Merge branch 'main' into ort-improvements

Author: xenova

README.md

@@ -47,7 +47,7 @@ npm i @huggingface/transformers
 Alternatively, you can use it in vanilla JS, without any bundler, by using a CDN or static hosting. For example, using [ES Modules](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Modules), you can import the library with:
 ```html
 <script type="module">
-    import { pipeline } from 'https://cdn.jsdelivr.net/npm/@huggingface/[email protected].0';
+    import { pipeline } from 'https://cdn.jsdelivr.net/npm/@huggingface/[email protected].2';
 </script>
 ```
 
@@ -155,7 +155,7 @@ Check out the Transformers.js [template](https://huggingface.co/new-space?templa
 
 
 
-By default, Transformers.js uses [hosted pretrained models](https://huggingface.co/models?library=transformers.js) and [precompiled WASM binaries](https://cdn.jsdelivr.net/npm/@huggingface/[email protected].0/dist/), which should work out-of-the-box. You can customize this as follows:
+By default, Transformers.js uses [hosted pretrained models](https://huggingface.co/models?library=transformers.js) and [precompiled WASM binaries](https://cdn.jsdelivr.net/npm/@huggingface/[email protected].2/dist/), which should work out-of-the-box. You can customize this as follows:
 
 ### Settings
 
@@ -323,6 +323,7 @@ You can refine your search by selecting the task you're interested in (e.g., [te
 1. **[DPT](https://huggingface.co/docs/transformers/master/model_doc/dpt)** (from Intel Labs) released with the paper [Vision Transformers for Dense Prediction](https://huggingface.co/papers/2103.13413) by René Ranftl, Alexey Bochkovskiy, Vladlen Koltun.
 1. **[EfficientNet](https://huggingface.co/docs/transformers/model_doc/efficientnet)** (from Google Brain) released with the paper [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://huggingface.co/papers/1905.11946) by Mingxing Tan, Quoc V. Le.
 1. **[ELECTRA](https://huggingface.co/docs/transformers/model_doc/electra)** (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders as discriminators rather than generators](https://huggingface.co/papers/2003.10555) by Kevin Clark, Minh-Thang Luong, Quoc V. Le, Christopher D. Manning.
+1. **ERNIE-4.5** (from Baidu ERNIE Team) released with the blog post [Announcing the Open Source Release of the ERNIE 4.5 Model Family](https://ernie.baidu.com/blog/posts/ernie4.5/) by the Baidu ERNIE Team.
 1. **[ESM](https://huggingface.co/docs/transformers/model_doc/esm)** (from Meta AI) are transformer protein language models.  **ESM-1b** was released with the paper [Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences](https://www.pnas.org/content/118/15/e2016239118) by Alexander Rives, Joshua Meier, Tom Sercu, Siddharth Goyal, Zeming Lin, Jason Liu, Demi Guo, Myle Ott, C. Lawrence Zitnick, Jerry Ma, and Rob Fergus. **ESM-1v** was released with the paper [Language models enable zero-shot prediction of the effects of mutations on protein function](https://doi.org/10.1101/2021.07.09.450648) by Joshua Meier, Roshan Rao, Robert Verkuil, Jason Liu, Tom Sercu and Alexander Rives. **ESM-2 and ESMFold** were released with the paper [Language models of protein sequences at the scale of evolution enable accurate structure prediction](https://doi.org/10.1101/2022.07.20.500902) by Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Allan dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Sal Candido, Alexander Rives.
 1. **EXAONE** (from LG AI Research) released with the papers [EXAONE 3.0 7.8B Instruction Tuned Language Model](https://huggingface.co/papers/2408.03541) and [EXAONE 3.5: Series of Large Language Models for Real-world Use Cases](https://huggingface.co/papers/2412.04862) by the LG AI Research team.
 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.
@@ -385,6 +386,7 @@ You can refine your search by selecting the task you're interested in (e.g., [te
 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://huggingface.co/papers/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 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://huggingface.co/papers/2010.11934) by Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, Colin Raffel.
+1. **NeoBERT** (from Chandar Research Lab) released with the paper [NeoBERT: A Next-Generation BERT](https://huggingface.co/papers/2502.19587) by Lola Le Breton, Quentin Fournier, Mariam El Mezouar, John X. Morris, Sarath Chandar.
 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://huggingface.co/papers/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://huggingface.co/papers/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://huggingface.co/papers/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.
@@ -414,6 +416,7 @@ You can refine your search by selecting the task you're interested in (e.g., [te
 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://huggingface.co/papers/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://huggingface.co/papers/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://huggingface.co/papers/2303.15343) by Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, Lucas Beyer.
+1. **[SmolLM3](https://huggingface.co/docs/transformers/main/model_doc/smollm3) (from Hugging Face) released with the blog post [SmolLM3: smol, multilingual, long-context reasoner](https://huggingface.co/blog/smollm3) by the Hugging Face TB Research team.
 1. **[SmolVLM](https://huggingface.co/docs/transformers/main/model_doc/smolvlm) (from Hugging Face) released with the blog posts [SmolVLM - small yet mighty Vision Language Model](https://huggingface.co/blog/smolvlm) and [SmolVLM Grows Smaller – Introducing the 250M & 500M Models!](https://huggingface.co/blog/smolervlm) by the Hugging Face TB Research team.
 1. **SNAC** (from Papla Media, ETH Zurich) released with the paper [SNAC: Multi-Scale Neural Audio Codec](https://huggingface.co/papers/2410.14411) by Hubert Siuzdak, Florian Grötschla, Luca A. Lanzendörfer.
 1. **[SpeechT5](https://huggingface.co/docs/transformers/model_doc/speecht5)** (from Microsoft Research) released with the paper [SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing](https://huggingface.co/papers/2110.07205) by Junyi Ao, Rui Wang, Long Zhou, Chengyi Wang, Shuo Ren, Yu Wu, Shujie Liu, Tom Ko, Qing Li, Yu Zhang, Zhihua Wei, Yao Qian, Jinyu Li, Furu Wei.

