Rosa, Matthew Dixon, Ronen Eldan, Dan Iter, Amit Garg, Abhishek Goswami, Suriya Gunasekar, Emman Haider, Junheng Hao, Russell J. Hewett, Jamie Huynh, Mojan Javaheripi, Xin Jin, Piero Kauffmann, Nikos Karampatziakis, Dongwoo Kim, Mahoud Khademi, Lev Kurilenko, James R. Lee, Yin Tat Lee, Yuanzhi Li, Chen Liang, Weishung Liu, Eric Lin, Zeqi Lin, Piyush Madan, Arindam Mitra, Hardik Modi, Anh Nguyen, Brandon Norick, Barun Patra, Daniel Perez-Becker, Thomas Portet, Reid Pryzant, Heyang Qin, Marko Radmilac, Corby Rosset, Sambudha Roy, Olatunji Ruwase, Olli Saarikivi, Amin Saied, Adil Salim, Michael Santacroce, Shital Shah, Ning Shang, Hiteshi Sharma, Xia Song, Masahiro Tanaka, Xin Wang, Rachel Ward, Guanhua Wang, Philipp Witte, Michael Wyatt, Can Xu, Jiahang Xu, Sonali Yadav, Fan Yang, Ziyi Yang, Donghan Yu, Chengruidong Zhang, Cyril Zhang, Jianwen Zhang, Li Lyna Zhang, Yi Zhang, Yue Zhang, Yunan Zhang, Xiren Zhou.
+1. **[PVT](https://huggingface.co/docs/transformers/main/model_doc/pvt)** (from Nanjing University, The University of Hong Kong etc.) released with the paper [Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions](https://arxiv.org/pdf/2102.12122.pdf) by Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lu, Ping Luo, Ling Shao.
+1. **PyAnnote** released in the repository [pyannote/pyannote-audio](https://github.com/pyannote/pyannote-audio) by Hervé Bredin.
1. **[Qwen2](https://huggingface.co/docs/transformers/model_doc/qwen2)** (from the Qwen team, Alibaba Group) released with the paper [Qwen Technical Report](https://arxiv.org/abs/2309.16609) by Jinze Bai, Shuai Bai, Yunfei Chu, Zeyu Cui, Kai Dang, Xiaodong Deng, Yang Fan, Wenbin Ge, Yu Han, Fei Huang, Binyuan Hui, Luo Ji, Mei Li, Junyang Lin, Runji Lin, Dayiheng Liu, Gao Liu, Chengqiang Lu, Keming Lu, Jianxin Ma, Rui Men, Xingzhang Ren, Xuancheng Ren, Chuanqi Tan, Sinan Tan, Jianhong Tu, Peng Wang, Shijie Wang, Wei Wang, Shengguang Wu, Benfeng Xu, Jin Xu, An Yang, Hao Yang, Jian Yang, Shusheng Yang, Yang Yao, Bowen Yu, Hongyi Yuan, Zheng Yuan, Jianwei Zhang, Xingxuan Zhang, Yichang Zhang, Zhenru Zhang, Chang Zhou, Jingren Zhou, Xiaohuan Zhou and Tianhang Zhu.
+1. **[Qwen2-VL](https://huggingface.co/docs/transformers/model_doc/qwen2_vl)** (from the Qwen team, Alibaba Group) released with the paper [Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond](https://arxiv.org/abs/2308.12966) by Jinze Bai, Shuai Bai, Shusheng Yang, Shijie Wang, Sinan Tan, Peng Wang, Junyang Lin, Chang Zhou, Jingren Zhou.
1. **[ResNet](https://huggingface.co/docs/transformers/model_doc/resnet)** (from Microsoft Research) released with the paper [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) by Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun.
1. **[RoBERTa](https://huggingface.co/docs/transformers/model_doc/roberta)** (from Facebook), released together with the paper [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://arxiv.org/abs/1907.11692) by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov.
1. **[RoFormer](https://huggingface.co/docs/transformers/model_doc/roformer)** (from ZhuiyiTechnology), released together with the paper [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://arxiv.org/abs/2104.09864) by Jianlin Su and Yu Lu and Shengfeng Pan and Bo Wen and Yunfeng Liu.
+1. **[RT-DETR](https://huggingface.co/docs/transformers/model_doc/rt_detr)** (from Baidu), released together with the paper [DETRs Beat YOLOs on Real-time Object Detection](https://arxiv.org/abs/2304.08069) by Yian Zhao, Wenyu Lv, Shangliang Xu, Jinman Wei, Guanzhong Wang, Qingqing Dang, Yi Liu, Jie Chen.
+1. **Sapiens** (from Meta AI) released with the paper [Sapiens: Foundation for Human Vision Models](https://arxiv.org/pdf/2408.12569) by Rawal Khirodkar, Timur Bagautdinov, Julieta Martinez, Su Zhaoen, Austin James, Peter Selednik, Stuart Anderson, Shunsuke Saito.
1. **[SegFormer](https://huggingface.co/docs/transformers/model_doc/segformer)** (from NVIDIA) released with the paper [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) by Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M. Alvarez, Ping Luo.
1. **[Segment Anything](https://huggingface.co/docs/transformers/model_doc/sam)** (from Meta AI) released with the paper [Segment Anything](https://arxiv.org/pdf/2304.02643v1.pdf) by Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alex Berg, Wan-Yen Lo, Piotr Dollar, Ross Girshick.
1. **[SigLIP](https://huggingface.co/docs/transformers/main/model_doc/siglip)** (from Google AI) released with the paper [Sigmoid Loss for Language Image Pre-Training](https://arxiv.org/abs/2303.15343) by Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, Lucas Beyer.
@@ -349,7 +407,10 @@ You can refine your search by selecting the task you're interested in (e.g., [te
1. **[UniSpeech](https://huggingface.co/docs/transformers/model_doc/unispeech)** (from Microsoft Research) released with the paper [UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data](https://arxiv.org/abs/2101.07597) by Chengyi Wang, Yu Wu, Yao Qian, Kenichi Kumatani, Shujie Liu, Furu Wei, Michael Zeng, Xuedong Huang.
1. **[UniSpeechSat](https://huggingface.co/docs/transformers/model_doc/unispeech-sat)** (from Microsoft Research) released with the paper [UNISPEECH-SAT: UNIVERSAL SPEECH REPRESENTATION LEARNING WITH SPEAKER AWARE PRE-TRAINING](https://arxiv.org/abs/2110.05752) by Sanyuan Chen, Yu Wu, Chengyi Wang, Zhengyang Chen, Zhuo Chen, Shujie Liu, Jian Wu, Yao Qian, Furu Wei, Jinyu Li, Xiangzhan Yu.
1. **[Vision Transformer (ViT)](https://huggingface.co/docs/transformers/model_doc/vit)** (from Google AI) released with the paper [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) by Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby.
+1. **[ViTMAE](https://huggingface.co/docs/transformers/model_doc/vit_mae)** (from Meta AI) released with the paper [Masked Autoencoders Are Scalable Vision Learners](https://arxiv.org/abs/2111.06377) by Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, Ross Girshick.
1. **[ViTMatte](https://huggingface.co/docs/transformers/model_doc/vitmatte)** (from HUST-VL) released with the paper [ViTMatte: Boosting Image Matting with Pretrained Plain Vision Transformers](https://arxiv.org/abs/2305.15272) by Jingfeng Yao, Xinggang Wang, Shusheng Yang, Baoyuan Wang.
+1. **[ViTMSN](https://huggingface.co/docs/transformers/model_doc/vit_msn)** (from Meta AI) released with the paper [Masked Siamese Networks for Label-Efficient Learning](https://arxiv.org/abs/2204.07141) by Mahmoud Assran, Mathilde Caron, Ishan Misra, Piotr Bojanowski, Florian Bordes, Pascal Vincent, Armand Joulin, Michael Rabbat, Nicolas Ballas.
+1. **[ViTPose](https://huggingface.co/docs/transformers/model_doc/vitpose)** (from The University of Sydney) released with the paper [ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation](https://arxiv.org/abs/2204.12484) by Yufei Xu, Jing Zhang, Qiming Zhang, Dacheng Tao.
1. **[VITS](https://huggingface.co/docs/transformers/model_doc/vits)** (from Kakao Enterprise) released with the paper [Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech](https://arxiv.org/abs/2106.06103) by Jaehyeon Kim, Jungil Kong, Juhee Son.
1. **[Wav2Vec2](https://huggingface.co/docs/transformers/model_doc/wav2vec2)** (from Facebook AI) released with the paper [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://arxiv.org/abs/2006.11477) by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli.
1. **[Wav2Vec2-BERT](https://huggingface.co/docs/transformers/main/model_doc/wav2vec2-bert)** (from Meta AI) released with the paper [Seamless: Multilingual Expressive and Streaming Speech Translation](https://ai.meta.com/research/publications/seamless-multilingual-expressive-and-streaming-speech-translation/) by the Seamless Communication team.
diff --git a/docs/scripts/build_readme.py b/docs/scripts/build_readme.py
index 82c51dad7..2b88f1421 100644
--- a/docs/scripts/build_readme.py
+++ b/docs/scripts/build_readme.py
@@ -5,41 +5,31 @@
{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)).
@@ -79,6 +69,8 @@
CUSTOM_LINK_MAP = {
'/custom_usage#convert-your-models-to-onnx': '#convert-your-models-to-onnx',
'./api/env': DOCS_BASE_URL + '/api/env',
+ './guides/webgpu': DOCS_BASE_URL + '/guides/webgpu',
+ './guides/dtypes': DOCS_BASE_URL + '/guides/dtypes',
}
diff --git a/docs/snippets/0_introduction.snippet b/docs/snippets/0_introduction.snippet
index a0ede3821..34d71bccb 100644
--- a/docs/snippets/0_introduction.snippet
+++ b/docs/snippets/0_introduction.snippet
@@ -1,11 +1,15 @@
-State-of-the-art Machine Learning for the web. Run 🤗 Transformers directly in your browser, with no need for a server!
+
+
State-of-the-art Machine Learning for the Web
+
+
+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.
- - 🖼️ **Computer Vision**: image classification, object detection, and segmentation.
- - 🗣️ **Audio**: automatic speech recognition and audio classification.
- - 🐙 **Multimodal**: zero-shot image classification.
+ - 🖼️ **Computer Vision**: image classification, object detection, segmentation, and depth estimation.
+ - 🗣️ **Audio**: automatic speech recognition, audio classification, and text-to-speech.
+ - 🐙 **Multimodal**: embeddings, zero-shot audio classification, zero-shot image classification, and zero-shot object detection.
Transformers.js uses [ONNX Runtime](https://onnxruntime.ai/) to run models in the browser. The best part about it, is that you can easily [convert](#convert-your-models-to-onnx) your pretrained PyTorch, TensorFlow, or JAX models to ONNX using [🤗 Optimum](https://github.com/huggingface/optimum#onnx--onnx-runtime).
diff --git a/docs/snippets/1_quick-tour.snippet b/docs/snippets/1_quick-tour.snippet
index dec6b341f..27fdd2214 100644
--- a/docs/snippets/1_quick-tour.snippet
+++ b/docs/snippets/1_quick-tour.snippet
@@ -23,12 +23,12 @@ out = pipe('I love transformers!')