docs/snippets/2_installation.snippet

@@ -7,6 +7,6 @@ npm i @huggingface/transformers
 Alternatively, you can use it in vanilla JS, without any bundler, by using a CDN or static hosting. For example, using [ES Modules](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Modules), you can import the library with:
 ```html
 <script type="module">
-    import { pipeline } from 'https://cdn.jsdelivr.net/npm/@huggingface/[email protected].0';
+    import { pipeline } from 'https://cdn.jsdelivr.net/npm/@huggingface/[email protected].2';
 </script>
 ```

docs/snippets/4_custom-usage.snippet

@@ -1,6 +1,6 @@
 
 
-By default, Transformers.js uses [hosted pretrained models](https://huggingface.co/models?library=transformers.js) and [precompiled WASM binaries](https://cdn.jsdelivr.net/npm/@huggingface/[email protected].0/dist/), which should work out-of-the-box. You can customize this as follows:
+By default, Transformers.js uses [hosted pretrained models](https://huggingface.co/models?library=transformers.js) and [precompiled WASM binaries](https://cdn.jsdelivr.net/npm/@huggingface/[email protected].2/dist/), which should work out-of-the-box. You can customize this as follows:
 
 ### Settings
 

docs/snippets/6_supported-models.snippet

@@ -37,6 +37,7 @@
 1. **[DPT](https://huggingface.co/docs/transformers/master/model_doc/dpt)** (from Intel Labs) released with the paper [Vision Transformers for Dense Prediction](https://huggingface.co/papers/2103.13413) by René Ranftl, Alexey Bochkovskiy, Vladlen Koltun.
 1. **[EfficientNet](https://huggingface.co/docs/transformers/model_doc/efficientnet)** (from Google Brain) released with the paper [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://huggingface.co/papers/1905.11946) by Mingxing Tan, Quoc V. Le.
 1. **[ELECTRA](https://huggingface.co/docs/transformers/model_doc/electra)** (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders as discriminators rather than generators](https://huggingface.co/papers/2003.10555) by Kevin Clark, Minh-Thang Luong, Quoc V. Le, Christopher D. Manning.
+1. **ERNIE-4.5** (from Baidu ERNIE Team) released with the blog post [Announcing the Open Source Release of the ERNIE 4.5 Model Family](https://ernie.baidu.com/blog/posts/ernie4.5/) by the Baidu ERNIE Team.
 1. **[ESM](https://huggingface.co/docs/transformers/model_doc/esm)** (from Meta AI) are transformer protein language models.  **ESM-1b** was released with the paper [Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences](https://www.pnas.org/content/118/15/e2016239118) by Alexander Rives, Joshua Meier, Tom Sercu, Siddharth Goyal, Zeming Lin, Jason Liu, Demi Guo, Myle Ott, C. Lawrence Zitnick, Jerry Ma, and Rob Fergus. **ESM-1v** was released with the paper [Language models enable zero-shot prediction of the effects of mutations on protein function](https://doi.org/10.1101/2021.07.09.450648) by Joshua Meier, Roshan Rao, Robert Verkuil, Jason Liu, Tom Sercu and Alexander Rives. **ESM-2 and ESMFold** were released with the paper [Language models of protein sequences at the scale of evolution enable accurate structure prediction](https://doi.org/10.1101/2022.07.20.500902) by Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Allan dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Sal Candido, Alexander Rives.
 1. **EXAONE** (from LG AI Research) released with the papers [EXAONE 3.0 7.8B Instruction Tuned Language Model](https://huggingface.co/papers/2408.03541) and [EXAONE 3.5: Series of Large Language Models for Real-world Use Cases](https://huggingface.co/papers/2412.04862) by the LG AI Research team.
 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.
@@ -99,6 +100,7 @@
 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://huggingface.co/papers/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 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://huggingface.co/papers/2010.11934) by Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, Colin Raffel.