```javascript
-import { pipeline } from '@xenova/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',
+});
```
diff --git a/docs/snippets/2_installation.snippet b/docs/snippets/2_installation.snippet
index 5f739c98f..0ef9f7992 100644
--- a/docs/snippets/2_installation.snippet
+++ b/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
```
diff --git a/docs/snippets/3_examples.snippet b/docs/snippets/3_examples.snippet
index 1ee5cc49a..4138482f6 100644
--- a/docs/snippets/3_examples.snippet
+++ b/docs/snippets/3_examples.snippet
@@ -1,20 +1,20 @@
-Want to jump straight in? Get started with one of our sample applications/templates:
+Want to jump straight in? Get started with one of our sample applications/templates, which can be found [here](https://github.com/huggingface/transformers.js-examples).
| Name | Description | Links |
|-------------------|----------------------------------|-------------------------------|
| Whisper Web | Speech recognition w/ Whisper | [code](https://github.com/xenova/whisper-web), [demo](https://huggingface.co/spaces/Xenova/whisper-web) |
| Doodle Dash | Real-time sketch-recognition game | [blog](https://huggingface.co/blog/ml-web-games), [code](https://github.com/xenova/doodle-dash), [demo](https://huggingface.co/spaces/Xenova/doodle-dash) |
-| Code Playground | In-browser code completion website | [code](https://github.com/xenova/transformers.js/tree/main/examples/code-completion/), [demo](https://huggingface.co/spaces/Xenova/ai-code-playground) |
-| Semantic Image Search (client-side) | Search for images with text | [code](https://github.com/xenova/transformers.js/tree/main/examples/semantic-image-search-client/), [demo](https://huggingface.co/spaces/Xenova/semantic-image-search-client) |
-| Semantic Image Search (server-side) | Search for images with text (Supabase) | [code](https://github.com/xenova/transformers.js/tree/main/examples/semantic-image-search/), [demo](https://huggingface.co/spaces/Xenova/semantic-image-search) |
-| Vanilla JavaScript | In-browser object detection | [video](https://scrimba.com/scrim/cKm9bDAg), [code](https://github.com/xenova/transformers.js/tree/main/examples/vanilla-js/), [demo](https://huggingface.co/spaces/Scrimba/vanilla-js-object-detector) |
-| React | Multilingual translation website | [code](https://github.com/xenova/transformers.js/tree/main/examples/react-translator/), [demo](https://huggingface.co/spaces/Xenova/react-translator) |
-| Text to speech (client-side) | In-browser speech synthesis | [code](https://github.com/xenova/transformers.js/tree/main/examples/text-to-speech-client/), [demo](https://huggingface.co/spaces/Xenova/text-to-speech-client) |
-| Browser extension | Text classification extension | [code](https://github.com/xenova/transformers.js/tree/main/examples/extension/) |
-| Electron | Text classification application | [code](https://github.com/xenova/transformers.js/tree/main/examples/electron/) |
-| Next.js (client-side) | Sentiment analysis (in-browser inference) | [code](https://github.com/xenova/transformers.js/tree/main/examples/next-client/), [demo](https://huggingface.co/spaces/Xenova/next-example-app) |
-| Next.js (server-side) | Sentiment analysis (Node.js inference) | [code](https://github.com/xenova/transformers.js/tree/main/examples/next-server/), [demo](https://huggingface.co/spaces/Xenova/next-server-example-app) |
-| Node.js | Sentiment analysis API | [code](https://github.com/xenova/transformers.js/tree/main/examples/node/) |
-| Demo site | A collection of demos | [code](https://github.com/xenova/transformers.js/tree/main/examples/demo-site/), [demo](https://xenova.github.io/transformers.js/) |
+| Code Playground | In-browser code completion website | [code](https://github.com/huggingface/transformers.js/tree/main/examples/code-completion/), [demo](https://huggingface.co/spaces/Xenova/ai-code-playground) |
+| Semantic Image Search (client-side) | Search for images with text | [code](https://github.com/huggingface/transformers.js/tree/main/examples/semantic-image-search-client/), [demo](https://huggingface.co/spaces/Xenova/semantic-image-search-client) |
+| Semantic Image Search (server-side) | Search for images with text (Supabase) | [code](https://github.com/huggingface/transformers.js/tree/main/examples/semantic-image-search/), [demo](https://huggingface.co/spaces/Xenova/semantic-image-search) |
+| Vanilla JavaScript | In-browser object detection | [video](https://scrimba.com/scrim/cKm9bDAg), [code](https://github.com/huggingface/transformers.js/tree/main/examples/vanilla-js/), [demo](https://huggingface.co/spaces/Scrimba/vanilla-js-object-detector) |
+| React | Multilingual translation website | [code](https://github.com/huggingface/transformers.js/tree/main/examples/react-translator/), [demo](https://huggingface.co/spaces/Xenova/react-translator) |
+| Text to speech (client-side) | In-browser speech synthesis | [code](https://github.com/huggingface/transformers.js/tree/main/examples/text-to-speech-client/), [demo](https://huggingface.co/spaces/Xenova/text-to-speech-client) |
+| Browser extension | Text classification extension | [code](https://github.com/huggingface/transformers.js/tree/main/examples/extension/) |
+| Electron | Text classification application | [code](https://github.com/huggingface/transformers.js/tree/main/examples/electron/) |
+| Next.js (client-side) | Sentiment analysis (in-browser inference) | [code](https://github.com/huggingface/transformers.js/tree/main/examples/next-client/), [demo](https://huggingface.co/spaces/Xenova/next-example-app) |
+| Next.js (server-side) | Sentiment analysis (Node.js inference) | [code](https://github.com/huggingface/transformers.js/tree/main/examples/next-server/), [demo](https://huggingface.co/spaces/Xenova/next-server-example-app) |
+| Node.js | Sentiment analysis API | [code](https://github.com/huggingface/transformers.js/tree/main/examples/node/) |
+| Demo site | A collection of demos | [code](https://github.com/huggingface/transformers.js/tree/main/examples/demo-site/), [demo](https://xenova.github.io/transformers.js/) |
Check out the Transformers.js [template](https://huggingface.co/new-space?template=static-templates%2Ftransformers.js) on Hugging Face to get started in one click!
diff --git a/docs/snippets/4_custom-usage.snippet b/docs/snippets/4_custom-usage.snippet
index 787c8f579..28a19fd93 100644
--- a/docs/snippets/4_custom-usage.snippet
+++ b/docs/snippets/4_custom-usage.snippet
@@ -1,12 +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/transformers@2.17.2/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/transformers@3.1.1/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/';
@@ -22,7 +21,7 @@ For a full list of available settings, check out the [API Reference](./api/env).
### Convert your models to ONNX
-We recommend using our [conversion script](https://github.com/xenova/transformers.js/blob/main/scripts/convert.py) to convert your PyTorch, TensorFlow, or JAX models to ONNX in a single command. Behind the scenes, it uses [🤗 Optimum](https://huggingface.co/docs/optimum) to perform conversion and quantization of your model.
+We recommend using our [conversion script](https://github.com/huggingface/transformers.js/blob/main/scripts/convert.py) to convert your PyTorch, TensorFlow, or JAX models to ONNX in a single command. Behind the scenes, it uses [🤗 Optimum](https://huggingface.co/docs/optimum) to perform conversion and quantization of your model.
```bash
python -m scripts.convert --quantize --model_id
diff --git a/docs/snippets/5_supported-tasks.snippet b/docs/snippets/5_supported-tasks.snippet
index ee682ffca..f481fbf96 100644
--- a/docs/snippets/5_supported-tasks.snippet
+++ b/docs/snippets/5_supported-tasks.snippet
@@ -7,7 +7,7 @@
|--------------------------|----|-------------|------------|
| [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) [(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) [(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) [(models)](https://huggingface.co/models?pipeline_tag=feature-extraction&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) [(models)](https://huggingface.co/models?pipeline_tag=sentence-similarity&library=transformers.js) |
| [Summarization](https://huggingface.co/tasks/summarization) | `summarization` | Producing a shorter version of a document while preserving its important information. | ✅ [(docs)](https://huggingface.co/docs/transformers.js/api/pipelines#module_pipelines.SummarizationPipeline) [(models)](https://huggingface.co/models?pipeline_tag=summarization&library=transformers.js) |
| [Table Question Answering](https://huggingface.co/tasks/table-question-answering) | `table-question-answering` | Answering a question about information from a given table. | ❌ |
| [Text Classification](https://huggingface.co/tasks/text-classification) | `text-classification` or `sentiment-analysis` | Assigning a label or class to a given text. | ✅ [(docs)](https://huggingface.co/docs/transformers.js/api/pipelines#module_pipelines.TextClassificationPipeline) [(models)](https://huggingface.co/models?pipeline_tag=text-classification&library=transformers.js) |
@@ -67,4 +67,4 @@
| Task | ID | Description | Supported? |
|--------------------------|----|-------------|------------|
-| [Reinforcement Learning](https://huggingface.co/tasks/reinforcement-learning) | n/a | Learning from actions by interacting with an environment through trial and error and receiving rewards (negative or positive) as feedback. | ❌ |
+| [Reinforcement Learning](https://huggingface.co/tasks/reinforcement-learning) | n/a | Learning from actions by interacting with an environment through trial and error and receiving rewards (negative or positive) as feedback. | ✅ |
diff --git a/docs/snippets/6_supported-models.snippet b/docs/snippets/6_supported-models.snippet
index 8fbdbffb6..ad4f6cdc4 100644
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+++ b/docs/snippets/6_supported-models.snippet
@@ -16,13 +16,16 @@
1. **[CLIPSeg](https://huggingface.co/docs/transformers/model_doc/clipseg)** (from University of Göttingen) released with the paper [Image Segmentation Using Text and Image Prompts](https://arxiv.org/abs/2112.10003) by Timo Lüddecke and Alexander Ecker.
1. **[CodeGen](https://huggingface.co/docs/transformers/model_doc/codegen)** (from Salesforce) released with the paper [A Conversational Paradigm for Program Synthesis](https://arxiv.org/abs/2203.13474) by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong.
1. **[CodeLlama](https://huggingface.co/docs/transformers/model_doc/llama_code)** (from MetaAI) released with the paper [Code Llama: Open Foundation Models for Code](https://ai.meta.com/research/publications/code-llama-open-foundation-models-for-code/) by Baptiste Rozière, Jonas Gehring, Fabian Gloeckle, Sten Sootla, Itai Gat, Xiaoqing Ellen Tan, Yossi Adi, Jingyu Liu, Tal Remez, Jérémy Rapin, Artyom Kozhevnikov, Ivan Evtimov, Joanna Bitton, Manish Bhatt, Cristian Canton Ferrer, Aaron Grattafiori, Wenhan Xiong, Alexandre Défossez, Jade Copet, Faisal Azhar, Hugo Touvron, Louis Martin, Nicolas Usunier, Thomas Scialom, Gabriel Synnaeve.
+1. **[Cohere](https://huggingface.co/docs/transformers/main/model_doc/cohere)** (from Cohere) released with the paper [Command-R: Retrieval Augmented Generation at Production Scale]() by Cohere.
1. **[ConvBERT](https://huggingface.co/docs/transformers/model_doc/convbert)** (from YituTech) released with the paper [ConvBERT: Improving BERT with Span-based Dynamic Convolution](https://arxiv.org/abs/2008.02496) by Zihang Jiang, Weihao Yu, Daquan Zhou, Yunpeng Chen, Jiashi Feng, Shuicheng Yan.
1. **[ConvNeXT](https://huggingface.co/docs/transformers/model_doc/convnext)** (from Facebook AI) released with the paper [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545) by Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, Saining Xie.
1. **[ConvNeXTV2](https://huggingface.co/docs/transformers/model_doc/convnextv2)** (from Facebook AI) released with the paper [ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders](https://arxiv.org/abs/2301.00808) by Sanghyun Woo, Shoubhik Debnath, Ronghang Hu, Xinlei Chen, Zhuang Liu, In So Kweon, Saining Xie.
1. **[DeBERTa](https://huggingface.co/docs/transformers/model_doc/deberta)** (from Microsoft) released with the paper [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://arxiv.org/abs/2006.03654) by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen.
1. **[DeBERTa-v2](https://huggingface.co/docs/transformers/model_doc/deberta-v2)** (from Microsoft) released with the paper [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://arxiv.org/abs/2006.03654) by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen.
+1. **[Decision Transformer](https://huggingface.co/docs/transformers/model_doc/decision_transformer)** (from Berkeley/Facebook/Google) released with the paper [Decision Transformer: Reinforcement Learning via Sequence Modeling](https://arxiv.org/abs/2106.01345) by Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas, Igor Mordatch.
1. **[DeiT](https://huggingface.co/docs/transformers/model_doc/deit)** (from Facebook) released with the paper [Training data-efficient image transformers & distillation through attention](https://arxiv.org/abs/2012.12877) by Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, Hervé Jégou.
1. **[Depth Anything](https://huggingface.co/docs/transformers/main/model_doc/depth_anything)** (from University of Hong Kong and TikTok) released with the paper [Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data](https://arxiv.org/abs/2401.10891) by Lihe Yang, Bingyi Kang, Zilong Huang, Xiaogang Xu, Jiashi Feng, Hengshuang Zhao.
+1. **Depth Pro** (from Apple) released with the paper [Depth Pro: Sharp Monocular Metric Depth in Less Than a Second](https://arxiv.org/abs/2410.02073) by Aleksei Bochkovskii, Amaël Delaunoy, Hugo Germain, Marcel Santos, Yichao Zhou, Stephan R. Richter, Vladlen Koltun.
1. **[DETR](https://huggingface.co/docs/transformers/model_doc/detr)** (from Facebook) released with the paper [End-to-End Object Detection with Transformers](https://arxiv.org/abs/2005.12872) by Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, Sergey Zagoruyko.
1. **[DINOv2](https://huggingface.co/docs/transformers/model_doc/dinov2)** (from Meta AI) released with the paper [DINOv2: Learning Robust Visual Features without Supervision](https://arxiv.org/abs/2304.07193) by Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, Mahmoud Assran, Nicolas Ballas, Wojciech Galuba, Russell Howes, Po-Yao Huang, Shang-Wen Li, Ishan Misra, Michael Rabbat, Vasu Sharma, Gabriel Synnaeve, Hu Xu, Hervé Jegou, Julien Mairal, Patrick Labatut, Armand Joulin, Piotr Bojanowski.
1. **[DistilBERT](https://huggingface.co/docs/transformers/model_doc/distilbert)** (from HuggingFace), released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same method has been applied to compress GPT2 into [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/research_projects/distillation), RoBERTa into [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/research_projects/distillation), Multilingual BERT into [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/research_projects/distillation) and a German version of DistilBERT.
@@ -35,39 +38,74 @@
1. **[Falcon](https://huggingface.co/docs/transformers/model_doc/falcon)** (from Technology Innovation Institute) by Almazrouei, Ebtesam and Alobeidli, Hamza and Alshamsi, Abdulaziz and Cappelli, Alessandro and Cojocaru, Ruxandra and Debbah, Merouane and Goffinet, Etienne and Heslow, Daniel and Launay, Julien and Malartic, Quentin and Noune, Badreddine and Pannier, Baptiste and Penedo, Guilherme.
1. **FastViT** (from Apple) released with the paper [FastViT: A Fast Hybrid Vision Transformer using Structural Reparameterization](https://arxiv.org/abs/2303.14189) by Pavan Kumar Anasosalu Vasu, James Gabriel, Jeff Zhu, Oncel Tuzel and Anurag Ranjan.
1. **[FLAN-T5](https://huggingface.co/docs/transformers/model_doc/flan-t5)** (from Google AI) released in the repository [google-research/t5x](https://github.com/google-research/t5x/blob/main/docs/models.md#flan-t5-checkpoints) by Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Albert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdhery, Sharan Narang, Gaurav Mishra, Adams Yu, Vincent Zhao, Yanping Huang, Andrew Dai, Hongkun Yu, Slav Petrov, Ed H. Chi, Jeff Dean, Jacob Devlin, Adam Roberts, Denny Zhou, Quoc V. Le, and Jason Wei
+1. **Florence2** (from Microsoft) released with the paper [Florence-2: Advancing a Unified Representation for a Variety of Vision Tasks](https://arxiv.org/abs/2311.06242) by Bin Xiao, Haiping Wu, Weijian Xu, Xiyang Dai, Houdong Hu, Yumao Lu, Michael Zeng, Ce Liu, Lu Yuan.
+1. **[Gemma](https://huggingface.co/docs/transformers/main/model_doc/gemma)** (from Google) released with the paper [Gemma: Open Models Based on Gemini Technology and Research](https://blog.google/technology/developers/gemma-open-models/) by the Gemma Google team.
+1. **[Gemma2](https://huggingface.co/docs/transformers/main/model_doc/gemma2)** (from Google) released with the paper [Gemma2: Open Models Based on Gemini Technology and Research](https://blog.google/technology/developers/google-gemma-2/) by the Gemma Google team.
1. **[GLPN](https://huggingface.co/docs/transformers/model_doc/glpn)** (from KAIST) released with the paper [Global-Local Path Networks for Monocular Depth Estimation with Vertical CutDepth](https://arxiv.org/abs/2201.07436) by Doyeon Kim, Woonghyun Ga, Pyungwhan Ahn, Donggyu Joo, Sehwan Chun, Junmo Kim.
1. **[GPT Neo](https://huggingface.co/docs/transformers/model_doc/gpt_neo)** (from EleutherAI) released in the repository [EleutherAI/gpt-neo](https://github.com/EleutherAI/gpt-neo) by Sid Black, Stella Biderman, Leo Gao, Phil Wang and Connor Leahy.
1. **[GPT NeoX](https://huggingface.co/docs/transformers/model_doc/gpt_neox)** (from EleutherAI) released with the paper [GPT-NeoX-20B: An Open-Source Autoregressive Language Model](https://arxiv.org/abs/2204.06745) by Sid Black, Stella Biderman, Eric Hallahan, Quentin Anthony, Leo Gao, Laurence Golding, Horace He, Connor Leahy, Kyle McDonell, Jason Phang, Michael Pieler, USVSN Sai Prashanth, Shivanshu Purohit, Laria Reynolds, Jonathan Tow, Ben Wang, Samuel Weinbach
1. **[GPT-2](https://huggingface.co/docs/transformers/model_doc/gpt2)** (from OpenAI) released with the paper [Language Models are Unsupervised Multitask Learners](https://blog.openai.com/better-language-models/) by Alec Radford*, Jeffrey Wu*, Rewon Child, David Luan, Dario Amodei** and Ilya Sutskever**.
1. **[GPT-J](https://huggingface.co/docs/transformers/model_doc/gptj)** (from EleutherAI) released in the repository [kingoflolz/mesh-transformer-jax](https://github.com/kingoflolz/mesh-transformer-jax/) by Ben Wang and Aran Komatsuzaki.
1. **[GPTBigCode](https://huggingface.co/docs/transformers/model_doc/gpt_bigcode)** (from BigCode) released with the paper [SantaCoder: don't reach for the stars!](https://arxiv.org/abs/2301.03988) by Loubna Ben Allal, Raymond Li, Denis Kocetkov, Chenghao Mou, Christopher Akiki, Carlos Munoz Ferrandis, Niklas Muennighoff, Mayank Mishra, Alex Gu, Manan Dey, Logesh Kumar Umapathi, Carolyn Jane Anderson, Yangtian Zi, Joel Lamy Poirier, Hailey Schoelkopf, Sergey Troshin, Dmitry Abulkhanov, Manuel Romero, Michael Lappert, Francesco De Toni, Bernardo García del Río, Qian Liu, Shamik Bose, Urvashi Bhattacharyya, Terry Yue Zhuo, Ian Yu, Paulo Villegas, Marco Zocca, Sourab Mangrulkar, David Lansky, Huu Nguyen, Danish Contractor, Luis Villa, Jia Li, Dzmitry Bahdanau, Yacine Jernite, Sean Hughes, Daniel Fried, Arjun Guha, Harm de Vries, Leandro von Werra.
+1. **[Granite](https://huggingface.co/docs/transformers/main/model_doc/granite)** (from IBM) released with the paper [Power Scheduler: A Batch Size and Token Number Agnostic Learning Rate Scheduler](https://arxiv.org/abs/2408.13359) by Yikang Shen, Matthew Stallone, Mayank Mishra, Gaoyuan Zhang, Shawn Tan, Aditya Prasad, Adriana Meza Soria, David D. Cox, Rameswar Panda.
+1. **[GroupViT](https://huggingface.co/docs/transformers/model_doc/groupvit)** (from UCSD, NVIDIA) released with the paper [GroupViT: Semantic Segmentation Emerges from Text Supervision](https://arxiv.org/abs/2202.11094) by Jiarui Xu, Shalini De Mello, Sifei Liu, Wonmin Byeon, Thomas Breuel, Jan Kautz, Xiaolong Wang.
1. **[HerBERT](https://huggingface.co/docs/transformers/model_doc/herbert)** (from Allegro.pl, AGH University of Science and Technology) released with the paper [KLEJ: Comprehensive Benchmark for Polish Language Understanding](https://www.aclweb.org/anthology/2020.acl-main.111.pdf) by Piotr Rybak, Robert Mroczkowski, Janusz Tracz, Ireneusz Gawlik.
+1. **[Hiera](https://huggingface.co/docs/transformers/model_doc/hiera)** (from Meta) released with the paper [Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles](https://arxiv.org/pdf/2306.00989) by Chaitanya Ryali, Yuan-Ting Hu, Daniel Bolya, Chen Wei, Haoqi Fan, Po-Yao Huang, Vaibhav Aggarwal, Arkabandhu Chowdhury, Omid Poursaeed, Judy Hoffman, Jitendra Malik, Yanghao Li, Christoph Feichtenhofer.
1. **[Hubert](https://huggingface.co/docs/transformers/model_doc/hubert)** (from Facebook) released with the paper [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://arxiv.org/abs/2106.07447) by Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, Abdelrahman Mohamed.
+1. **[I-JEPA](https://huggingface.co/docs/transformers/model_doc/ijepa)** (from Meta) released with the paper [Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture](https://arxiv.org/abs/2301.08243) by Mahmoud Assran, Quentin Duval, Ishan Misra, Piotr Bojanowski, Pascal Vincent, Michael Rabbat, Yann LeCun, Nicolas Ballas.
+1. **[Idefics3](https://huggingface.co/docs/transformers/model_doc/idefics3)** (from Hugging Face) released with the paper [Building and better understanding vision-language models: insights and future directions](https://arxiv.org/abs/2408.12637) by Hugo Laurençon, Andrés Marafioti, Victor Sanh, Léo Tronchon.
+1. **JAIS** (from Core42) released with the paper [Jais and Jais-chat: Arabic-Centric Foundation and Instruction-Tuned Open Generative Large Language Models](https://arxiv.org/pdf/2308.16149) by Neha Sengupta, Sunil Kumar Sahu, Bokang Jia, Satheesh Katipomu, Haonan Li, Fajri Koto, William Marshall, Gurpreet Gosal, Cynthia Liu, Zhiming Chen, Osama Mohammed Afzal, Samta Kamboj, Onkar Pandit, Rahul Pal, Lalit Pradhan, Zain Muhammad Mujahid, Massa Baali, Xudong Han, Sondos Mahmoud Bsharat, Alham Fikri Aji, Zhiqiang Shen, Zhengzhong Liu, Natalia Vassilieva, Joel Hestness, Andy Hock, Andrew Feldman, Jonathan Lee, Andrew Jackson, Hector Xuguang Ren, Preslav Nakov, Timothy Baldwin, Eric Xing.
+1. **Janus** (from DeepSeek) released with the paper [Janus: Decoupling Visual Encoding for Unified Multimodal Understanding and Generation](https://arxiv.org/abs/2410.13848) Chengyue Wu, Xiaokang Chen, Zhiyu Wu, Yiyang Ma, Xingchao Liu, Zizheng Pan, Wen Liu, Zhenda Xie, Xingkai Yu, Chong Ruan, Ping Luo.
+1. **JinaCLIP** (from Jina AI) released with the paper [Jina CLIP: Your CLIP Model Is Also Your Text Retriever](https://arxiv.org/abs/2405.20204) by Andreas Koukounas, Georgios Mastrapas, Michael Günther, Bo Wang, Scott Martens, Isabelle Mohr, Saba Sturua, Mohammad Kalim Akram, Joan Fontanals Martínez, Saahil Ognawala, Susana Guzman, Maximilian Werk, Nan Wang, Han Xiao.
1. **[LongT5](https://huggingface.co/docs/transformers/model_doc/longt5)** (from Google AI) released with the paper [LongT5: Efficient Text-To-Text Transformer for Long Sequences](https://arxiv.org/abs/2112.07916) by Mandy Guo, Joshua Ainslie, David Uthus, Santiago Ontanon, Jianmo Ni, Yun-Hsuan Sung, Yinfei Yang.
1. **[LLaMA](https://huggingface.co/docs/transformers/model_doc/llama)** (from The FAIR team of Meta AI) released with the paper [LLaMA: Open and Efficient Foundation Language Models](https://arxiv.org/abs/2302.13971) by Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, Guillaume Lample.
1. **[Llama2](https://huggingface.co/docs/transformers/model_doc/llama2)** (from The FAIR team of Meta AI) released with the paper [Llama2: Open Foundation and Fine-Tuned Chat Models](https://ai.meta.com/research/publications/llama-2-open-foundation-and-fine-tuned-chat-models/XXX) by Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller, Cynthia Gao, Vedanuj Goswami, Naman Goyal, Anthony Hartshorn, Saghar Hosseini, Rui Hou, Hakan Inan, Marcin Kardas, Viktor Kerkez Madian Khabsa, Isabel Kloumann, Artem Korenev, Punit Singh Koura, Marie-Anne Lachaux, Thibaut Lavril, Jenya Lee, Diana Liskovich, Yinghai Lu, Yuning Mao, Xavier Martinet, Todor Mihaylov, Pushka rMishra, Igor Molybog, Yixin Nie, Andrew Poulton, Jeremy Reizenstein, Rashi Rungta, Kalyan Saladi, Alan Schelten, Ruan Silva, Eric Michael Smith, Ranjan Subramanian, Xiaoqing EllenTan, Binh Tang, Ross Taylor, Adina Williams, Jian Xiang Kuan, Puxin Xu, Zheng Yan, Iliyan Zarov, Yuchen Zhang, Angela Fan, Melanie Kambadur, Sharan Narang, Aurelien Rodriguez, Robert Stojnic, Sergey Edunov, Thomas Scialom.
+1. **[LLaVa](https://huggingface.co/docs/transformers/model_doc/llava)** (from Microsoft Research & University of Wisconsin-Madison) released with the paper [Visual Instruction Tuning](https://arxiv.org/abs/2304.08485) by Haotian Liu, Chunyuan Li, Yuheng Li and Yong Jae Lee.
+1. **[LLaVA-OneVision](https://huggingface.co/docs/transformers/model_doc/llava_onevision)** (from ByteDance & NTU & CUHK & HKUST) released with the paper [LLaVA-OneVision: Easy Visual Task Transfer](https://arxiv.org/abs/2408.03326) by Bo Li, Yuanhan Zhang, Dong Guo, Renrui Zhang, Feng Li, Hao Zhang, Kaichen Zhang, Yanwei Li, Ziwei Liu, Chunyuan Li
1. **[M2M100](https://huggingface.co/docs/transformers/model_doc/m2m_100)** (from Facebook) released with the paper [Beyond English-Centric Multilingual Machine Translation](https://arxiv.org/abs/2010.11125) by Angela Fan, Shruti Bhosale, Holger Schwenk, Zhiyi Ma, Ahmed El-Kishky, Siddharth Goyal, Mandeep Baines, Onur Celebi, Guillaume Wenzek, Vishrav Chaudhary, Naman Goyal, Tom Birch, Vitaliy Liptchinsky, Sergey Edunov, Edouard Grave, Michael Auli, Armand Joulin.
1. **[MarianMT](https://huggingface.co/docs/transformers/model_doc/marian)** Machine translation models trained using [OPUS](http://opus.nlpl.eu/) data by Jörg Tiedemann. The [Marian Framework](https://marian-nmt.github.io/) is being developed by the Microsoft Translator Team.
+1. **[MaskFormer](https://huggingface.co/docs/transformers/model_doc/maskformer)** (from Meta and UIUC) released with the paper [Per-Pixel Classification is Not All You Need for Semantic Segmentation](https://arxiv.org/abs/2107.06278) by Bowen Cheng, Alexander G. Schwing, Alexander Kirillov.
1. **[mBART](https://huggingface.co/docs/transformers/model_doc/mbart)** (from Facebook) released with the paper [Multilingual Denoising Pre-training for Neural Machine Translation](https://arxiv.org/abs/2001.08210) by Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer.
1. **[mBART-50](https://huggingface.co/docs/transformers/model_doc/mbart)** (from Facebook) released with the paper [Multilingual Translation with Extensible Multilingual Pretraining and Finetuning](https://arxiv.org/abs/2008.00401) by Yuqing Tang, Chau Tran, Xian Li, Peng-Jen Chen, Naman Goyal, Vishrav Chaudhary, Jiatao Gu, Angela Fan.
+1. **[MusicGen](https://huggingface.co/docs/transformers/model_doc/musicgen)** (from Meta) released with the paper [Simple and Controllable Music Generation](https://arxiv.org/abs/2306.05284) by Jade Copet, Felix Kreuk, Itai Gat, Tal Remez, David Kant, Gabriel Synnaeve, Yossi Adi and Alexandre Défossez.
+1. **[MGP-STR](https://huggingface.co/docs/transformers/model_doc/mgp-str)** (from Alibaba Research) released with the paper [Multi-Granularity Prediction for Scene Text Recognition](https://arxiv.org/abs/2209.03592) by Peng Wang, Cheng Da, and Cong Yao.
1. **[Mistral](https://huggingface.co/docs/transformers/model_doc/mistral)** (from Mistral AI) by The [Mistral AI](https://mistral.ai) team: Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed.
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. **MobileCLIP** (from Apple) released with the paper [MobileCLIP: Fast Image-Text Models through Multi-Modal Reinforced Training](https://arxiv.org/abs/2311.17049) by Pavan Kumar Anasosalu Vasu, Hadi Pouransari, Fartash Faghri, Raviteja Vemulapalli, Oncel Tuzel.
+1. **MobileLLM** (from Meta) released with the paper [MobileLLM: Optimizing Sub-billion Parameter Language Models for On-Device Use Cases](https://arxiv.org/abs/2402.14905) by Zechun Liu, Changsheng Zhao, Forrest Iandola, Chen Lai, Yuandong Tian, Igor Fedorov, Yunyang Xiong, Ernie Chang, Yangyang Shi, Raghuraman Krishnamoorthi, Liangzhen Lai, Vikas Chandra.
+1. **[MobileNetV1](https://huggingface.co/docs/transformers/model_doc/mobilenet_v1)** (from Google Inc.) released with the paper [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://arxiv.org/abs/1704.04861) by Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, Hartwig Adam.
+1. **[MobileNetV2](https://huggingface.co/docs/transformers/model_doc/mobilenet_v2)** (from Google Inc.) released with the paper [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://arxiv.org/abs/1801.04381) by Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen.
+1. **MobileNetV3** (from Google Inc.) released with the paper [Searching for MobileNetV3](https://arxiv.org/abs/1905.02244) by Andrew Howard, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, Mingxing Tan, Weijun Wang, Yukun Zhu, Ruoming Pang, Vijay Vasudevan, Quoc V. Le, Hartwig Adam.
+1. **MobileNetV4** (from Google Inc.) released with the paper [MobileNetV4 - Universal Models for the Mobile Ecosystem](https://arxiv.org/abs/2404.10518) by Danfeng Qin, Chas Leichner, Manolis Delakis, Marco Fornoni, Shixin Luo, Fan Yang, Weijun Wang, Colby Banbury, Chengxi Ye, Berkin Akin, Vaibhav Aggarwal, Tenghui Zhu, Daniele Moro, Andrew Howard.
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. **Moondream1** released in the repository [moondream](https://github.com/vikhyat/moondream) by vikhyat.
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. **[MPT](https://huggingface.co/docs/transformers/model_doc/mpt)** (from MosaicML) 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.
1. **[NLLB](https://huggingface.co/docs/transformers/model_doc/nllb)** (from Meta) released with the paper [No Language Left Behind: Scaling Human-Centered Machine Translation](https://arxiv.org/abs/2207.04672) by the NLLB team.
1. **[Nougat](https://huggingface.co/docs/transformers/model_doc/nougat)** (from Meta AI) released with the paper [Nougat: Neural Optical Understanding for Academic Documents](https://arxiv.org/abs/2308.13418) by Lukas Blecher, Guillem Cucurull, Thomas Scialom, Robert Stojnic.
+1. **[OLMo](https://huggingface.co/docs/transformers/master/model_doc/olmo)** (from Ai2) released with the paper [OLMo: Accelerating the Science of Language Models](https://arxiv.org/abs/2402.00838) by Dirk Groeneveld, Iz Beltagy, Pete Walsh, Akshita Bhagia, Rodney Kinney, Oyvind Tafjord, Ananya Harsh Jha, Hamish Ivison, Ian Magnusson, Yizhong Wang, Shane Arora, David Atkinson, Russell Authur, Khyathi Raghavi Chandu, Arman Cohan, Jennifer Dumas, Yanai Elazar, Yuling Gu, Jack Hessel, Tushar Khot, William Merrill, Jacob Morrison, Niklas Muennighoff, Aakanksha Naik, Crystal Nam, Matthew E. Peters, Valentina Pyatkin, Abhilasha Ravichander, Dustin Schwenk, Saurabh Shah, Will Smith, Emma Strubell, Nishant Subramani, Mitchell Wortsman, Pradeep Dasigi, Nathan Lambert, Kyle Richardson, Luke Zettlemoyer, Jesse Dodge, Kyle Lo, Luca Soldaini, Noah A. Smith, Hannaneh Hajishirzi.
+1. **[OLMo2](https://huggingface.co/docs/transformers/master/model_doc/olmo2)** (from Ai2) released with the blog [OLMo 2: The best fully open language model to date](https://allenai.org/blog/olmo2) by the Ai2 OLMo team.
+1. **OpenELM** (from Apple) released with the paper [OpenELM: An Efficient Language Model Family with Open-source Training and Inference Framework](https://arxiv.org/abs/2404.14619) by Sachin Mehta, Mohammad Hossein Sekhavat, Qingqing Cao, Maxwell Horton, Yanzi Jin, Chenfan Sun, Iman Mirzadeh, Mahyar Najibi, Dmitry Belenko, Peter Zatloukal, Mohammad Rastegari.
1. **[OPT](https://huggingface.co/docs/transformers/master/model_doc/opt)** (from Meta AI) released with the paper [OPT: Open Pre-trained Transformer Language Models](https://arxiv.org/abs/2205.01068) by Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen et al.
1. **[OWL-ViT](https://huggingface.co/docs/transformers/model_doc/owlvit)** (from Google AI) released with the paper [Simple Open-Vocabulary Object Detection with Vision Transformers](https://arxiv.org/abs/2205.06230) by Matthias Minderer, Alexey Gritsenko, Austin Stone, Maxim Neumann, Dirk Weissenborn, Alexey Dosovitskiy, Aravindh Mahendran, Anurag Arnab, Mostafa Dehghani, Zhuoran Shen, Xiao Wang, Xiaohua Zhai, Thomas Kipf, and Neil Houlsby.
1. **[OWLv2](https://huggingface.co/docs/transformers/model_doc/owlv2)** (from Google AI) released with the paper [Scaling Open-Vocabulary Object Detection](https://arxiv.org/abs/2306.09683) by Matthias Minderer, Alexey Gritsenko, Neil Houlsby.
+1. **[PaliGemma](https://huggingface.co/docs/transformers/main/model_doc/paligemma)** (from Google) released with the papers [PaliGemma: A versatile 3B VLM for transfer](https://arxiv.org/abs/2407.07726) and [PaliGemma 2: A Family of Versatile VLMs for Transfer](https://arxiv.org/abs/2412.03555) by the PaliGemma Google team.
+1. **[PatchTSMixer](https://huggingface.co/docs/transformers/main/model_doc/patchtsmixer)** (from IBM) released with the paper [TSMixer: Lightweight MLP-Mixer Model for Multivariate Time Series Forecasting](https://arxiv.org/abs/2306.09364) by Vijay Ekambaram, Arindam Jati, Nam Nguyen, Phanwadee Sinthong, Jayant Kalagnanam.
+1. **[PatchTST](https://huggingface.co/docs/transformers/main/model_doc/patchtst)** (from Princeton University, IBM) released with the paper [A Time Series is Worth 64 Words: Long-term Forecasting with Transformers](https://arxiv.org/abs/2211.14730) by Yuqi Nie, Nam H. Nguyen, Phanwadee Sinthong, Jayant Kalagnanam.
1. **[Phi](https://huggingface.co/docs/transformers/main/model_doc/phi)** (from Microsoft) released with the papers - [Textbooks Are All You Need](https://arxiv.org/abs/2306.11644) by Suriya Gunasekar, Yi Zhang, Jyoti Aneja, Caio César Teodoro Mendes, Allie Del Giorno, Sivakanth Gopi, Mojan Javaheripi, Piero Kauffmann, Gustavo de Rosa, Olli Saarikivi, Adil Salim, Shital Shah, Harkirat Singh Behl, Xin Wang, Sébastien Bubeck, Ronen Eldan, Adam Tauman Kalai, Yin Tat Lee and Yuanzhi Li, [Textbooks Are All You Need II: phi-1.5 technical report](https://arxiv.org/abs/2309.05463) by Yuanzhi Li, Sébastien Bubeck, Ronen Eldan, Allie Del Giorno, Suriya Gunasekar and Yin Tat Lee.
+1. **[Phi3](https://huggingface.co/docs/transformers/main/model_doc/phi3)** (from Microsoft) released with the paper [Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone](https://arxiv.org/abs/2404.14219) by Marah Abdin, Sam Ade Jacobs, Ammar Ahmad Awan, Jyoti Aneja, Ahmed Awadallah, Hany Awadalla, Nguyen Bach, Amit Bahree, Arash Bakhtiari, Harkirat Behl, Alon Benhaim, Misha Bilenko, Johan Bjorck, Sébastien Bubeck, Martin Cai, Caio César Teodoro Mendes, Weizhu Chen, Vishrav Chaudhary, Parul Chopra, Allie Del Giorno, Gustavo de Rosa, Matthew Dixon, Ronen Eldan, Dan Iter, Amit Garg, Abhishek Goswami, Suriya Gunasekar, Emman Haider, Junheng Hao, Russell J. Hewett, Jamie Huynh, Mojan Javaheripi, Xin Jin, Piero Kauffmann, Nikos Karampatziakis, Dongwoo Kim, Mahoud Khademi, Lev Kurilenko, James R. Lee, Yin Tat Lee, Yuanzhi Li, Chen Liang, Weishung Liu, Eric Lin, Zeqi Lin, Piyush Madan, Arindam Mitra, Hardik Modi, Anh Nguyen, Brandon Norick, Barun Patra, Daniel Perez-Becker, Thomas Portet, Reid Pryzant, Heyang Qin, Marko Radmilac, Corby Rosset, Sambudha Roy, Olatunji Ruwase, Olli Saarikivi, Amin Saied, Adil Salim, Michael Santacroce, Shital Shah, Ning Shang, Hiteshi Sharma, Xia Song, Masahiro Tanaka, Xin Wang, Rachel Ward, Guanhua Wang, Philipp Witte, Michael Wyatt, Can Xu, Jiahang Xu, Sonali Yadav, Fan Yang, Ziyi Yang, Donghan Yu, Chengruidong Zhang, Cyril Zhang, Jianwen Zhang, Li Lyna Zhang, Yi Zhang, Yue Zhang, Yunan Zhang, Xiren Zhou.
+1. **[PVT](https://huggingface.co/docs/transformers/main/model_doc/pvt)** (from Nanjing University, The University of Hong Kong etc.) released with the paper [Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions](https://arxiv.org/pdf/2102.12122.pdf) by Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lu, Ping Luo, Ling Shao.
+1. **PyAnnote** released in the repository [pyannote/pyannote-audio](https://github.com/pyannote/pyannote-audio) by Hervé Bredin.
1. **[Qwen2](https://huggingface.co/docs/transformers/model_doc/qwen2)** (from the Qwen team, Alibaba Group) released with the paper [Qwen Technical Report](https://arxiv.org/abs/2309.16609) by Jinze Bai, Shuai Bai, Yunfei Chu, Zeyu Cui, Kai Dang, Xiaodong Deng, Yang Fan, Wenbin Ge, Yu Han, Fei Huang, Binyuan Hui, Luo Ji, Mei Li, Junyang Lin, Runji Lin, Dayiheng Liu, Gao Liu, Chengqiang Lu, Keming Lu, Jianxin Ma, Rui Men, Xingzhang Ren, Xuancheng Ren, Chuanqi Tan, Sinan Tan, Jianhong Tu, Peng Wang, Shijie Wang, Wei Wang, Shengguang Wu, Benfeng Xu, Jin Xu, An Yang, Hao Yang, Jian Yang, Shusheng Yang, Yang Yao, Bowen Yu, Hongyi Yuan, Zheng Yuan, Jianwei Zhang, Xingxuan Zhang, Yichang Zhang, Zhenru Zhang, Chang Zhou, Jingren Zhou, Xiaohuan Zhou and Tianhang Zhu.
+1. **[Qwen2-VL](https://huggingface.co/docs/transformers/model_doc/qwen2_vl)** (from the Qwen team, Alibaba Group) released with the paper [Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond](https://arxiv.org/abs/2308.12966) by Jinze Bai, Shuai Bai, Shusheng Yang, Shijie Wang, Sinan Tan, Peng Wang, Junyang Lin, Chang Zhou, Jingren Zhou.
1. **[ResNet](https://huggingface.co/docs/transformers/model_doc/resnet)** (from Microsoft Research) released with the paper [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) by Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun.
1. **[RoBERTa](https://huggingface.co/docs/transformers/model_doc/roberta)** (from Facebook), released together with the paper [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://arxiv.org/abs/1907.11692) by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov.
1. **[RoFormer](https://huggingface.co/docs/transformers/model_doc/roformer)** (from ZhuiyiTechnology), released together with the paper [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://arxiv.org/abs/2104.09864) by Jianlin Su and Yu Lu and Shengfeng Pan and Bo Wen and Yunfeng Liu.
+1. **[RT-DETR](https://huggingface.co/docs/transformers/model_doc/rt_detr)** (from Baidu), released together with the paper [DETRs Beat YOLOs on Real-time Object Detection](https://arxiv.org/abs/2304.08069) by Yian Zhao, Wenyu Lv, Shangliang Xu, Jinman Wei, Guanzhong Wang, Qingqing Dang, Yi Liu, Jie Chen.
+1. **Sapiens** (from Meta AI) released with the paper [Sapiens: Foundation for Human Vision Models](https://arxiv.org/pdf/2408.12569) by Rawal Khirodkar, Timur Bagautdinov, Julieta Martinez, Su Zhaoen, Austin James, Peter Selednik, Stuart Anderson, Shunsuke Saito.
1. **[SegFormer](https://huggingface.co/docs/transformers/model_doc/segformer)** (from NVIDIA) released with the paper [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) by Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M. Alvarez, Ping Luo.
1. **[Segment Anything](https://huggingface.co/docs/transformers/model_doc/sam)** (from Meta AI) released with the paper [Segment Anything](https://arxiv.org/pdf/2304.02643v1.pdf) by Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alex Berg, Wan-Yen Lo, Piotr Dollar, Ross Girshick.
1. **[SigLIP](https://huggingface.co/docs/transformers/main/model_doc/siglip)** (from Google AI) released with the paper [Sigmoid Loss for Language Image Pre-Training](https://arxiv.org/abs/2303.15343) by Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, Lucas Beyer.
@@ -84,7 +122,10 @@
1. **[UniSpeech](https://huggingface.co/docs/transformers/model_doc/unispeech)** (from Microsoft Research) released with the paper [UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data](https://arxiv.org/abs/2101.07597) by Chengyi Wang, Yu Wu, Yao Qian, Kenichi Kumatani, Shujie Liu, Furu Wei, Michael Zeng, Xuedong Huang.
1. **[UniSpeechSat](https://huggingface.co/docs/transformers/model_doc/unispeech-sat)** (from Microsoft Research) released with the paper [UNISPEECH-SAT: UNIVERSAL SPEECH REPRESENTATION LEARNING WITH SPEAKER AWARE PRE-TRAINING](https://arxiv.org/abs/2110.05752) by Sanyuan Chen, Yu Wu, Chengyi Wang, Zhengyang Chen, Zhuo Chen, Shujie Liu, Jian Wu, Yao Qian, Furu Wei, Jinyu Li, Xiangzhan Yu.
1. **[Vision Transformer (ViT)](https://huggingface.co/docs/transformers/model_doc/vit)** (from Google AI) released with the paper [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) by Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby.
+1. **[ViTMAE](https://huggingface.co/docs/transformers/model_doc/vit_mae)** (from Meta AI) released with the paper [Masked Autoencoders Are Scalable Vision Learners](https://arxiv.org/abs/2111.06377) by Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, Ross Girshick.
1. **[ViTMatte](https://huggingface.co/docs/transformers/model_doc/vitmatte)** (from HUST-VL) released with the paper [ViTMatte: Boosting Image Matting with Pretrained Plain Vision Transformers](https://arxiv.org/abs/2305.15272) by Jingfeng Yao, Xinggang Wang, Shusheng Yang, Baoyuan Wang.
+1. **[ViTMSN](https://huggingface.co/docs/transformers/model_doc/vit_msn)** (from Meta AI) released with the paper [Masked Siamese Networks for Label-Efficient Learning](https://arxiv.org/abs/2204.07141) by Mahmoud Assran, Mathilde Caron, Ishan Misra, Piotr Bojanowski, Florian Bordes, Pascal Vincent, Armand Joulin, Michael Rabbat, Nicolas Ballas.
+1. **[ViTPose](https://huggingface.co/docs/transformers/model_doc/vitpose)** (from The University of Sydney) released with the paper [ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation](https://arxiv.org/abs/2204.12484) by Yufei Xu, Jing Zhang, Qiming Zhang, Dacheng Tao.
1. **[VITS](https://huggingface.co/docs/transformers/model_doc/vits)** (from Kakao Enterprise) released with the paper [Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech](https://arxiv.org/abs/2106.06103) by Jaehyeon Kim, Jungil Kong, Juhee Son.
1. **[Wav2Vec2](https://huggingface.co/docs/transformers/model_doc/wav2vec2)** (from Facebook AI) released with the paper [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://arxiv.org/abs/2006.11477) by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli.
1. **[Wav2Vec2-BERT](https://huggingface.co/docs/transformers/main/model_doc/wav2vec2-bert)** (from Meta AI) released with the paper [Seamless: Multilingual Expressive and Streaming Speech Translation](https://ai.meta.com/research/publications/seamless-multilingual-expressive-and-streaming-speech-translation/) by the Seamless Communication team.
diff --git a/docs/source/_toctree.yml b/docs/source/_toctree.yml
index 1fe9150f6..d0b622528 100644
--- a/docs/source/_toctree.yml
+++ b/docs/source/_toctree.yml
@@ -23,10 +23,14 @@
title: Server-side Inference in Node.js
title: Tutorials
- sections:
+ - local: guides/webgpu
+ title: Running models on WebGPU
+ - local: guides/dtypes
+ title: Using quantized models (dtypes)
- local: guides/private
title: Accessing Private/Gated Models
- local: guides/node-audio-processing
- title: Server-side Audio Processing in Node.js
+ title: Server-side Audio Processing
title: Developer Guides
- sections:
- local: api/transformers
@@ -48,6 +52,21 @@
title: ONNX
title: Backends
isExpanded: false
+ - sections:
+ - local: api/generation/parameters
+ title: Parameters
+ - local: api/generation/configuration_utils
+ title: Configuration
+ - local: api/generation/logits_process
+ title: Logits Processors
+ - local: api/generation/logits_sampler
+ title: Logits Samplers
+ - local: api/generation/stopping_criteria
+ title: Stopping Criteria
+ - local: api/generation/streamers
+ title: Streamers
+ title: Generation
+ isExpanded: false
- sections:
- local: api/utils/core
title: Core
@@ -61,8 +80,6 @@
title: Tensor
- local: api/utils/maths
title: Maths
- - local: api/utils/generation
- title: Generation
- local: api/utils/data-structures
title: Data Structures
title: Utilities
diff --git a/docs/source/guides/dtypes.md b/docs/source/guides/dtypes.md
new file mode 100644
index 000000000..a479e1f95
--- /dev/null
+++ b/docs/source/guides/dtypes.md
@@ -0,0 +1,130 @@
+# Using quantized models (dtypes)
+
+Before Transformers.js v3, we used the `quantized` option to specify whether to use a quantized (q8) or full-precision (fp32) variant of the model by setting `quantized` to `true` or `false`, respectively. Now, we've added the ability to select from a much larger list with the `dtype` parameter.
+
+The list of available quantizations depends on the model, but some common ones are: full-precision (`"fp32"`), half-precision (`"fp16"`), 8-bit (`"q8"`, `"int8"`, `"uint8"`), and 4-bit (`"q4"`, `"bnb4"`, `"q4f16"`).
+
+
+
+## Basic usage
+
+**Example:** Run Qwen2.5-0.5B-Instruct in 4-bit quantization ([demo](https://v2.scrimba.com/s0dlcpv0ci))
+
+```js
+import { pipeline } from "@huggingface/transformers";
+
+// Create a text generation pipeline
+const generator = await pipeline(
+ "text-generation",
+ "onnx-community/Qwen2.5-0.5B-Instruct",
+ { dtype: "q4", device: "webgpu" },
+);
+
+// Define the list of messages
+const messages = [
+ { role: "system", content: "You are a helpful assistant." },
+ { role: "user", content: "Tell me a funny joke." },
+];
+
+// Generate a response
+const output = await generator(messages, { max_new_tokens: 128 });
+console.log(output[0].generated_text.at(-1).content);
+```
+
+## Per-module dtypes
+
+Some encoder-decoder models, like Whisper or Florence-2, are extremely sensitive to quantization settings: especially of the encoder. For this reason, we added the ability to select per-module dtypes, which can be done by providing a mapping from module name to dtype.
+
+**Example:** Run Florence-2 on WebGPU ([demo](https://v2.scrimba.com/s0pdm485fo))
+
+```js
+import { Florence2ForConditionalGeneration } from "@huggingface/transformers";
+
+const model = await Florence2ForConditionalGeneration.from_pretrained(
+ "onnx-community/Florence-2-base-ft",
+ {
+ dtype: {
+ embed_tokens: "fp16",
+ vision_encoder: "fp16",
+ encoder_model: "q4",
+ decoder_model_merged: "q4",
+ },
+ device: "webgpu",
+ },
+);
+```
+
+
+
+
+
+
+
+See full code example
+
+
+```js
+import {
+ Florence2ForConditionalGeneration,
+ AutoProcessor,
+ AutoTokenizer,
+ RawImage,
+} from "@huggingface/transformers";
+
+// Load model, processor, and tokenizer
+const model_id = "onnx-community/Florence-2-base-ft";
+const model = await Florence2ForConditionalGeneration.from_pretrained(
+ model_id,
+ {
+ dtype: {
+ embed_tokens: "fp16",
+ vision_encoder: "fp16",
+ encoder_model: "q4",
+ decoder_model_merged: "q4",
+ },
+ device: "webgpu",
+ },
+);
+const processor = await AutoProcessor.from_pretrained(model_id);
+const tokenizer = await AutoTokenizer.from_pretrained(model_id);
+
+// Load image and prepare vision inputs
+const url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg";
+const image = await RawImage.fromURL(url);
+const vision_inputs = await processor(image);
+
+// Specify task and prepare text inputs
+const task = "";
+const prompts = processor.construct_prompts(task);
+const text_inputs = tokenizer(prompts);
+
+// Generate text
+const generated_ids = await model.generate({
+ ...text_inputs,
+ ...vision_inputs,
+ max_new_tokens: 100,
+});
+
+// Decode generated text
+const generated_text = tokenizer.batch_decode(generated_ids, {
+ skip_special_tokens: false,
+})[0];
+
+// Post-process the generated text
+const result = processor.post_process_generation(
+ generated_text,
+ task,
+ image.size,
+);
+console.log(result);
+// { '': 'A green car is parked in front of a tan building. The building has a brown door and two brown windows. The car is a two door and the door is closed. The green car has black tires.' }
+```
+
+
diff --git a/docs/source/guides/node-audio-processing.md b/docs/source/guides/node-audio-processing.md
index 88d93df2d..1b9e3cfea 100644
--- a/docs/source/guides/node-audio-processing.md
+++ b/docs/source/guides/node-audio-processing.md
@@ -13,7 +13,7 @@ This tutorial will be written as an ES module, but you can easily adapt it to us
**Useful links:**
-- [Source code](https://github.com/xenova/transformers.js/tree/main/examples/node-audio-processing)
+- [Source code](https://github.com/huggingface/transformers.js/tree/main/examples/node-audio-processing)
- [Documentation](https://huggingface.co/docs/transformers.js)
@@ -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
```
@@ -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';
```
diff --git a/docs/source/guides/private.md b/docs/source/guides/private.md
index a687e1789..6715f0d4e 100644
--- a/docs/source/guides/private.md
+++ b/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');
diff --git a/docs/source/guides/webgpu.md b/docs/source/guides/webgpu.md
new file mode 100644
index 000000000..9c3009671
--- /dev/null
+++ b/docs/source/guides/webgpu.md
@@ -0,0 +1,87 @@
+# Running models on WebGPU
+
+WebGPU is a new web standard for accelerated graphics and compute. The [API](https://developer.mozilla.org/en-US/docs/Web/API/WebGPU_API) enables web developers to use the underlying system's GPU to carry out high-performance computations directly in the browser. WebGPU is the successor to [WebGL](https://developer.mozilla.org/en-US/docs/Web/API/WebGL_API) and provides significantly better performance, because it allows for more direct interaction with modern GPUs. Lastly, it supports general-purpose GPU computations, which makes it just perfect for machine learning!
+
+> [!WARNING]
+> As of October 2024, global WebGPU support is around 70% (according to [caniuse.com](https://caniuse.com/webgpu)), meaning some users may not be able to use the API.
+>
+> If the following demos do not work in your browser, you may need to enable it using a feature flag:
+>
+> - Firefox: with the `dom.webgpu.enabled` flag (see [here](https://developer.mozilla.org/en-US/docs/Mozilla/Firefox/Experimental_features#:~:text=tested%20by%20Firefox.-,WebGPU%20API,-The%20WebGPU%20API)).
+> - Safari: with the `WebGPU` feature flag (see [here](https://webkit.org/blog/14879/webgpu-now-available-for-testing-in-safari-technology-preview/)).
+> - Older Chromium browsers (on Windows, macOS, Linux): with the `enable-unsafe-webgpu` flag (see [here](https://developer.chrome.com/docs/web-platform/webgpu/troubleshooting-tips)).
+
+## Usage in Transformers.js v3
+
+Thanks to our collaboration with [ONNX Runtime Web](https://www.npmjs.com/package/onnxruntime-web), enabling WebGPU acceleration is as simple as setting `device: 'webgpu'` when loading a model. Let's see some examples!
+
+**Example:** Compute text embeddings on WebGPU ([demo](https://v2.scrimba.com/s06a2smeej))
+
+```js
+import { pipeline } from "@huggingface/transformers";
+
+// Create a feature-extraction pipeline
+const extractor = await pipeline(
+ "feature-extraction",
+ "mixedbread-ai/mxbai-embed-xsmall-v1",
+ { device: "webgpu" },
+);
+
+// Compute embeddings
+const texts = ["Hello world!", "This is an example sentence."];
+const embeddings = await extractor(texts, { pooling: "mean", normalize: true });
+console.log(embeddings.tolist());
+// [
+// [-0.016986183822155, 0.03228696808218956, -0.0013630966423079371, ... ],
+// [0.09050482511520386, 0.07207386940717697, 0.05762749910354614, ... ],
+// ]
+```
+
+**Example:** Perform automatic speech recognition with OpenAI whisper on WebGPU ([demo](https://v2.scrimba.com/s0oi76h82g))
+
+```js
+import { pipeline } from "@huggingface/transformers";
+
+// Create automatic speech recognition pipeline
+const transcriber = await pipeline(
+ "automatic-speech-recognition",
+ "onnx-community/whisper-tiny.en",
+ { device: "webgpu" },
+);
+
+// Transcribe audio from a URL
+const url = "https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/jfk.wav";
+const output = await transcriber(url);
+console.log(output);
+// { text: ' And so my fellow Americans ask not what your country can do for you, ask what you can do for your country.' }
+```
+
+**Example:** Perform image classification with MobileNetV4 on WebGPU ([demo](https://v2.scrimba.com/s0fv2uab1t))
+
+```js
+import { pipeline } from "@huggingface/transformers";
+
+// Create image classification pipeline
+const classifier = await pipeline(
+ "image-classification",
+ "onnx-community/mobilenetv4_conv_small.e2400_r224_in1k",
+ { device: "webgpu" },
+);
+
+// Classify an image from a URL
+const url = "https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/tiger.jpg";
+const output = await classifier(url);
+console.log(output);
+// [
+// { label: 'tiger, Panthera tigris', score: 0.6149784922599792 },
+// { label: 'tiger cat', score: 0.30281734466552734 },
+// { label: 'tabby, tabby cat', score: 0.0019135422771796584 },
+// { label: 'lynx, catamount', score: 0.0012161266058683395 },
+// { label: 'Egyptian cat', score: 0.0011465961579233408 }
+// ]
+```
+
+## Reporting bugs and providing feedback
+
+Due to the experimental nature of WebGPU, especially in non-Chromium browsers, you may experience issues when trying to run a model (even it it can run in WASM). If you do, please open [an issue on GitHub](https://github.com/huggingface/transformers.js/issues/new?title=[WebGPU]%20Error%20running%20MODEL_GOES_HERE&assignees=&labels=bug,webgpu&projects=&template=1_bug-report.yml) and we'll do our best to address it. Thanks!
+
diff --git a/docs/source/index.md b/docs/source/index.md
index 1b94c115f..6551e303f 100644
--- a/docs/source/index.md
+++ b/docs/source/index.md
@@ -35,7 +35,7 @@ The documentation is organized into 4 sections:
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)).
diff --git a/docs/source/pipelines.md b/docs/source/pipelines.md
index 93f4ee216..1e2e1d439 100644
--- a/docs/source/pipelines.md
+++ b/docs/source/pipelines.md
@@ -14,9 +14,9 @@ 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');
+const classifier = await pipeline('sentiment-analysis');
```
When running for the first time, the `pipeline` will download and cache the default pretrained model associated with the task. This can take a while, but subsequent calls will be much faster.
@@ -30,14 +30,14 @@ By default, models will be downloaded from the [Hugging Face Hub](https://huggin
You can now use the classifier on your target text by calling it as a function:
```javascript
-let result = await classifier('I love transformers!');
+const result = await classifier('I love transformers!');
// [{'label': 'POSITIVE', 'score': 0.9998}]
```
If you have multiple inputs, you can pass them as an array:
```javascript
-let result = await classifier(['I love transformers!', 'I hate transformers!']);
+const result = await classifier(['I love transformers!', 'I hate transformers!']);
// [{'label': 'POSITIVE', 'score': 0.9998}, {'label': 'NEGATIVE', 'score': 0.9982}]
```
@@ -46,9 +46,9 @@ You can also specify a different model to use for the pipeline by passing it as
```javascript
-let reviewer = await pipeline('sentiment-analysis', 'Xenova/bert-base-multilingual-uncased-sentiment');
+const reviewer = await pipeline('sentiment-analysis', 'Xenova/bert-base-multilingual-uncased-sentiment');
-let result = await reviewer('The Shawshank Redemption is a true masterpiece of cinema.');
+const result = await reviewer('The Shawshank Redemption is a true masterpiece of cinema.');
// [{label: '5 stars', score: 0.8167929649353027}]
```
@@ -59,10 +59,10 @@ The `pipeline()` function is a great way to quickly use a pretrained model for i
```javascript
// Allocate a pipeline for Automatic Speech Recognition
-let transcriber = await pipeline('automatic-speech-recognition', 'Xenova/whisper-small.en');
+const transcriber = await pipeline('automatic-speech-recognition', 'Xenova/whisper-small.en');
// Transcribe an audio file, loaded from a URL.
-let result = await transcriber('https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/mlk.flac');
+const result = await transcriber('https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/mlk.flac');
// {text: ' I have a dream that one day this nation will rise up and live out the true meaning of its creed.'}
```
@@ -86,7 +86,7 @@ You can also specify which revision of the model to use, by passing a `revision`
Since the Hugging Face Hub uses a git-based versioning system, you can use any valid git revision specifier (e.g., branch name or commit hash)
```javascript
-let transcriber = await pipeline('automatic-speech-recognition', 'Xenova/whisper-tiny.en', {
+const transcriber = await pipeline('automatic-speech-recognition', 'Xenova/whisper-tiny.en', {
revision: 'output_attentions',
});
```
@@ -100,17 +100,17 @@ Many pipelines have additional options that you can specify. For example, when u
```javascript
// Allocation a pipeline for translation
-let translator = await pipeline('translation', 'Xenova/nllb-200-distilled-600M');
+const translator = await pipeline('translation', 'Xenova/nllb-200-distilled-600M');
// Translate from English to Greek
-let result = await translator('I like to walk my dog.', {
+const result = await translator('I like to walk my dog.', {
src_lang: 'eng_Latn',
tgt_lang: 'ell_Grek'
});
// [ { translation_text: 'Μου αρέσει να περπατάω το σκυλί μου.' } ]
// Translate back to English
-let result2 = await translator(result[0].translation_text, {
+const result2 = await translator(result[0].translation_text, {
src_lang: 'ell_Grek',
tgt_lang: 'eng_Latn'
});
@@ -125,8 +125,8 @@ For example, to generate a poem using `LaMini-Flan-T5-783M`, you can do:
```javascript
// Allocate a pipeline for text2text-generation
-let poet = await pipeline('text2text-generation', 'Xenova/LaMini-Flan-T5-783M');
-let result = await poet('Write me a love poem about cheese.', {
+const poet = await pipeline('text2text-generation', 'Xenova/LaMini-Flan-T5-783M');
+const result = await poet('Write me a love poem about cheese.', {
max_new_tokens: 200,
temperature: 0.9,
repetition_penalty: 2.0,
@@ -148,6 +148,70 @@ Cheddar is my go-to for any occasion or mood;
It adds depth and richness without being overpowering its taste buds alone
```
+### Streaming
+
+Some pipelines such as `text-generation` or `automatic-speech-recognition` support streaming output. This is achieved using the `TextStreamer` class. For example, when using a chat model like `Qwen2.5-Coder-0.5B-Instruct`, you can specify a callback function that will be called with each generated token text (if unset, new tokens will be printed to the console).
+
+```js
+import { pipeline, TextStreamer } from "@huggingface/transformers";
+
+// Create a text generation pipeline
+const generator = await pipeline(
+ "text-generation",
+ "onnx-community/Qwen2.5-Coder-0.5B-Instruct",
+ { dtype: "q4" },
+);
+
+// Define the list of messages
+const messages = [
+ { role: "system", content: "You are a helpful assistant." },
+ { role: "user", content: "Write a quick sort algorithm." },
+];
+
+// Create text streamer
+const streamer = new TextStreamer(generator.tokenizer, {
+ skip_prompt: true,
+ // Optionally, do something with the text (e.g., write to a textbox)
+ // callback_function: (text) => { /* Do something with text */ },
+})
+
+// Generate a response
+const result = await generator(messages, { max_new_tokens: 512, do_sample: false, streamer });
+```
+
+Logging `result[0].generated_text` to the console gives:
+
+
+
+Click to view the console output
+
+Here's a simple implementation of the quick sort algorithm in Python:
+```python
+def quick_sort(arr):
+ if len(arr) <= 1:
+ return arr
+ pivot = arr[len(arr) // 2]
+ left = [x for x in arr if x < pivot]
+ middle = [x for x in arr if x == pivot]
+ right = [x for x in arr if x > pivot]
+ return quick_sort(left) + middle + quick_sort(right)
+# Example usage:
+arr = [3, 6, 8, 10, 1, 2]
+sorted_arr = quick_sort(arr)
+print(sorted_arr)
+```
+### Explanation:
+- **Base Case**: If the array has less than or equal to one element (i.e., `len(arr)` is less than or equal to `1`), it is already sorted and can be returned as is.
+- **Pivot Selection**: The pivot is chosen as the middle element of the array.
+- **Partitioning**: The array is partitioned into three parts: elements less than the pivot (`left`), elements equal to the pivot (`middle`), and elements greater than the pivot (`right`). These partitions are then recursively sorted.
+- **Recursive Sorting**: The subarrays are sorted recursively using `quick_sort`.
+This approach ensures that each recursive call reduces the problem size by half until it reaches a base case.
+
+
+
+This streaming feature allows you to process the output as it is generated, rather than waiting for the entire output to be generated before processing it.
+
+
For more information on the available options for each pipeline, refer to the [API Reference](./api/pipelines).
If you would like more control over the inference process, you can use the [`AutoModel`](./api/models), [`AutoTokenizer`](./api/tokenizers), or [`AutoProcessor`](./api/processors) classes instead.
diff --git a/docs/source/tutorials/browser-extension.md b/docs/source/tutorials/browser-extension.md
index a5fd391bd..a8853c5d4 100644
--- a/docs/source/tutorials/browser-extension.md
+++ b/docs/source/tutorials/browser-extension.md
@@ -1,4 +1,4 @@
# Building a browser extension
-*Full tutorial coming soon...* In the meantime, check out the example application: https://github.com/xenova/transformers.js/tree/main/examples/extension
+*Full tutorial coming soon...* In the meantime, check out the example application: https://github.com/huggingface/transformers.js/tree/main/examples/extension
diff --git a/docs/source/tutorials/electron.md b/docs/source/tutorials/electron.md
index 6962e4b7e..5fb3650c1 100644
--- a/docs/source/tutorials/electron.md
+++ b/docs/source/tutorials/electron.md
@@ -1,3 +1,3 @@
# Building an Electron application
-*Full tutorial coming soon...* In the meantime, check out the example application: https://github.com/xenova/transformers.js/tree/main/examples/electron
+*Full tutorial coming soon...* In the meantime, check out the example application: https://github.com/huggingface/transformers.js/tree/main/examples/electron
diff --git a/docs/source/tutorials/next.md b/docs/source/tutorials/next.md
index b3bcff659..0c8c70279 100644
--- a/docs/source/tutorials/next.md
+++ b/docs/source/tutorials/next.md
@@ -9,7 +9,7 @@ The final product will look something like this:
Useful links:
- Demo site: [client-side](https://huggingface.co/spaces/Xenova/next-example-app) or [server-side](https://huggingface.co/spaces/Xenova/next-server-example-app)
-- Source code: [client-side](https://github.com/xenova/transformers.js/tree/main/examples/next-client) or [server-side](https://github.com/xenova/transformers.js/tree/main/examples/next-server)
+- Source code: [client-side](https://github.com/huggingface/transformers.js/tree/main/examples/next-client) or [server-side](https://github.com/huggingface/transformers.js/tree/main/examples/next-server)
## Prerequisites
@@ -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).
@@ -413,7 +413,7 @@ Visit the URL shown in the terminal (e.g., [http://localhost:3000/](http://local
For this demo, we will build and deploy our application to [Hugging Face Spaces](https://huggingface.co/docs/hub/spaces). If you haven't already, you can create a free Hugging Face account [here](https://huggingface.co/join).
-1. Create a new `Dockerfile` in your project's root folder. You can use our [example Dockerfile](https://github.com/xenova/transformers.js/blob/main/examples/next-server/Dockerfile) as a template.
+1. Create a new `Dockerfile` in your project's root folder. You can use our [example Dockerfile](https://github.com/huggingface/transformers.js/blob/main/examples/next-server/Dockerfile) as a template.
2. Visit [https://huggingface.co/new-space](https://huggingface.co/new-space) and fill in the form. Remember to select "Docker" as the space type (you can choose the "Blank" Docker template).
3. Click the "Create space" button at the bottom of the page.
4. Go to "Files" → "Add file" → "Upload files". Drag the files from your project folder (excluding `node_modules` and `.next`, if present) into the upload box and click "Upload". After they have uploaded, scroll down to the button and click "Commit changes to main".
diff --git a/docs/source/tutorials/node.md b/docs/source/tutorials/node.md
index 2d9e3adf4..7cc5cc6be 100644
--- a/docs/source/tutorials/node.md
+++ b/docs/source/tutorials/node.md
@@ -19,7 +19,7 @@ Although you can always use the [Python library](https://github.com/huggingface/
**Useful links:**
-- Source code ([ESM](https://github.com/xenova/transformers.js/tree/main/examples/node/esm/app.js) or [CommonJS](https://github.com/xenova/transformers.js/tree/main/examples/node/commonjs/app.js))
+- Source code ([ESM](https://github.com/huggingface/transformers.js/tree/main/examples/node/esm/app.js) or [CommonJS](https://github.com/huggingface/transformers.js/tree/main/examples/node/commonjs/app.js))
- [Documentation](https://huggingface.co/docs/transformers.js)
@@ -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';
diff --git a/docs/source/tutorials/react.md b/docs/source/tutorials/react.md
index ab50d4de9..e617d8a05 100644
--- a/docs/source/tutorials/react.md
+++ b/docs/source/tutorials/react.md
@@ -7,7 +7,7 @@ In this tutorial, we'll be building a simple React application that performs mul
Useful links:
- [Demo site](https://huggingface.co/spaces/Xenova/react-translator)
-- [Source code](https://github.com/xenova/transformers.js/tree/main/examples/react-translator)
+- [Source code](https://github.com/huggingface/transformers.js/tree/main/examples/react-translator)
## Prerequisites
@@ -44,10 +44,10 @@ You can stop the development server by pressing Ctrl + C 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';
@@ -127,7 +127,7 @@ We recommend starting the development server again with `npm run dev`
First, let's define our components. Create a folder called `components` in the `src` directory, and create the following files:
-1. `LanguageSelector.jsx`: This component will allow the user to select the input and output languages. Check out the full list of languages [here](https://github.com/xenova/transformers.js/blob/main/examples/react-translator/src/components/LanguageSelector.jsx).
+1. `LanguageSelector.jsx`: This component will allow the user to select the input and output languages. Check out the full list of languages [here](https://github.com/huggingface/transformers.js/blob/main/examples/react-translator/src/components/LanguageSelector.jsx).
```jsx
const LANGUAGES = {
"Acehnese (Arabic script)": "ace_Arab",
diff --git a/docs/source/tutorials/vanilla-js.md b/docs/source/tutorials/vanilla-js.md
index 976f79d44..58e336f12 100644
--- a/docs/source/tutorials/vanilla-js.md
+++ b/docs/source/tutorials/vanilla-js.md
@@ -10,7 +10,7 @@ Useful links:
- [Demo site](https://huggingface.co/spaces/Scrimba/vanilla-js-object-detector)
- [Interactive code walk-through (scrim)](https://scrimba.com/scrim/cKm9bDAg)
-- [Source code](https://github.com/xenova/transformers.js/tree/main/examples/vanilla-js)
+- [Source code](https://github.com/huggingface/transformers.js/tree/main/examples/vanilla-js)
## Step 1: HTML and CSS setup
@@ -57,7 +57,6 @@ body {
.custom-file-upload {
display: flex;
align-items: center;
- cursor: pointer;
gap: 10px;
border: 2px solid black;
padding: 8px 16px;
@@ -105,7 +104,7 @@ 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:
@@ -124,7 +123,7 @@ const status = document.getElementById("status");
## Step 3: Create an object detection pipeline
-We’re finally ready to create our object detection pipeline! As a reminder, a [pipeline](./pipelines). is a high-level interface provided by the library to perform a specific task. In our case, we will instantiate an object detection pipeline with the `pipeline()` helper function.
+We’re finally ready to create our object detection pipeline! As a reminder, a [pipeline](../pipelines). is a high-level interface provided by the library to perform a specific task. In our case, we will instantiate an object detection pipeline with the `pipeline()` helper function.
Since this can take some time (especially the first time when we have to download the ~40MB model), we first update the `status` paragraph so that the user knows that we’re about to load the model.
@@ -269,14 +268,19 @@ The bounding box and label span also need some styling, so add the following to
```css
.bounding-box {
- position: absolute;
- box-sizing: border-box;
+ position: absolute;
+ box-sizing: border-box;
+ border-width: 2px;
+ border-style: solid;
}
.bounding-box-label {
- position: absolute;
- color: white;
- font-size: 12px;
+ color: white;
+ position: absolute;
+ font-size: 12px;
+ margin-top: -16px;
+ margin-left: -2px;
+ padding: 1px;
}
```
diff --git a/examples/adaptive-retrieval/package-lock.json b/examples/adaptive-retrieval/package-lock.json
index 5ba3dfb24..0acdc4309 100644
--- a/examples/adaptive-retrieval/package-lock.json
+++ b/examples/adaptive-retrieval/package-lock.json
@@ -1,11 +1,11 @@
{
- "name": "sentence-similarity",
+ "name": "adaptive-retrieval",
"version": "0.0.0",
"lockfileVersion": 3,
"requires": true,
"packages": {
"": {
- "name": "sentence-similarity",
+ "name": "adaptive-retrieval",
"version": "0.0.0",
"dependencies": {
"@xenova/transformers": "^2.15.0",
@@ -1703,12 +1703,12 @@
}
},
"node_modules/braces": {
- "version": "3.0.2",
- "resolved": "https://registry.npmjs.org/braces/-/braces-3.0.2.tgz",
- "integrity": "sha512-b8um+L1RzM3WDSzvhm6gIz1yfTbBt6YTlcEKAvsmqCZZFw46z626lVj9j1yEPW33H5H+lBQpZMP1k8l+78Ha0A==",
+ "version": "3.0.3",
+ "resolved": "https://registry.npmjs.org/braces/-/braces-3.0.3.tgz",
+ "integrity": "sha512-yQbXgO/OSZVD2IsiLlro+7Hf6Q18EJrKSEsdoMzKePKXct3gvD8oLcOQdIzGupr5Fj+EDe8gO/lxc1BzfMpxvA==",
"dev": true,
"dependencies": {
- "fill-range": "^7.0.1"
+ "fill-range": "^7.1.1"
},
"engines": {
"node": ">=8"
@@ -2703,9 +2703,9 @@
}
},
"node_modules/fill-range": {
- "version": "7.0.1",
- "resolved": "https://registry.npmjs.org/fill-range/-/fill-range-7.0.1.tgz",
- "integrity": "sha512-qOo9F+dMUmC2Lcb4BbVvnKJxTPjCm+RRpe4gDuGrzkL7mEVl/djYSu2OdQ2Pa302N4oqkSg9ir6jaLWJ2USVpQ==",
+ "version": "7.1.1",
+ "resolved": "https://registry.npmjs.org/fill-range/-/fill-range-7.1.1.tgz",
+ "integrity": "sha512-YsGpe3WHLK8ZYi4tWDg2Jy3ebRz2rXowDxnld4bkQB00cc/1Zw9AWnC0i9ztDJitivtQvaI9KaLyKrc+hBW0yg==",
"dev": true,
"dependencies": {
"to-regex-range": "^5.0.1"
diff --git a/examples/code-completion/package-lock.json b/examples/code-completion/package-lock.json
index 197127c59..d6832028b 100644
--- a/examples/code-completion/package-lock.json
+++ b/examples/code-completion/package-lock.json
@@ -997,11 +997,12 @@
}
},
"node_modules/braces": {
- "version": "3.0.2",
+ "version": "3.0.3",
+ "resolved": "https://registry.npmjs.org/braces/-/braces-3.0.3.tgz",
+ "integrity": "sha512-yQbXgO/OSZVD2IsiLlro+7Hf6Q18EJrKSEsdoMzKePKXct3gvD8oLcOQdIzGupr5Fj+EDe8gO/lxc1BzfMpxvA==",
"dev": true,
- "license": "MIT",
"dependencies": {
- "fill-range": "^7.0.1"
+ "fill-range": "^7.1.1"
},
"engines": {
"node": ">=8"
@@ -2228,9 +2229,10 @@
}
},
"node_modules/fill-range": {
- "version": "7.0.1",
+ "version": "7.1.1",
+ "resolved": "https://registry.npmjs.org/fill-range/-/fill-range-7.1.1.tgz",
+ "integrity": "sha512-YsGpe3WHLK8ZYi4tWDg2Jy3ebRz2rXowDxnld4bkQB00cc/1Zw9AWnC0i9ztDJitivtQvaI9KaLyKrc+hBW0yg==",
"dev": true,
- "license": "MIT",
"dependencies": {
"to-regex-range": "^5.0.1"
},
@@ -2766,8 +2768,9 @@
},
"node_modules/is-number": {
"version": "7.0.0",
+ "resolved": "https://registry.npmjs.org/is-number/-/is-number-7.0.0.tgz",
+ "integrity": "sha512-41Cifkg6e8TylSpdtTpeLVMqvSBEVzTttHvERD741+pnZ8ANv0004MRL43QKPDlK9cGvNp6NZWZUBlbGXYxxng==",
"dev": true,
- "license": "MIT",
"engines": {
"node": ">=0.12.0"
}
@@ -4470,8 +4473,9 @@
},
"node_modules/to-regex-range": {
"version": "5.0.1",
+ "resolved": "https://registry.npmjs.org/to-regex-range/-/to-regex-range-5.0.1.tgz",
+ "integrity": "sha512-65P7iz6X5yEr1cwcgvQxbbIw7Uk3gOy5dIdtZ4rDveLqhrdJP+Li/Hx6tyK0NEb+2GCyneCMJiGqrADCSNk8sQ==",
"dev": true,
- "license": "MIT",
"dependencies": {
"is-number": "^7.0.0"
},
diff --git a/examples/code-completion/src/App.jsx b/examples/code-completion/src/App.jsx
index a532f7299..7fc84f538 100644
--- a/examples/code-completion/src/App.jsx
+++ b/examples/code-completion/src/App.jsx
@@ -162,7 +162,7 @@ function App() {
Powerful vision foundation model running locally in your browser.
+
+
+
+
+
+ You are about to download Florence-2-base-ft,
+ a 230 million parameter vision foundation model that uses a prompt-based approach to handle a wide range of vision and vision-language tasks like captioning, object detection, and segmentation.
+ Once loaded, the model (340 MB) will be cached and reused when you revisit the page.
+
+ Everything runs locally in your browser using 🤗 Transformers.js and ONNX Runtime Web,
+ meaning no API calls are made to a server for inference. You can even disconnect from the internet after the model has loaded!
+
+ Drag & drop or click to select an image
+ {
+ e.stopPropagation();
+ setImagePreview(EXAMPLE_URL);
+ onImageChange(null, EXAMPLE_URL);
+ }}>(or try an example)
+
+ 
-
+ 
+ 
+