+1. **NeoBERT** (from Chandar Research Lab) released with the paper [NeoBERT: A Next-Generation BERT](https://huggingface.co/papers/2502.19587) by Lola Le Breton, Quentin Fournier, Mariam El Mezouar, John X. Morris, Sarath Chandar.
 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://huggingface.co/papers/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://huggingface.co/papers/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://huggingface.co/papers/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.
@@ -128,6 +130,7 @@
 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://huggingface.co/papers/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://huggingface.co/papers/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://huggingface.co/papers/2303.15343) by Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, Lucas Beyer.
+1. **[SmolLM3](https://huggingface.co/docs/transformers/main/model_doc/smollm3) (from Hugging Face) released with the blog post [SmolLM3: smol, multilingual, long-context reasoner](https://huggingface.co/blog/smollm3) by the Hugging Face TB Research team.
 1. **[SmolVLM](https://huggingface.co/docs/transformers/main/model_doc/smolvlm) (from Hugging Face) released with the blog posts [SmolVLM - small yet mighty Vision Language Model](https://huggingface.co/blog/smolvlm) and [SmolVLM Grows Smaller – Introducing the 250M & 500M Models!](https://huggingface.co/blog/smolervlm) by the Hugging Face TB Research team.
 1. **SNAC** (from Papla Media, ETH Zurich) released with the paper [SNAC: Multi-Scale Neural Audio Codec](https://huggingface.co/papers/2410.14411) by Hubert Siuzdak, Florian Grötschla, Luca A. Lanzendörfer.
 1. **[SpeechT5](https://huggingface.co/docs/transformers/model_doc/speecht5)** (from Microsoft Research) released with the paper [SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing](https://huggingface.co/papers/2110.07205) by Junyi Ao, Rui Wang, Long Zhou, Chengyi Wang, Shuo Ren, Yu Wu, Shujie Liu, Tom Ko, Qing Li, Yu Zhang, Zhihua Wei, Yao Qian, Jinyu Li, Furu Wei.

package-lock.json

@@ -1,6 +1,6 @@
 {
   "name": "@huggingface/transformers",
-  "version": "3.6.0",
+  "version": "3.6.2",
   "description": "State-of-the-art Machine Learning for the web. Run 🤗 Transformers directly in your browser, with no need for a server!",
   "main": "./src/transformers.js",
   "types": "./types/transformers.d.ts",

package.json

@@ -4,3 +4,4 @@ optimum==1.26.1
 onnx==1.18.0
 tqdm==4.67.1
 onnxslim==0.1.52
+numpy==2.2.6

scripts/requirements.txt

@@ -31,7 +31,7 @@ export class FeatureExtractor extends Callable {
      * 
      * @returns {Promise<FeatureExtractor>} A new instance of the Feature Extractor class.
      */
-    static async from_pretrained(pretrained_model_name_or_path, options) {
+    static async from_pretrained(pretrained_model_name_or_path, options={}) {
         const config = await getModelJSON(pretrained_model_name_or_path, FEATURE_EXTRACTOR_NAME, true, options);
         return new this(config);
     }

src/base/feature_extraction_utils.js

@@ -1098,7 +1098,7 @@ export class ImageProcessor extends Callable {
      * 
      * @returns {Promise<ImageProcessor>} A new instance of the Processor class.
      */
-    static async from_pretrained(pretrained_model_name_or_path, options) {
+    static async from_pretrained(pretrained_model_name_or_path, options={}) {
         const preprocessorConfig = await getModelJSON(pretrained_model_name_or_path, IMAGE_PROCESSOR_NAME, true, options);
         return new this(preprocessorConfig);
     }

src/base/image_processors_utils.js

@@ -148,7 +148,7 @@ export class Processor extends Callable {
      * 
      * @returns {Promise<Processor>} A new instance of the Processor class.
      */
-    static async from_pretrained(pretrained_model_name_or_path, options) {
+    static async from_pretrained(pretrained_model_name_or_path, options={}) {
 
         const [config, components, chat_template] = await Promise.all([
             // TODO: