Use HF papers (#38184)

* Use hf papers

* Hugging Face papers

* doi to hf papers

* style

docs/source/en/model_doc/albert.md

@@ -26,7 +26,7 @@ rendered properly in your Markdown viewer.
 
 ## Overview
 
-The ALBERT model was proposed in [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942) by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma,
+The ALBERT model was proposed in [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://huggingface.co/papers/1909.11942) by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma,
 Radu Soricut. It presents two parameter-reduction techniques to lower memory consumption and increase the training
 speed of BERT:
 

docs/source/en/model_doc/altclip.md

@@ -21,7 +21,7 @@ rendered properly in your Markdown viewer.
 
 # AltCLIP
 
-[AltCLIP](https://huggingface.co/papers/2211.06679v2) replaces the [CLIP](./clip) text encoder with a multilingual XLM-R encoder and aligns image and text representations with teacher learning and contrastive learning.
+[AltCLIP](https://huggingface.co/papers/2211.06679) replaces the [CLIP](./clip) text encoder with a multilingual XLM-R encoder and aligns image and text representations with teacher learning and contrastive learning.
 
 You can find all the original AltCLIP checkpoints under the [AltClip](https://huggingface.co/collections/BAAI/alt-clip-diffusion-66987a97de8525205f1221bf) collection.
 

docs/source/en/model_doc/audio-spectrogram-transformer.md

@@ -24,7 +24,7 @@ rendered properly in your Markdown viewer.
 
 ## Overview
 
-The Audio Spectrogram Transformer model was proposed in [AST: Audio Spectrogram Transformer](https://arxiv.org/abs/2104.01778) by Yuan Gong, Yu-An Chung, James Glass.
+The Audio Spectrogram Transformer model was proposed in [AST: Audio Spectrogram Transformer](https://huggingface.co/papers/2104.01778) by Yuan Gong, Yu-An Chung, James Glass.
 The Audio Spectrogram Transformer applies a [Vision Transformer](vit) to audio, by turning audio into an image (spectrogram). The model obtains state-of-the-art results
 for audio classification.
 
@@ -35,7 +35,7 @@ The abstract from the paper is the following:
 <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/audio_spectogram_transformer_architecture.png"
 alt="drawing" width="600"/>
 
-<small> Audio Spectrogram Transformer architecture. Taken from the <a href="https://arxiv.org/abs/2104.01778">original paper</a>.</small>
+<small> Audio Spectrogram Transformer architecture. Taken from the <a href="https://huggingface.co/papers/2104.01778">original paper</a>.</small>
 
 This model was contributed by [nielsr](https://huggingface.co/nielsr).
 The original code can be found [here](https://github.com/YuanGongND/ast).
@@ -47,7 +47,7 @@ sure the input has mean of 0 and std of 0.5). [`ASTFeatureExtractor`] takes care
 mean and std by default. You can check [`ast/src/get_norm_stats.py`](https://github.com/YuanGongND/ast/blob/master/src/get_norm_stats.py) to see how
 the authors compute the stats for a downstream dataset.
 - Note that the AST needs a low learning rate (the authors use a 10 times smaller learning rate compared to their CNN model proposed in the
-[PSLA paper](https://arxiv.org/abs/2102.01243)) and converges quickly, so please search for a suitable learning rate and learning rate scheduler for your task.
+[PSLA paper](https://huggingface.co/papers/2102.01243)) and converges quickly, so please search for a suitable learning rate and learning rate scheduler for your task.
 
 ### Using Scaled Dot Product Attention (SDPA)
 

docs/source/en/model_doc/autoformer.md

@@ -22,7 +22,7 @@ rendered properly in your Markdown viewer.
 
 ## Overview
 
-The Autoformer model was proposed in [Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting](https://arxiv.org/abs/2106.13008) by Haixu Wu, Jiehui Xu, Jianmin Wang, Mingsheng Long.
+The Autoformer model was proposed in [Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting](https://huggingface.co/papers/2106.13008) by Haixu Wu, Jiehui Xu, Jianmin Wang, Mingsheng Long.
 
 This model augments the Transformer as a deep decomposition architecture, which can progressively decompose the trend and seasonal components during the forecasting process.
 

docs/source/en/model_doc/barthez.md

@@ -25,7 +25,7 @@ rendered properly in your Markdown viewer.
 
 ## Overview
 
-The BARThez model was proposed in [BARThez: a Skilled Pretrained French Sequence-to-Sequence Model](https://arxiv.org/abs/2010.12321) by Moussa Kamal Eddine, Antoine J.-P. Tixier, Michalis Vazirgiannis on 23 Oct,
+The BARThez model was proposed in [BARThez: a Skilled Pretrained French Sequence-to-Sequence Model](https://huggingface.co/papers/2010.12321) by Moussa Kamal Eddine, Antoine J.-P. Tixier, Michalis Vazirgiannis on 23 Oct,
 2020.
 
 The abstract of the paper:

docs/source/en/model_doc/bartpho.md

@@ -25,7 +25,7 @@ rendered properly in your Markdown viewer.
 
 ## Overview
 
-The BARTpho model was proposed in [BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese](https://arxiv.org/abs/2109.09701) by Nguyen Luong Tran, Duong Minh Le and Dat Quoc Nguyen.
+The BARTpho model was proposed in [BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese](https://huggingface.co/papers/2109.09701) by Nguyen Luong Tran, Duong Minh Le and Dat Quoc Nguyen.
 
 The abstract from the paper is the following:
 

docs/source/en/model_doc/beit.md

@@ -25,11 +25,11 @@ rendered properly in your Markdown viewer.
 
 ## Overview
 
-The BEiT model was proposed in [BEiT: BERT Pre-Training of Image Transformers](https://arxiv.org/abs/2106.08254) by
+The BEiT model was proposed in [BEiT: BERT Pre-Training of Image Transformers](https://huggingface.co/papers/2106.08254) by
 Hangbo Bao, Li Dong and Furu Wei. Inspired by BERT, BEiT is the first paper that makes self-supervised pre-training of
 Vision Transformers (ViTs) outperform supervised pre-training. Rather than pre-training the model to predict the class
-of an image (as done in the [original ViT paper](https://arxiv.org/abs/2010.11929)), BEiT models are pre-trained to
-predict visual tokens from the codebook of OpenAI's [DALL-E model](https://arxiv.org/abs/2102.12092) given masked
+of an image (as done in the [original ViT paper](https://huggingface.co/papers/2010.11929)), BEiT models are pre-trained to
+predict visual tokens from the codebook of OpenAI's [DALL-E model](https://huggingface.co/papers/2102.12092) given masked
 patches.
 
 The abstract from the paper is the following:
@@ -76,7 +76,7 @@ contributed by [kamalkraj](https://huggingface.co/kamalkraj). The original code
 <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/beit_architecture.jpg"
 alt="drawing" width="600"/>
 
-<small> BEiT pre-training. Taken from the <a href="https://arxiv.org/abs/2106.08254">original paper.</a> </small>
+<small> BEiT pre-training. Taken from the <a href="https://huggingface.co/papers/2106.08254">original paper.</a> </small>
 
 ### Using Scaled Dot Product Attention (SDPA)
 

docs/source/en/model_doc/bert-generation.md

@@ -24,7 +24,7 @@ rendered properly in your Markdown viewer.
 
 The BertGeneration model is a BERT model that can be leveraged for sequence-to-sequence tasks using
 [`EncoderDecoderModel`] as proposed in [Leveraging Pre-trained Checkpoints for Sequence Generation
-Tasks](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
+Tasks](https://huggingface.co/papers/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
 
 The abstract from the paper is the following:
 

docs/source/en/model_doc/big_bird.md

@@ -26,7 +26,6 @@ rendered properly in your Markdown viewer.
 
 [BigBird](https://huggingface.co/papers/2007.14062) is a transformer model built to handle sequence lengths up to 4096 compared to 512 for [BERT](./bert). Traditional transformers struggle with long inputs because attention gets really expensive as the sequence length grows. BigBird fixes this by using a sparse attention mechanism, which means it doesn’t try to look at everything at once. Instead, it mixes in local attention, random attention, and a few global tokens to process the whole input. This combination gives it the best of both worlds. It keeps the computation efficient while still capturing enough of the sequence to understand it well. Because of this, BigBird is great at tasks involving long documents, like question answering, summarization, and genomic applications.
 
-
 You can find all the original BigBird checkpoints under the [Google](https://huggingface.co/google?search_models=bigbird) organization.
 
 > [!TIP]

docs/source/en/model_doc/bigbird_pegasus.md

@@ -22,7 +22,7 @@ rendered properly in your Markdown viewer.
 
 ## Overview
 
-The BigBird model was proposed in [Big Bird: Transformers for Longer Sequences](https://arxiv.org/abs/2007.14062) by
+The BigBird model was proposed in [Big Bird: Transformers for Longer Sequences](https://huggingface.co/papers/2007.14062) by
 Zaheer, Manzil and Guruganesh, Guru and Dubey, Kumar Avinava and Ainslie, Joshua and Alberti, Chris and Ontanon,
 Santiago and Pham, Philip and Ravula, Anirudh and Wang, Qifan and Yang, Li and others. BigBird, is a sparse-attention
 based transformer which extends Transformer based models, such as BERT to much longer sequences. In addition to sparse

docs/source/en/model_doc/bit.md

@@ -22,7 +22,7 @@ rendered properly in your Markdown viewer.
 
 ## Overview
 
-The BiT model was proposed in [Big Transfer (BiT): General Visual Representation Learning](https://arxiv.org/abs/1912.11370) by Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Joan Puigcerver, Jessica Yung, Sylvain Gelly, Neil Houlsby.
+The BiT model was proposed in [Big Transfer (BiT): General Visual Representation Learning](https://huggingface.co/papers/1912.11370) by Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Joan Puigcerver, Jessica Yung, Sylvain Gelly, Neil Houlsby.
 BiT is a simple recipe for scaling up pre-training of [ResNet](resnet)-like architectures (specifically, ResNetv2). The method results in significant improvements for transfer learning.
 
 The abstract from the paper is the following:
@@ -34,8 +34,8 @@ The original code can be found [here](https://github.com/google-research/big_tra
 
 ## Usage tips
 
-- BiT models are equivalent to ResNetv2 in terms of architecture, except that: 1) all batch normalization layers are replaced by [group normalization](https://arxiv.org/abs/1803.08494),
-2) [weight standardization](https://arxiv.org/abs/1903.10520) is used for convolutional layers. The authors show that the combination of both is useful for training with large batch sizes, and has a significant
+- BiT models are equivalent to ResNetv2 in terms of architecture, except that: 1) all batch normalization layers are replaced by [group normalization](https://huggingface.co/papers/1803.08494),
+2) [weight standardization](https://huggingface.co/papers/1903.10520) is used for convolutional layers. The authors show that the combination of both is useful for training with large batch sizes, and has a significant
 impact on transfer learning.
 
 ## Resources

docs/source/en/model_doc/bitnet.md

@@ -20,7 +20,7 @@ rendered properly in your Markdown viewer.
 
 Trained on a corpus of 4 trillion tokens, this model demonstrates that native 1-bit LLMs can achieve performance comparable to leading open-weight, full-precision models of similar size, while offering substantial advantages in computational efficiency (memory, energy, latency).
 
-➡️ **Technical Report:** [BitNet b1.58 2B4T Technical Report](https://arxiv.org/abs/2504.12285)
+➡️ **Technical Report:** [BitNet b1.58 2B4T Technical Report](https://huggingface.co/papers/2504.12285)
 
 ➡️ **Official Inference Code:** [microsoft/BitNet (bitnet.cpp)](https://github.com/microsoft/BitNet)
 

docs/source/en/model_doc/blenderbot-small.md

@@ -33,7 +33,7 @@ instead be used with [`BlenderbotModel`] and
 
 ## Overview
 
-The Blender chatbot model was proposed in [Recipes for building an open-domain chatbot](https://arxiv.org/pdf/2004.13637.pdf) Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu,
+The Blender chatbot model was proposed in [Recipes for building an open-domain chatbot](https://huggingface.co/papers/2004.13637) Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu,
 Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston on 30 Apr 2020.
 
 The abstract of the paper is the following:

docs/source/en/model_doc/blenderbot.md

@@ -27,7 +27,7 @@ rendered properly in your Markdown viewer.
 
 ## Overview
 
-The Blender chatbot model was proposed in [Recipes for building an open-domain chatbot](https://arxiv.org/pdf/2004.13637.pdf) Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu,
+The Blender chatbot model was proposed in [Recipes for building an open-domain chatbot](https://huggingface.co/papers/2004.13637) Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu,
 Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston on 30 Apr 2020.
 
 The abstract of the paper is the following:
@@ -67,7 +67,7 @@ An example:
 
 ## Implementation Notes
 
-- Blenderbot uses a standard [seq2seq model transformer](https://arxiv.org/pdf/1706.03762.pdf) based architecture.
+- Blenderbot uses a standard [seq2seq model transformer](https://huggingface.co/papers/1706.03762) based architecture.
 - Available checkpoints can be found in the [model hub](https://huggingface.co/models?search=blenderbot).
 - This is the *default* Blenderbot model class. However, some smaller checkpoints, such as
   `facebook/blenderbot_small_90M`, have a different architecture and consequently should be used with

docs/source/en/model_doc/blip-2.md

@@ -22,9 +22,9 @@ rendered properly in your Markdown viewer.
 
 ## Overview
 
-The BLIP-2 model was proposed in [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://arxiv.org/abs/2301.12597) by
+The BLIP-2 model was proposed in [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://huggingface.co/papers/2301.12597) by
 Junnan Li, Dongxu Li, Silvio Savarese, Steven Hoi. BLIP-2 leverages frozen pre-trained image encoders and large language models (LLMs) by training a lightweight, 12-layer Transformer
-encoder in between them, achieving state-of-the-art performance on various vision-language tasks. Most notably, BLIP-2 improves upon [Flamingo](https://arxiv.org/abs/2204.14198), an 80 billion parameter model, by 8.7%
+encoder in between them, achieving state-of-the-art performance on various vision-language tasks. Most notably, BLIP-2 improves upon [Flamingo](https://huggingface.co/papers/2204.14198), an 80 billion parameter model, by 8.7%
 on zero-shot VQAv2 with 54x fewer trainable parameters. 
 
 The abstract from the paper is the following:
@@ -34,7 +34,7 @@ The abstract from the paper is the following:
 <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/blip2_architecture.jpg"
 alt="drawing" width="600"/> 
 
-<small> BLIP-2 architecture. Taken from the <a href="https://arxiv.org/abs/2301.12597">original paper.</a> </small>
+<small> BLIP-2 architecture. Taken from the <a href="https://huggingface.co/papers/2301.12597">original paper.</a> </small>
 
 This model was contributed by [nielsr](https://huggingface.co/nielsr).
 The original code can be found [here](https://github.com/salesforce/LAVIS/tree/5ee63d688ba4cebff63acee04adaef2dee9af207).

docs/source/en/model_doc/blip.md

@@ -23,7 +23,7 @@ rendered properly in your Markdown viewer.
 
 ## Overview
 
-The BLIP model was proposed in [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://arxiv.org/abs/2201.12086) by Junnan Li, Dongxu Li, Caiming Xiong, Steven Hoi.
+The BLIP model was proposed in [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://huggingface.co/papers/2201.12086) by Junnan Li, Dongxu Li, Caiming Xiong, Steven Hoi.
 
 BLIP is a model that is able to perform various multi-modal tasks including:
 - Visual Question Answering 

docs/source/en/model_doc/bort.md

@@ -34,7 +34,7 @@ You can do so by running the following command: `pip install -U transformers==4.
 
 ## Overview
 
-The BORT model was proposed in [Optimal Subarchitecture Extraction for BERT](https://arxiv.org/abs/2010.10499) by
+The BORT model was proposed in [Optimal Subarchitecture Extraction for BERT](https://huggingface.co/papers/2010.10499) by
 Adrian de Wynter and Daniel J. Perry. It is an optimal subset of architectural parameters for the BERT, which the
 authors refer to as "Bort".
 

docs/source/en/model_doc/bridgetower.md

@@ -22,7 +22,7 @@ rendered properly in your Markdown viewer.
 
 ## Overview
 
-The BridgeTower model was proposed in [BridgeTower: Building Bridges Between Encoders in Vision-Language Representative Learning](https://arxiv.org/abs/2206.08657) by Xiao Xu, Chenfei Wu, Shachar Rosenman, Vasudev Lal, Wanxiang Che, Nan Duan. The goal of this model is to build a
+The BridgeTower model was proposed in [BridgeTower: Building Bridges Between Encoders in Vision-Language Representative Learning](https://huggingface.co/papers/2206.08657) by Xiao Xu, Chenfei Wu, Shachar Rosenman, Vasudev Lal, Wanxiang Che, Nan Duan. The goal of this model is to build a
 bridge between each uni-modal encoder and the cross-modal encoder to enable comprehensive and detailed interaction at each layer of the cross-modal encoder thus achieving remarkable performance on various downstream tasks with almost negligible additional performance and computational costs.
 
 This paper has been accepted to the [AAAI'23](https://aaai.org/Conferences/AAAI-23/) conference. 
@@ -39,7 +39,7 @@ Notably, when further scaling the model, BRIDGETOWER achieves an accuracy of 81.
 <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/bridgetower_architecture%20.jpg"
 alt="drawing" width="600"/>
 
-<small> BridgeTower architecture. Taken from the <a href="https://arxiv.org/abs/2206.08657">original paper.</a> </small>
+<small> BridgeTower architecture. Taken from the <a href="https://huggingface.co/papers/2206.08657">original paper.</a> </small>
 
 This model was contributed by [Anahita Bhiwandiwalla](https://huggingface.co/anahita-b), [Tiep Le](https://huggingface.co/Tile) and [Shaoyen Tseng](https://huggingface.co/shaoyent). The original code can be found [here](https://github.com/microsoft/BridgeTower).
 
@@ -126,7 +126,7 @@ Tips:
 
 - This implementation of BridgeTower uses [`RobertaTokenizer`] to generate text embeddings and OpenAI's CLIP/ViT model to compute visual embeddings.
 - Checkpoints for pre-trained [bridgeTower-base](https://huggingface.co/BridgeTower/bridgetower-base) and [bridgetower masked language modeling and image text matching](https://huggingface.co/BridgeTower/bridgetower-base-itm-mlm) are released.
-- Please refer to [Table 5](https://arxiv.org/pdf/2206.08657.pdf) for BridgeTower's performance on Image Retrieval and other down stream tasks.
+- Please refer to [Table 5](https://huggingface.co/papers/2206.08657) for BridgeTower's performance on Image Retrieval and other down stream tasks.
 - The PyTorch version of this model is only available in torch 1.10 and higher.
 
 

docs/source/en/model_doc/bros.md

@@ -18,7 +18,7 @@ specific language governing permissions and limitations under the License.
 
 ## Overview
 
-The BROS model was proposed in [BROS: A Pre-trained Language Model Focusing on Text and Layout for Better Key Information Extraction from Documents](https://arxiv.org/abs/2108.04539) by Teakgyu Hong, Donghyun Kim, Mingi Ji, Wonseok Hwang, Daehyun Nam, Sungrae Park.
+The BROS model was proposed in [BROS: A Pre-trained Language Model Focusing on Text and Layout for Better Key Information Extraction from Documents](https://huggingface.co/papers/2108.04539) by Teakgyu Hong, Donghyun Kim, Mingi Ji, Wonseok Hwang, Daehyun Nam, Sungrae Park.
 
 BROS stands for *BERT Relying On Spatiality*. It is an encoder-only Transformer model that takes a sequence of tokens and their bounding boxes as inputs and outputs a sequence of hidden states. BROS encode relative spatial information instead of using absolute spatial information.
 

docs/source/en/model_doc/camembert.md

@@ -24,7 +24,7 @@ rendered properly in your Markdown viewer.
 
 ## Overview
 
-The CamemBERT model was proposed in [CamemBERT: a Tasty French Language Model](https://arxiv.org/abs/1911.03894) by
+The CamemBERT model was proposed in [CamemBERT: a Tasty French Language Model](https://huggingface.co/papers/1911.03894) by
 [Louis Martin](https://huggingface.co/louismartin), [Benjamin Muller](https://huggingface.co/benjamin-mlr), [Pedro Javier Ortiz Suárez](https://huggingface.co/pjox), Yoann Dupont, Laurent Romary, Éric Villemonte de la
 Clergerie, [Djamé Seddah](https://huggingface.co/Djame), and [Benoît Sagot](https://huggingface.co/sagot). It is based on Facebook's RoBERTa model released in 2019. It is a model
 trained on 138GB of French text.

docs/source/en/model_doc/chameleon.md

@@ -25,7 +25,7 @@ rendered properly in your Markdown viewer.
 ## Overview
 
 The Chameleon model was proposed in [Chameleon: Mixed-Modal Early-Fusion Foundation Models
-](https://arxiv.org/abs/2405.09818v1) by META AI Chameleon Team. Chameleon is a Vision-Language Model that use vector quantization to tokenize images which enables the model to generate multimodal output. The model takes images and texts as input, including an interleaved format, and generates textual response. Image generation module is not released yet.
+](https://huggingface.co/papers/2405.09818) by META AI Chameleon Team. Chameleon is a Vision-Language Model that use vector quantization to tokenize images which enables the model to generate multimodal output. The model takes images and texts as input, including an interleaved format, and generates textual response. Image generation module is not released yet.
 
 
 The abstract from the paper is the following:
@@ -46,7 +46,7 @@ text. Chameleon marks a significant step forward in unified modeling of full mul
 <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/chameleon_arch.png"
 alt="drawing" width="600"/>
 
-<small> Chameleon incorporates a vector quantizer module to transform images into discrete tokens. That also enables image generation using an auto-regressive transformer. Taken from the <a href="https://arxiv.org/abs/2405.09818v1">original paper.</a> </small>
+<small> Chameleon incorporates a vector quantizer module to transform images into discrete tokens. That also enables image generation using an auto-regressive transformer. Taken from the <a href="https://huggingface.co/papers/2405.09818">original paper.</a> </small>
 
 This model was contributed by [joaogante](https://huggingface.co/joaogante) and [RaushanTurganbay](https://huggingface.co/RaushanTurganbay).
 The original code can be found [here](https://github.com/facebookresearch/chameleon).

docs/source/en/model_doc/chinese_clip.md

@@ -22,7 +22,7 @@ rendered properly in your Markdown viewer.
 
 ## Overview
 
-The Chinese-CLIP model was proposed in [Chinese CLIP: Contrastive Vision-Language Pretraining in Chinese](https://arxiv.org/abs/2211.01335) by An Yang, Junshu Pan, Junyang Lin, Rui Men, Yichang Zhang, Jingren Zhou, Chang Zhou.
+The Chinese-CLIP model was proposed in [Chinese CLIP: Contrastive Vision-Language Pretraining in Chinese](https://huggingface.co/papers/2211.01335) by An Yang, Junshu Pan, Junyang Lin, Rui Men, Yichang Zhang, Jingren Zhou, Chang Zhou.
 Chinese-CLIP is an implementation of CLIP (Radford et al., 2021) on a large-scale dataset of Chinese image-text pairs. It is capable of performing cross-modal retrieval and also playing as a vision backbone for vision tasks like zero-shot image classification, open-domain object detection, etc. The original Chinese-CLIP code is released [at this link](https://github.com/OFA-Sys/Chinese-CLIP).
 
 The abstract from the paper is the following:

docs/source/en/model_doc/clap.md

@@ -23,7 +23,7 @@ rendered properly in your Markdown viewer.
 ## Overview
 
 The CLAP model was proposed in [Large Scale Contrastive Language-Audio pretraining with
-feature fusion and keyword-to-caption augmentation](https://arxiv.org/pdf/2211.06687.pdf) by Yusong Wu, Ke Chen, Tianyu Zhang, Yuchen Hui, Taylor Berg-Kirkpatrick, Shlomo Dubnov.
+feature fusion and keyword-to-caption augmentation](https://huggingface.co/papers/2211.06687) by Yusong Wu, Ke Chen, Tianyu Zhang, Yuchen Hui, Taylor Berg-Kirkpatrick, Shlomo Dubnov.
 
 CLAP (Contrastive Language-Audio Pretraining) is a neural network trained on a variety of (audio, text) pairs. It can be instructed in to predict the most relevant text snippet, given an audio, without directly optimizing for the task. The CLAP model uses a SWINTransformer to get audio features from a log-Mel spectrogram input, and a RoBERTa model to get text features. Both the text and audio features are then projected to a latent space with identical dimension. The dot product between the projected audio and text features is then used as a similar score.
 

docs/source/en/model_doc/clipseg.md

@@ -22,7 +22,7 @@ rendered properly in your Markdown viewer.
 
 ## Overview
 
-The CLIPSeg model was proposed in [Image Segmentation Using Text and Image Prompts](https://arxiv.org/abs/2112.10003) by Timo Lüddecke
+The CLIPSeg model was proposed in [Image Segmentation Using Text and Image Prompts](https://huggingface.co/papers/2112.10003) by Timo Lüddecke
 and Alexander Ecker. CLIPSeg adds a minimal decoder on top of a frozen [CLIP](clip) model for zero-shot and one-shot image segmentation.
 
 The abstract from the paper is the following:
@@ -48,7 +48,7 @@ to generalized queries involving affordances or properties*
 <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/clipseg_architecture.png"
 alt="drawing" width="600"/> 
 
-<small> CLIPSeg overview. Taken from the <a href="https://arxiv.org/abs/2112.10003">original paper.</a> </small>
+<small> CLIPSeg overview. Taken from the <a href="https://huggingface.co/papers/2112.10003">original paper.</a> </small>
 
 This model was contributed by [nielsr](https://huggingface.co/nielsr).
 The original code can be found [here](https://github.com/timojl/clipseg).

docs/source/en/model_doc/clvp.md

@@ -22,7 +22,7 @@ rendered properly in your Markdown viewer.
 
 ## Overview
 
-The CLVP (Contrastive Language-Voice Pretrained Transformer) model was proposed in [Better speech synthesis through scaling](https://arxiv.org/abs/2305.07243) by James Betker.
+The CLVP (Contrastive Language-Voice Pretrained Transformer) model was proposed in [Better speech synthesis through scaling](https://huggingface.co/papers/2305.07243) by James Betker.
 
 The abstract from the paper is the following:
 

docs/source/en/model_doc/codegen.md

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 ## Overview
 
-The CodeGen model was proposed in [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, and Caiming Xiong.
+The CodeGen model was proposed in [A Conversational Paradigm for Program Synthesis](https://huggingface.co/papers/2203.13474) by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, and Caiming Xiong.
 
 CodeGen is an autoregressive language model for program synthesis trained sequentially on [The Pile](https://pile.eleuther.ai/), BigQuery, and BigPython.
 

docs/source/en/model_doc/colqwen2.md

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 # ColQwen2
 
-[ColQwen2](https://doi.org/10.48550/arXiv.2407.01449) is a variant of the [ColPali](./colpali) model designed to retrieve documents by analyzing their visual features. Unlike traditional systems that rely heavily on text extraction and OCR, ColQwen2 treats each page as an image. It uses the [Qwen2-VL](./qwen2_vl) backbone to capture not only text, but also the layout, tables, charts, and other visual elements to create detailed multi-vector embeddings that can be used for retrieval by computing pairwise late interaction similarity scores. This offers a more comprehensive understanding of documents and enables more efficient and accurate retrieval.
+[ColQwen2](https://huggingface.co/papers/2407.01449) is a variant of the [ColPali](./colpali) model designed to retrieve documents by analyzing their visual features. Unlike traditional systems that rely heavily on text extraction and OCR, ColQwen2 treats each page as an image. It uses the [Qwen2-VL](./qwen2_vl) backbone to capture not only text, but also the layout, tables, charts, and other visual elements to create detailed multi-vector embeddings that can be used for retrieval by computing pairwise late interaction similarity scores. This offers a more comprehensive understanding of documents and enables more efficient and accurate retrieval.
 
 This model was contributed by [@tonywu71](https://huggingface.co/tonywu71) (ILLUIN Technology) and [@yonigozlan](https://huggingface.co/yonigozlan) (HuggingFace).
 

docs/source/en/model_doc/conditional_detr.md

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 ## Overview
 
-The Conditional DETR model was proposed in [Conditional DETR for Fast Training Convergence](https://arxiv.org/abs/2108.06152) by Depu Meng, Xiaokang Chen, Zejia Fan, Gang Zeng, Houqiang Li, Yuhui Yuan, Lei Sun, Jingdong Wang. Conditional DETR presents a conditional cross-attention mechanism for fast DETR training. Conditional DETR converges 6.7× to 10× faster than DETR.
+The Conditional DETR model was proposed in [Conditional DETR for Fast Training Convergence](https://huggingface.co/papers/2108.06152) by Depu Meng, Xiaokang Chen, Zejia Fan, Gang Zeng, Houqiang Li, Yuhui Yuan, Lei Sun, Jingdong Wang. Conditional DETR presents a conditional cross-attention mechanism for fast DETR training. Conditional DETR converges 6.7× to 10× faster than DETR.
 
 The abstract from the paper is the following:
 
@@ -31,7 +31,7 @@ The abstract from the paper is the following:
 <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/conditional_detr_curve.jpg"
 alt="drawing" width="600"/>
 
-<small> Conditional DETR shows much faster convergence compared to the original DETR. Taken from the <a href="https://arxiv.org/abs/2108.06152">original paper</a>.</small>
+<small> Conditional DETR shows much faster convergence compared to the original DETR. Taken from the <a href="https://huggingface.co/papers/2108.06152">original paper</a>.</small>
 
 This model was contributed by [DepuMeng](https://huggingface.co/DepuMeng). The original code can be found [here](https://github.com/Atten4Vis/ConditionalDETR).
 

docs/source/en/model_doc/convbert.md

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 ## Overview
 
-The ConvBERT model was proposed in [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
+The ConvBERT model was proposed in [ConvBERT: Improving BERT with Span-based Dynamic Convolution](https://huggingface.co/papers/2008.02496) by Zihang Jiang, Weihao Yu, Daquan Zhou, Yunpeng Chen, Jiashi Feng, Shuicheng
 Yan.
 
 The abstract from the paper is the following:

docs/source/en/model_doc/convnext.md

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 ## Overview
 
-The ConvNeXT model was proposed in [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.
+The ConvNeXT model was proposed in [A ConvNet for the 2020s](https://huggingface.co/papers/2201.03545) by Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, Saining Xie.
 ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them.
 
 The abstract from the paper is the following:
@@ -40,7 +40,7 @@ and outperforming Swin Transformers on COCO detection and ADE20K segmentation, w
 <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/convnext_architecture.jpg"
 alt="drawing" width="600"/>
 
-<small> ConvNeXT architecture. Taken from the <a href="https://arxiv.org/abs/2201.03545">original paper</a>.</small>
+<small> ConvNeXT architecture. Taken from the <a href="https://huggingface.co/papers/2201.03545">original paper</a>.</small>
 
 This model was contributed by [nielsr](https://huggingface.co/nielsr). TensorFlow version of the model was contributed by [ariG23498](https://github.com/ariG23498),
 [gante](https://github.com/gante), and [sayakpaul](https://github.com/sayakpaul) (equal contribution). The original code can be found [here](https://github.com/facebookresearch/ConvNeXt).

docs/source/en/model_doc/convnextv2.md

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 ## Overview
 
-The ConvNeXt V2 model was proposed in [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.
+The ConvNeXt V2 model was proposed in [ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders](https://huggingface.co/papers/2301.00808) by Sanghyun Woo, Shoubhik Debnath, Ronghang Hu, Xinlei Chen, Zhuang Liu, In So Kweon, Saining Xie.
 ConvNeXt V2 is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, and a successor of [ConvNeXT](convnext).
 
 The abstract from the paper is the following:
@@ -33,7 +33,7 @@ The abstract from the paper is the following:
 <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/convnextv2_architecture.png"
 alt="drawing" width="600"/>
 
-<small> ConvNeXt V2 architecture. Taken from the <a href="https://arxiv.org/abs/2301.00808">original paper</a>.</small>
+<small> ConvNeXt V2 architecture. Taken from the <a href="https://huggingface.co/papers/2301.00808">original paper</a>.</small>
 
 This model was contributed by [adirik](https://huggingface.co/adirik). The original code can be found [here](https://github.com/facebookresearch/ConvNeXt-V2).
 

docs/source/en/model_doc/cpm.md

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 ## Overview
 
-The CPM model was proposed in [CPM: A Large-scale Generative Chinese Pre-trained Language Model](https://arxiv.org/abs/2012.00413) by Zhengyan Zhang, Xu Han, Hao Zhou, Pei Ke, Yuxian Gu, Deming Ye, Yujia Qin,
+The CPM model was proposed in [CPM: A Large-scale Generative Chinese Pre-trained Language Model](https://huggingface.co/papers/2012.00413) by Zhengyan Zhang, Xu Han, Hao Zhou, Pei Ke, Yuxian Gu, Deming Ye, Yujia Qin,
 Yusheng Su, Haozhe Ji, Jian Guan, Fanchao Qi, Xiaozhi Wang, Yanan Zheng, Guoyang Zeng, Huanqi Cao, Shengqi Chen,
 Daixuan Li, Zhenbo Sun, Zhiyuan Liu, Minlie Huang, Wentao Han, Jie Tang, Juanzi Li, Xiaoyan Zhu, Maosong Sun.
 

docs/source/en/model_doc/ctrl.md

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 ## Overview
 
-CTRL model was proposed in [CTRL: A Conditional Transformer Language Model for Controllable Generation](https://arxiv.org/abs/1909.05858) by Nitish Shirish Keskar*, Bryan McCann*, Lav R. Varshney, Caiming Xiong and
+CTRL model was proposed in [CTRL: A Conditional Transformer Language Model for Controllable Generation](https://huggingface.co/papers/1909.05858) by Nitish Shirish Keskar*, Bryan McCann*, Lav R. Varshney, Caiming Xiong and
 Richard Socher. It's a causal (unidirectional) transformer pre-trained using language modeling on a very large corpus
 of ~140 GB of text data with the first token reserved as a control code (such as Links, Books, Wikipedia etc.).
 

docs/source/en/model_doc/cvt.md

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 ## Overview
 
-The CvT model was proposed in [CvT: Introducing Convolutions to Vision Transformers](https://arxiv.org/abs/2103.15808) by Haiping Wu, Bin Xiao, Noel Codella, Mengchen Liu, Xiyang Dai, Lu Yuan and Lei Zhang. The Convolutional vision Transformer (CvT) improves the [Vision Transformer (ViT)](vit) in performance and efficiency by introducing convolutions into ViT to yield the best of both designs.
+The CvT model was proposed in [CvT: Introducing Convolutions to Vision Transformers](https://huggingface.co/papers/2103.15808) by Haiping Wu, Bin Xiao, Noel Codella, Mengchen Liu, Xiyang Dai, Lu Yuan and Lei Zhang. The Convolutional vision Transformer (CvT) improves the [Vision Transformer (ViT)](vit) in performance and efficiency by introducing convolutions into ViT to yield the best of both designs.
 
 The abstract from the paper is the following:
 

docs/source/en/model_doc/d_fine.md

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 ## Overview
 
-The D-FINE model was proposed in [D-FINE: Redefine Regression Task in DETRs as Fine-grained Distribution Refinement](https://arxiv.org/abs/2410.13842) by
+The D-FINE model was proposed in [D-FINE: Redefine Regression Task in DETRs as Fine-grained Distribution Refinement](https://huggingface.co/papers/2410.13842) by
 Yansong Peng, Hebei Li, Peixi Wu, Yueyi Zhang, Xiaoyan Sun, Feng Wu
 
 The abstract from the paper is the following:

docs/source/en/model_doc/dab-detr.md

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 ## Overview
 
-The DAB-DETR model was proposed in [DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETR](https://arxiv.org/abs/2201.12329) by Shilong Liu, Feng Li, Hao Zhang, Xiao Yang, Xianbiao Qi, Hang Su, Jun Zhu, Lei Zhang.
+The DAB-DETR model was proposed in [DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETR](https://huggingface.co/papers/2201.12329) by Shilong Liu, Feng Li, Hao Zhang, Xiao Yang, Xianbiao Qi, Hang Su, Jun Zhu, Lei Zhang.
 DAB-DETR is an enhanced variant of Conditional DETR. It utilizes dynamically updated anchor boxes to provide both a reference query point (x, y) and a reference anchor size (w, h), improving cross-attention computation. This new approach achieves 45.7% AP when trained for 50 epochs with a single ResNet-50 model as the backbone.
 
 <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/dab_detr_convergence_plot.png"

docs/source/en/model_doc/dac.md

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 ## Overview
 
 
-The DAC model was proposed in [Descript Audio Codec: High-Fidelity Audio Compression with Improved RVQGAN](https://arxiv.org/abs/2306.06546) by Rithesh Kumar, Prem Seetharaman, Alejandro Luebs, Ishaan Kumar, Kundan Kumar.
+The DAC model was proposed in [Descript Audio Codec: High-Fidelity Audio Compression with Improved RVQGAN](https://huggingface.co/papers/2306.06546) by Rithesh Kumar, Prem Seetharaman, Alejandro Luebs, Ishaan Kumar, Kundan Kumar.
 
 The Descript Audio Codec (DAC) model is a powerful tool for compressing audio data, making it highly efficient for storage and transmission. By compressing 44.1 KHz audio into tokens at just 8kbps bandwidth, the DAC model enables high-quality audio processing while significantly reducing the data footprint. This is particularly useful in scenarios where bandwidth is limited or storage space is at a premium, such as in streaming applications, remote conferencing, and archiving large audio datasets.
 

docs/source/en/model_doc/data2vec.md

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 ## Overview
 
-The Data2Vec model was proposed in [data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language](https://arxiv.org/pdf/2202.03555) by Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu and Michael Auli.
+The Data2Vec model was proposed in [data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language](https://huggingface.co/papers/2202.03555) by Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu and Michael Auli.
 Data2Vec proposes a unified framework for self-supervised learning across different data modalities - text, audio and images.
 Importantly, predicted targets for pre-training are contextualized latent representations of the inputs, rather than modality-specific, context-independent targets.
 

docs/source/en/model_doc/deberta-v2.md

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 ## Overview
 
-The DeBERTa model was proposed in [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://arxiv.org/abs/2006.03654) by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen It is based on Google's
+The DeBERTa model was proposed in [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://huggingface.co/papers/2006.03654) by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen It is based on Google's
 BERT model released in 2018 and Facebook's RoBERTa model released in 2019.
 
 It builds on RoBERTa with disentangled attention and enhanced mask decoder training with half of the data used in

docs/source/en/model_doc/deberta.md

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 ## Overview
 
-The DeBERTa model was proposed in [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://arxiv.org/abs/2006.03654) by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen It is based on Google's
+The DeBERTa model was proposed in [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://huggingface.co/papers/2006.03654) by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen It is based on Google's
 BERT model released in 2018 and Facebook's RoBERTa model released in 2019.
 
 It builds on RoBERTa with disentangled attention and enhanced mask decoder training with half of the data used in

docs/source/en/model_doc/decision_transformer.md

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 ## Overview
 
-The Decision Transformer model was proposed in [Decision Transformer: Reinforcement Learning via Sequence Modeling](https://arxiv.org/abs/2106.01345)  
+The Decision Transformer model was proposed in [Decision Transformer: Reinforcement Learning via Sequence Modeling](https://huggingface.co/papers/2106.01345)  
 by Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas, Igor Mordatch.
 
 The abstract from the paper is the following:

docs/source/en/model_doc/deepseek_v3.md

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 ## Overview
 
-The DeepSeek-V3 model was proposed in [DeepSeek-V3 Technical Report](https://arxiv.org/abs/2412.19437) by DeepSeek-AI Team.
+The DeepSeek-V3 model was proposed in [DeepSeek-V3 Technical Report](https://huggingface.co/papers/2412.19437) by DeepSeek-AI Team.
 
 The abstract from the paper is the following:
 We present DeepSeek-V3, a strong Mixture-of-Experts (MoE) language model with 671B total parameters with 37B activated for each token. To achieve efficient inference and cost-effective training, DeepSeek-V3 adopts Multi-head Latent Attention (MLA) and DeepSeekMoE architectures, which were thoroughly validated in DeepSeek-V2. Furthermore, DeepSeek-V3 pioneers an auxiliary-loss-free strategy for load balancing and sets a multi-token prediction training objective for stronger performance. We pre-train DeepSeek-V3 on 14.8 trillion diverse and high-quality tokens, followed by Supervised Fine-Tuning and Reinforcement Learning stages to fully harness its capabilities. Comprehensive evaluations reveal that DeepSeek-V3 outperforms other open-source models and achieves performance comparable to leading closed-source models. Despite its excellent performance, DeepSeek-V3 requires only 2.788M H800 GPU hours for its full training. In addition, its training process is remarkably stable. Throughout the entire training process, we did not experience any irrecoverable loss spikes or perform any rollbacks. The model checkpoints are available at https://github.com/deepseek-ai/DeepSeek-V3.

docs/source/en/model_doc/deformable_detr.md

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 ## Overview
 
-The Deformable DETR model was proposed in [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://arxiv.org/abs/2010.04159) by Xizhou Zhu, Weijie Su, Lewei Lu, Bin Li, Xiaogang Wang, Jifeng Dai.
+The Deformable DETR model was proposed in [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://huggingface.co/papers/2010.04159) by Xizhou Zhu, Weijie Su, Lewei Lu, Bin Li, Xiaogang Wang, Jifeng Dai.
 Deformable DETR mitigates the slow convergence issues and limited feature spatial resolution of the original [DETR](detr) by leveraging a new deformable attention module which only attends to a small set of key sampling points around a reference.
 
 The abstract from the paper is the following:
@@ -32,7 +32,7 @@ The abstract from the paper is the following:
 <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/deformable_detr_architecture.png"
 alt="drawing" width="600"/>
 
-<small> Deformable DETR architecture. Taken from the <a href="https://arxiv.org/abs/2010.04159">original paper</a>.</small>
+<small> Deformable DETR architecture. Taken from the <a href="https://huggingface.co/papers/2010.04159">original paper</a>.</small>
 
 This model was contributed by [nielsr](https://huggingface.co/nielsr). The original code can be found [here](https://github.com/fundamentalvision/Deformable-DETR).
 

docs/source/en/model_doc/deit.md

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 ## Overview
 
-The DeiT model was proposed in [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. The [Vision Transformer (ViT)](vit) introduced in [Dosovitskiy et al., 2020](https://arxiv.org/abs/2010.11929) has shown that one can match or even outperform existing convolutional neural
+The DeiT model was proposed in [Training data-efficient image transformers & distillation through attention](https://huggingface.co/papers/2012.12877) by Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre
+Sablayrolles, Hervé Jégou. The [Vision Transformer (ViT)](vit) introduced in [Dosovitskiy et al., 2020](https://huggingface.co/papers/2010.11929) has shown that one can match or even outperform existing convolutional neural
 networks using a Transformer encoder (BERT-like). However, the ViT models introduced in that paper required training on
 expensive infrastructure for multiple weeks, using external data. DeiT (data-efficient image transformers) are more
 efficiently trained transformers for image classification, requiring far less data and far less computing resources

docs/source/en/model_doc/deplot.md

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 ## Overview 
 
-DePlot was proposed in the paper [DePlot: One-shot visual language reasoning by plot-to-table translation](https://arxiv.org/abs/2212.10505) from Fangyu Liu, Julian Martin Eisenschlos, Francesco Piccinno, Syrine Krichene, Chenxi Pang, Kenton Lee, Mandar Joshi, Wenhu Chen, Nigel Collier, Yasemin Altun.
+DePlot was proposed in the paper [DePlot: One-shot visual language reasoning by plot-to-table translation](https://huggingface.co/papers/2212.10505) from Fangyu Liu, Julian Martin Eisenschlos, Francesco Piccinno, Syrine Krichene, Chenxi Pang, Kenton Lee, Mandar Joshi, Wenhu Chen, Nigel Collier, Yasemin Altun.
 
 The abstract of the paper states the following:
 

docs/source/en/model_doc/depth_anything_v2.md

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 ## Overview
 
-Depth Anything V2 was introduced in [the paper of the same name](https://arxiv.org/abs/2406.09414) by Lihe Yang et al. It uses the same architecture as the original [Depth Anything model](depth_anything), but uses synthetic data and a larger capacity teacher model to achieve much finer and robust depth predictions.
+Depth Anything V2 was introduced in [the paper of the same name](https://huggingface.co/papers/2406.09414) by Lihe Yang et al. It uses the same architecture as the original [Depth Anything model](depth_anything), but uses synthetic data and a larger capacity teacher model to achieve much finer and robust depth predictions.
 
 The abstract from the paper is the following:
 
@@ -27,7 +27,7 @@ The abstract from the paper is the following:
 <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/depth_anything_overview.jpg"
 alt="drawing" width="600"/>
 
-<small> Depth Anything overview. Taken from the <a href="https://arxiv.org/abs/2401.10891">original paper</a>.</small>
+<small> Depth Anything overview. Taken from the <a href="https://huggingface.co/papers/2401.10891">original paper</a>.</small>
 
 The Depth Anything models were contributed by [nielsr](https://huggingface.co/nielsr).
 The original code can be found [here](https://github.com/DepthAnything/Depth-Anything-V2).

docs/source/en/model_doc/depth_pro.md

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 ## Overview
 
-The DepthPro model was proposed in [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.
+The DepthPro model was proposed in [Depth Pro: Sharp Monocular Metric Depth in Less Than a Second](https://huggingface.co/papers/2410.02073) by Aleksei Bochkovskii, Amaël Delaunoy, Hugo Germain, Marcel Santos, Yichao Zhou, Stephan R. Richter, Vladlen Koltun.
 
 DepthPro is a foundation model for zero-shot metric monocular depth estimation, designed to generate high-resolution depth maps with remarkable sharpness and fine-grained details. It employs a multi-scale Vision Transformer (ViT)-based architecture, where images are downsampled, divided into patches, and processed using a shared Dinov2 encoder. The extracted patch-level features are merged, upsampled, and refined using a DPT-like fusion stage, enabling precise depth estimation.
 
@@ -78,7 +78,7 @@ The DepthPro model processes an input image by first downsampling it at multiple
 <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/depth_pro_architecture.png"
 alt="drawing" width="600"/>
 
-<small> DepthPro architecture. Taken from the <a href="https://arxiv.org/abs/2410.02073" target="_blank">original paper</a>. </small>
+<small> DepthPro architecture. Taken from the <a href="https://huggingface.co/papers/2410.02073" target="_blank">original paper</a>. </small>
 
 The `DepthProForDepthEstimation` model uses a `DepthProEncoder`, for encoding the input image and a `FeatureFusionStage` for fusing the output features from encoder.
 
@@ -151,7 +151,7 @@ On a local benchmark (A100-40GB, PyTorch 2.3.0, OS Ubuntu 22.04) with `float32`
 
 A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with DepthPro:
 
-- Research Paper: [Depth Pro: Sharp Monocular Metric Depth in Less Than a Second](https://arxiv.org/pdf/2410.02073)
+- Research Paper: [Depth Pro: Sharp Monocular Metric Depth in Less Than a Second](https://huggingface.co/papers/2410.02073)
 - Official Implementation: [apple/ml-depth-pro](https://github.com/apple/ml-depth-pro)
 - DepthPro Inference Notebook: [DepthPro Inference](https://github.com/qubvel/transformers-notebooks/blob/main/notebooks/DepthPro_inference.ipynb)
 - DepthPro for Super Resolution and Image Segmentation

docs/source/en/model_doc/deta.md

@@ -30,7 +30,7 @@ You can do so by running the following command: `pip install -U transformers==4.
 
 ## Overview
 
-The DETA model was proposed in [NMS Strikes Back](https://arxiv.org/abs/2212.06137) by Jeffrey Ouyang-Zhang, Jang Hyun Cho, Xingyi Zhou, Philipp Krähenbühl.
+The DETA model was proposed in [NMS Strikes Back](https://huggingface.co/papers/2212.06137) by Jeffrey Ouyang-Zhang, Jang Hyun Cho, Xingyi Zhou, Philipp Krähenbühl.
 DETA (short for Detection Transformers with Assignment) improves [Deformable DETR](deformable_detr) by replacing the one-to-one bipartite Hungarian matching loss
 with one-to-many label assignments used in traditional detectors with non-maximum suppression (NMS). This leads to significant gains of up to 2.5 mAP.
 
@@ -41,7 +41,7 @@ The abstract from the paper is the following:
 <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/deta_architecture.jpg"
 alt="drawing" width="600"/>
 
-<small> DETA overview. Taken from the <a href="https://arxiv.org/abs/2212.06137">original paper</a>. </small>
+<small> DETA overview. Taken from the <a href="https://huggingface.co/papers/2212.06137">original paper</a>. </small>
 
 This model was contributed by [nielsr](https://huggingface.co/nielsr).
 The original code can be found [here](https://github.com/jozhang97/DETA).

docs/source/en/model_doc/detr.md

@@ -22,7 +22,7 @@ rendered properly in your Markdown viewer.
 
 ## Overview
 
-The DETR model was proposed in [End-to-End Object Detection with Transformers](https://arxiv.org/abs/2005.12872) by
+The DETR model was proposed in [End-to-End Object Detection with Transformers](https://huggingface.co/papers/2005.12872) by
 Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov and Sergey Zagoruyko. DETR
 consists of a convolutional backbone followed by an encoder-decoder Transformer which can be trained end-to-end for
 object detection. It greatly simplifies a lot of the complexity of models like Faster-R-CNN and Mask-R-CNN, which use

docs/source/en/model_doc/dialogpt.md

@@ -25,7 +25,7 @@ rendered properly in your Markdown viewer.
 
 ## Overview
 
-DialoGPT was proposed in [DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://arxiv.org/abs/1911.00536) by Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao,
+DialoGPT was proposed in [DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://huggingface.co/papers/1911.00536) by Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao,
 Jianfeng Gao, Jingjing Liu, Bill Dolan. It's a GPT2 Model trained on 147M conversation-like exchanges extracted from
 Reddit.
 

docs/source/en/model_doc/diffllama.md

@@ -24,7 +24,7 @@ rendered properly in your Markdown viewer.
 
 ## Overview
 
-The DiffLlama model was proposed in [Differential Transformer](https://arxiv.org/abs/2410.05258) by Kazuma Matsumoto and .
+The DiffLlama model was proposed in [Differential Transformer](https://huggingface.co/papers/2410.05258) by Kazuma Matsumoto and .
 This model is combine Llama model and Differential Transformer's Attention.
 
 The abstract from the paper is the following:

docs/source/en/model_doc/dinat.md

@@ -22,7 +22,7 @@ rendered properly in your Markdown viewer.
 
 ## Overview
 
-DiNAT was proposed in [Dilated Neighborhood Attention Transformer](https://arxiv.org/abs/2209.15001)
+DiNAT was proposed in [Dilated Neighborhood Attention Transformer](https://huggingface.co/papers/2209.15001)
 by Ali Hassani and Humphrey Shi.
 
 It extends [NAT](nat) by adding a Dilated Neighborhood Attention pattern to capture global context,
@@ -53,7 +53,7 @@ src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/ma
 alt="drawing" width="600"/>
 
 <small> Neighborhood Attention with different dilation values.
-Taken from the <a href="https://arxiv.org/abs/2209.15001">original paper</a>.</small>
+Taken from the <a href="https://huggingface.co/papers/2209.15001">original paper</a>.</small>
 
 This model was contributed by [Ali Hassani](https://huggingface.co/alihassanijr).
 The original code can be found [here](https://github.com/SHI-Labs/Neighborhood-Attention-Transformer).

docs/source/en/model_doc/dinov2_with_registers.md

@@ -17,7 +17,7 @@ specific language governing permissions and limitations under the License.
 
 ## Overview
 
-The DINOv2 with Registers model was proposed in [Vision Transformers Need Registers](https://arxiv.org/abs/2309.16588) by Timothée Darcet, Maxime Oquab, Julien Mairal, Piotr Bojanowski.
+The DINOv2 with Registers model was proposed in [Vision Transformers Need Registers](https://huggingface.co/papers/2309.16588) by Timothée Darcet, Maxime Oquab, Julien Mairal, Piotr Bojanowski.
 
 The [Vision Transformer](vit) (ViT) is a transformer encoder model (BERT-like) originally introduced to do supervised image classification on ImageNet.
 
@@ -35,7 +35,7 @@ The abstract from the paper is the following:
 <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/dinov2_with_registers_visualization.png"
 alt="drawing" width="600"/>
 
-<small> Visualization of attention maps of various models trained with vs. without registers. Taken from the <a href="https://arxiv.org/abs/2309.16588">original paper</a>. </small>
+<small> Visualization of attention maps of various models trained with vs. without registers. Taken from the <a href="https://huggingface.co/papers/2309.16588">original paper</a>. </small>
 
 Tips:
 

docs/source/en/model_doc/dpr.md

@@ -25,7 +25,7 @@ rendered properly in your Markdown viewer.
 ## Overview
 
 Dense Passage Retrieval (DPR) is a set of tools and models for state-of-the-art open-domain Q&A research. It was
-introduced in [Dense Passage Retrieval for Open-Domain Question Answering](https://arxiv.org/abs/2004.04906) by
+introduced in [Dense Passage Retrieval for Open-Domain Question Answering](https://huggingface.co/papers/2004.04906) by
 Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, Wen-tau Yih.
 
 The abstract from the paper is the following:

docs/source/en/model_doc/dpt.md

@@ -24,7 +24,7 @@ rendered properly in your Markdown viewer.
 
 ## Overview
 
-The DPT model was proposed in [Vision Transformers for Dense Prediction](https://arxiv.org/abs/2103.13413) by René Ranftl, Alexey Bochkovskiy, Vladlen Koltun.
+The DPT model was proposed in [Vision Transformers for Dense Prediction](https://huggingface.co/papers/2103.13413) by René Ranftl, Alexey Bochkovskiy, Vladlen Koltun.
 DPT is a model that leverages the [Vision Transformer (ViT)](vit) as backbone for dense prediction tasks like semantic segmentation and depth estimation.
 
 The abstract from the paper is the following:
@@ -34,7 +34,7 @@ The abstract from the paper is the following:
 <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/dpt_architecture.jpg"
 alt="drawing" width="600"/>
 
-<small> DPT architecture. Taken from the <a href="https://arxiv.org/abs/2103.13413" target="_blank">original paper</a>. </small>
+<small> DPT architecture. Taken from the <a href="https://huggingface.co/papers/2103.13413" target="_blank">original paper</a>. </small>
 
 This model was contributed by [nielsr](https://huggingface.co/nielsr). The original code can be found [here](https://github.com/isl-org/DPT).
 

docs/source/en/model_doc/efficientformer.md

@@ -31,7 +31,7 @@ You can do so by running the following command: `pip install -U transformers==4.
 
 ## Overview
 
-The EfficientFormer model was proposed in [EfficientFormer: Vision Transformers at MobileNet Speed](https://arxiv.org/abs/2206.01191)
+The EfficientFormer model was proposed in [EfficientFormer: Vision Transformers at MobileNet Speed](https://huggingface.co/papers/2206.01191)
 by Yanyu Li, Geng Yuan, Yang Wen, Eric Hu, Georgios Evangelidis, Sergey Tulyakov, Yanzhi Wang, Jian Ren.  EfficientFormer proposes a
 dimension-consistent pure transformer that can be run on mobile devices for dense prediction tasks like image classification, object
 detection and semantic segmentation.

docs/source/en/model_doc/efficientnet.md

@@ -22,7 +22,7 @@ rendered properly in your Markdown viewer.
 
 ## Overview
 
-The EfficientNet model was proposed in [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://arxiv.org/abs/1905.11946) 
+The EfficientNet model was proposed in [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://huggingface.co/papers/1905.11946) 
 by Mingxing Tan and Quoc V. Le. EfficientNets are a family of image classification models, which achieve state-of-the-art accuracy, yet being an order-of-magnitude smaller and faster than previous models.
 
 The abstract from the paper is the following:

docs/source/en/model_doc/emu3.md

@@ -24,7 +24,7 @@ rendered properly in your Markdown viewer.
 
 ## Overview
 
-The Emu3 model was proposed in [Emu3: Next-Token Prediction is All You Need](https://arxiv.org/abs/2409.18869) by Xinlong Wang, Xiaosong Zhang, Zhengxiong Luo, Quan Sun, Yufeng Cui, Jinsheng Wang, Fan Zhang, Yueze Wang, Zhen Li, Qiying Yu, Yingli Zhao, Yulong Ao, Xuebin Min, Tao Li, Boya Wu, Bo Zhao, Bowen Zhang, Liangdong Wang, Guang Liu, Zheqi He, Xi Yang, Jingjing Liu, Yonghua Lin, Tiejun Huang, Zhongyuan Wang.
+The Emu3 model was proposed in [Emu3: Next-Token Prediction is All You Need](https://huggingface.co/papers/2409.18869) by Xinlong Wang, Xiaosong Zhang, Zhengxiong Luo, Quan Sun, Yufeng Cui, Jinsheng Wang, Fan Zhang, Yueze Wang, Zhen Li, Qiying Yu, Yingli Zhao, Yulong Ao, Xuebin Min, Tao Li, Boya Wu, Bo Zhao, Bowen Zhang, Liangdong Wang, Guang Liu, Zheqi He, Xi Yang, Jingjing Liu, Yonghua Lin, Tiejun Huang, Zhongyuan Wang.
 
 Emu3 is a multimodal LLM that uses vector quantization to tokenize images into discrete tokens. Discretized image tokens are later fused with text token ids for image and text generation. The model can additionally generate images by predicting image token ids. 
 

docs/source/en/model_doc/encodec.md

@@ -22,7 +22,7 @@ rendered properly in your Markdown viewer.
 
 ## Overview
 
-The EnCodec neural codec model was proposed in [High Fidelity Neural Audio Compression](https://arxiv.org/abs/2210.13438) by Alexandre Défossez, Jade Copet, Gabriel Synnaeve, Yossi Adi.
+The EnCodec neural codec model was proposed in [High Fidelity Neural Audio Compression](https://huggingface.co/papers/2210.13438) by Alexandre Défossez, Jade Copet, Gabriel Synnaeve, Yossi Adi.
 
 The abstract from the paper is the following:
 

docs/source/en/model_doc/encoder-decoder.md

@@ -30,14 +30,14 @@ The [`EncoderDecoderModel`] can be used to initialize a sequence-to-sequence mod
 pretrained autoencoding model as the encoder and any pretrained autoregressive model as the decoder.
 
 The effectiveness of initializing sequence-to-sequence models with pretrained checkpoints for sequence generation tasks
-was shown in [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://arxiv.org/abs/1907.12461) by
+was shown in [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://huggingface.co/papers/1907.12461) by
 Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
 
 After such an [`EncoderDecoderModel`] has been trained/fine-tuned, it can be saved/loaded just like
 any other models (see the examples for more information).
 
 An application of this architecture could be to leverage two pretrained [`BertModel`] as the encoder
-and decoder for a summarization model as was shown in: [Text Summarization with Pretrained Encoders](https://arxiv.org/abs/1908.08345) by Yang Liu and Mirella Lapata.
+and decoder for a summarization model as was shown in: [Text Summarization with Pretrained Encoders](https://huggingface.co/papers/1908.08345) by Yang Liu and Mirella Lapata.
 
 ## Randomly initializing `EncoderDecoderModel` from model configurations.
 

docs/source/en/model_doc/ernie.md

@@ -22,8 +22,8 @@ rendered properly in your Markdown viewer.
 
 ## Overview
 ERNIE is a series of powerful models proposed by baidu, especially in Chinese tasks,
-including [ERNIE1.0](https://arxiv.org/abs/1904.09223), [ERNIE2.0](https://ojs.aaai.org/index.php/AAAI/article/view/6428),
-[ERNIE3.0](https://arxiv.org/abs/2107.02137), [ERNIE-Gram](https://arxiv.org/abs/2010.12148), [ERNIE-health](https://arxiv.org/abs/2110.07244), etc.
+including [ERNIE1.0](https://huggingface.co/papers/1904.09223), [ERNIE2.0](https://ojs.aaai.org/index.php/AAAI/article/view/6428),
+[ERNIE3.0](https://huggingface.co/papers/2107.02137), [ERNIE-Gram](https://huggingface.co/papers/2010.12148), [ERNIE-health](https://huggingface.co/papers/2110.07244), etc.
 
 These models are contributed by [nghuyong](https://huggingface.co/nghuyong) and the official code can be found in [PaddleNLP](https://github.com/PaddlePaddle/PaddleNLP) (in PaddlePaddle).
 

docs/source/en/model_doc/ernie_m.md

@@ -31,7 +31,7 @@ You can do so by running the following command: `pip install -U transformers==4.
 ## Overview
 
 The ErnieM model was proposed in [ERNIE-M: Enhanced Multilingual Representation by Aligning
-Cross-lingual Semantics with Monolingual Corpora](https://arxiv.org/abs/2012.15674)  by Xuan Ouyang, Shuohuan Wang, Chao Pang, Yu Sun,
+Cross-lingual Semantics with Monolingual Corpora](https://huggingface.co/papers/2012.15674)  by Xuan Ouyang, Shuohuan Wang, Chao Pang, Yu Sun,
 Hao Tian, Hua Wu, Haifeng Wang.
 
 The abstract from the paper is the following:

docs/source/en/model_doc/fastspeech2_conformer.md

@@ -18,7 +18,7 @@ specific language governing permissions and limitations under the License.
 
 ## Overview
 
-The FastSpeech2Conformer model was proposed with the paper [Recent Developments On Espnet Toolkit Boosted By Conformer](https://arxiv.org/abs/2010.13956) by Pengcheng Guo, Florian Boyer, Xuankai Chang, Tomoki Hayashi, Yosuke Higuchi, Hirofumi Inaguma, Naoyuki Kamo, Chenda Li, Daniel Garcia-Romero, Jiatong Shi, Jing Shi, Shinji Watanabe, Kun Wei, Wangyou Zhang, and Yuekai Zhang.
+The FastSpeech2Conformer model was proposed with the paper [Recent Developments On Espnet Toolkit Boosted By Conformer](https://huggingface.co/papers/2010.13956) by Pengcheng Guo, Florian Boyer, Xuankai Chang, Tomoki Hayashi, Yosuke Higuchi, Hirofumi Inaguma, Naoyuki Kamo, Chenda Li, Daniel Garcia-Romero, Jiatong Shi, Jing Shi, Shinji Watanabe, Kun Wei, Wangyou Zhang, and Yuekai Zhang.
 
 The abstract from the original FastSpeech2 paper is the following:
 

docs/source/en/model_doc/flan-t5.md

@@ -25,7 +25,7 @@ rendered properly in your Markdown viewer.
 
 ## Overview
 
-FLAN-T5 was released in the paper [Scaling Instruction-Finetuned Language Models](https://arxiv.org/pdf/2210.11416.pdf) - it is an enhanced version of T5 that has been finetuned in a mixture of tasks.
+FLAN-T5 was released in the paper [Scaling Instruction-Finetuned Language Models](https://huggingface.co/papers/2210.11416) - it is an enhanced version of T5 that has been finetuned in a mixture of tasks.
 
 One can directly use FLAN-T5 weights without finetuning the model:
 

docs/source/en/model_doc/flaubert.md

@@ -23,7 +23,7 @@ rendered properly in your Markdown viewer.
 
 ## Overview
 
-The FlauBERT model was proposed in the paper [FlauBERT: Unsupervised Language Model Pre-training for French](https://arxiv.org/abs/1912.05372) by Hang Le et al. It's a transformer model pretrained using a masked language
+The FlauBERT model was proposed in the paper [FlauBERT: Unsupervised Language Model Pre-training for French](https://huggingface.co/papers/1912.05372) by Hang Le et al. It's a transformer model pretrained using a masked language
 modeling (MLM) objective (like BERT).
 
 The abstract from the paper is the following:

docs/source/en/model_doc/flava.md

@@ -22,7 +22,7 @@ rendered properly in your Markdown viewer.
 
 ## Overview
 
-The FLAVA model was proposed in [FLAVA: A Foundational Language And Vision Alignment Model](https://arxiv.org/abs/2112.04482) by Amanpreet Singh, Ronghang Hu, Vedanuj Goswami, Guillaume Couairon, Wojciech Galuba, Marcus Rohrbach, and Douwe Kiela and is accepted at CVPR 2022.
+The FLAVA model was proposed in [FLAVA: A Foundational Language And Vision Alignment Model](https://huggingface.co/papers/2112.04482) by Amanpreet Singh, Ronghang Hu, Vedanuj Goswami, Guillaume Couairon, Wojciech Galuba, Marcus Rohrbach, and Douwe Kiela and is accepted at CVPR 2022.
 
 The paper aims at creating a single unified foundation model which can work across vision, language
 as well as vision-and-language multimodal tasks.

docs/source/en/model_doc/fnet.md

@@ -22,7 +22,7 @@ rendered properly in your Markdown viewer.
 
 ## Overview
 
-The FNet model was proposed in [FNet: Mixing Tokens with Fourier Transforms](https://arxiv.org/abs/2105.03824) by
+The FNet model was proposed in [FNet: Mixing Tokens with Fourier Transforms](https://huggingface.co/papers/2105.03824) by
 James Lee-Thorp, Joshua Ainslie, Ilya Eckstein, Santiago Ontanon. The model replaces the self-attention layer in a BERT
 model with a fourier transform which returns only the real parts of the transform. The model is significantly faster
 than the BERT model because it has fewer parameters and is more memory efficient. The model achieves about 92-97%

docs/source/en/model_doc/focalnet.md

@@ -22,7 +22,7 @@ rendered properly in your Markdown viewer.
 
 ## Overview
 
-The FocalNet model was proposed in [Focal Modulation Networks](https://arxiv.org/abs/2203.11926) by Jianwei Yang, Chunyuan Li, Xiyang Dai, Lu Yuan, Jianfeng Gao.
+The FocalNet model was proposed in [Focal Modulation Networks](https://huggingface.co/papers/2203.11926) by Jianwei Yang, Chunyuan Li, Xiyang Dai, Lu Yuan, Jianfeng Gao.
 FocalNets completely replace self-attention (used in models like [ViT](vit) and [Swin](swin)) by a focal modulation mechanism for modeling token interactions in vision.
 The authors claim that FocalNets outperform self-attention based models with similar computational costs on the tasks of image classification, object detection, and segmentation.
 

docs/source/en/model_doc/fsmt.md

@@ -18,7 +18,7 @@ rendered properly in your Markdown viewer.
 
 ## Overview
 
-FSMT (FairSeq MachineTranslation) models were introduced in [Facebook FAIR's WMT19 News Translation Task Submission](https://arxiv.org/abs/1907.06616) by Nathan Ng, Kyra Yee, Alexei Baevski, Myle Ott, Michael Auli, Sergey Edunov.
+FSMT (FairSeq MachineTranslation) models were introduced in [Facebook FAIR's WMT19 News Translation Task Submission](https://huggingface.co/papers/1907.06616) by Nathan Ng, Kyra Yee, Alexei Baevski, Myle Ott, Michael Auli, Sergey Edunov.
 
 The abstract of the paper is the following:
 

docs/source/en/model_doc/funnel.md

@@ -24,7 +24,7 @@ rendered properly in your Markdown viewer.
 ## Overview
 
 The Funnel Transformer model was proposed in the paper [Funnel-Transformer: Filtering out Sequential Redundancy for
-Efficient Language Processing](https://arxiv.org/abs/2006.03236). It is a bidirectional transformer model, like
+Efficient Language Processing](https://huggingface.co/papers/2006.03236). It is a bidirectional transformer model, like
 BERT, but with a pooling operation after each block of layers, a bit like in traditional convolutional neural networks
 (CNN) in computer vision.
 

docs/source/en/model_doc/git.md

@@ -22,7 +22,7 @@ rendered properly in your Markdown viewer.
 
 ## Overview
 
-The GIT model was proposed in [GIT: A Generative Image-to-text Transformer for Vision and Language](https://arxiv.org/abs/2205.14100) by
+The GIT model was proposed in [GIT: A Generative Image-to-text Transformer for Vision and Language](https://huggingface.co/papers/2205.14100) by
 Jianfeng Wang, Zhengyuan Yang, Xiaowei Hu, Linjie Li, Kevin Lin, Zhe Gan, Zicheng Liu, Ce Liu, Lijuan Wang. GIT is a decoder-only Transformer
 that leverages [CLIP](clip)'s vision encoder to condition the model on vision inputs besides text. The model obtains state-of-the-art results on
 image captioning and visual question answering benchmarks.
@@ -34,7 +34,7 @@ The abstract from the paper is the following:
 <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/git_architecture.jpg"
 alt="drawing" width="600"/>
 
-<small> GIT architecture. Taken from the <a href="https://arxiv.org/abs/2205.14100" target="_blank">original paper</a>. </small>
+<small> GIT architecture. Taken from the <a href="https://huggingface.co/papers/2205.14100" target="_blank">original paper</a>. </small>
 
 This model was contributed by [nielsr](https://huggingface.co/nielsr).
 The original code can be found [here](https://github.com/microsoft/GenerativeImage2Text).

docs/source/en/model_doc/glm.md

@@ -25,7 +25,7 @@ rendered properly in your Markdown viewer.
 ## Overview
 
 The GLM Model was proposed
-in [ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools](https://arxiv.org/html/2406.12793v1)
+in [ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools](https://huggingface.co/papers/2406.12793)
 by GLM Team, THUDM & ZhipuAI.
 
 The abstract from the paper is the following:

docs/source/en/model_doc/glpn.md

@@ -29,7 +29,7 @@ breaking changes to fix it in the future. If you see something strange, file a [
 
 ## Overview
 
-The GLPN model was proposed in [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.
+The GLPN model was proposed in [Global-Local Path Networks for Monocular Depth Estimation with Vertical CutDepth](https://huggingface.co/papers/2201.07436)  by Doyeon Kim, Woonghyun Ga, Pyungwhan Ahn, Donggyu Joo, Sehwan Chun, Junmo Kim.
 GLPN combines [SegFormer](segformer)'s hierarchical mix-Transformer with a lightweight decoder for monocular depth estimation. The proposed decoder shows better performance than the previously proposed decoders, with considerably
 less computational complexity.
 
@@ -40,7 +40,7 @@ The abstract from the paper is the following:
 <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/glpn_architecture.jpg"
 alt="drawing" width="600"/>
 
-<small> Summary of the approach. Taken from the <a href="https://arxiv.org/abs/2201.07436" target="_blank">original paper</a>. </small>
+<small> Summary of the approach. Taken from the <a href="https://huggingface.co/papers/2201.07436" target="_blank">original paper</a>. </small>
 
 This model was contributed by [nielsr](https://huggingface.co/nielsr). The original code can be found [here](https://github.com/vinvino02/GLPDepth).
 

docs/source/en/model_doc/got_ocr2.md

@@ -22,7 +22,7 @@ rendered properly in your Markdown viewer.
 
 ## Overview
 
-The GOT-OCR2 model was proposed in [General OCR Theory: Towards OCR-2.0 via a Unified End-to-end Model](https://arxiv.org/abs/2409.01704) by Haoran Wei, Chenglong Liu, Jinyue Chen, Jia Wang, Lingyu Kong, Yanming Xu, Zheng Ge, Liang Zhao, Jianjian Sun, Yuang Peng, Chunrui Han, Xiangyu Zhang.
+The GOT-OCR2 model was proposed in [General OCR Theory: Towards OCR-2.0 via a Unified End-to-end Model](https://huggingface.co/papers/2409.01704) by Haoran Wei, Chenglong Liu, Jinyue Chen, Jia Wang, Lingyu Kong, Yanming Xu, Zheng Ge, Liang Zhao, Jianjian Sun, Yuang Peng, Chunrui Han, Xiangyu Zhang.
 
 The abstract from the paper is the following:
 
@@ -31,7 +31,7 @@ The abstract from the paper is the following:
 <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/got_ocr_overview.png"
 alt="drawing" width="600"/>
 
-<small> GOT-OCR2 training stages. Taken from the <a href="https://arxiv.org/abs/2409.01704">original paper.</a> </small>
+<small> GOT-OCR2 training stages. Taken from the <a href="https://huggingface.co/papers/2409.01704">original paper.</a> </small>
 
 
 Tips:

docs/source/en/model_doc/gpt_bigcode.md

@@ -24,7 +24,7 @@ rendered properly in your Markdown viewer.
 
 ## Overview
 
-The GPTBigCode model was proposed in [SantaCoder: don't reach for the stars!](https://arxiv.org/abs/2301.03988) by BigCode. The listed authors are: 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.
+The GPTBigCode model was proposed in [SantaCoder: don't reach for the stars!](https://huggingface.co/papers/2301.03988) by BigCode. The listed authors are: 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.
 
 The abstract from the paper is the following:
 

docs/source/en/model_doc/granite_speech.md

@@ -23,11 +23,11 @@ rendered properly in your Markdown viewer.
 ## Overview
 The Granite Speech model is a multimodal language model, consisting of a speech encoder, speech projector, large language model, and LoRA adapter(s). More details regarding each component for the current (Granite 3.2 Speech) model architecture may be found below.
 
-1. Speech Encoder: A [Conformer](https://arxiv.org/abs/2005.08100) encoder trained with Connectionist Temporal Classification (CTC) on character-level targets on ASR corpora. The encoder uses block-attention and self-conditioned CTC from the middle layer.
+1. Speech Encoder: A [Conformer](https://huggingface.co/papers/2005.08100) encoder trained with Connectionist Temporal Classification (CTC) on character-level targets on ASR corpora. The encoder uses block-attention and self-conditioned CTC from the middle layer.
 
 2. Speech Projector: A query transformer (q-former) operating on the outputs of the last encoder block. The encoder and projector temporally downsample the audio features to be merged into the multimodal embeddings to be processed by the llm.
 
-3. Large Language Model: The Granite Speech model leverages Granite LLMs, which were originally proposed in [this paper](https://arxiv.org/abs/2408.13359).
+3. Large Language Model: The Granite Speech model leverages Granite LLMs, which were originally proposed in [this paper](https://huggingface.co/papers/2408.13359).
 
 4. LoRA adapter(s): The Granite Speech model contains a modality specific LoRA, which will be enabled when audio features are provided, and disabled otherwise.
 

docs/source/en/model_doc/granitemoe.md

@@ -24,7 +24,7 @@ rendered properly in your Markdown viewer.
 
 ## Overview
 
-The GraniteMoe model was proposed in [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 and Rameswar Panda.
+The GraniteMoe model was proposed in [Power Scheduler: A Batch Size and Token Number Agnostic Learning Rate Scheduler](https://huggingface.co/papers/2408.13359) by Yikang Shen, Matthew Stallone, Mayank Mishra, Gaoyuan Zhang, Shawn Tan, Aditya Prasad, Adriana Meza Soria, David D. Cox and Rameswar Panda.
 
 PowerMoE-3B is a 3B sparse Mixture-of-Experts (sMoE) language model trained with the Power learning rate scheduler. It sparsely activates 800M parameters for each token. It is trained on a mix of open-source and proprietary datasets. PowerMoE-3B has shown promising results compared to other dense models with 2x activate parameters across various benchmarks, including natural language multi-choices, code generation, and math reasoning.
 

docs/source/en/model_doc/granitemoeshared.md

@@ -19,7 +19,7 @@ rendered properly in your Markdown viewer.
 ## Overview
 
 
-The GraniteMoe model was proposed in [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 and Rameswar Panda.
+The GraniteMoe model was proposed in [Power Scheduler: A Batch Size and Token Number Agnostic Learning Rate Scheduler](https://huggingface.co/papers/2408.13359) by Yikang Shen, Matthew Stallone, Mayank Mishra, Gaoyuan Zhang, Shawn Tan, Aditya Prasad, Adriana Meza Soria, David D. Cox and Rameswar Panda.
 
 Additionally this class GraniteMoeSharedModel adds shared experts for Moe.
 

docs/source/en/model_doc/graphormer.md

@@ -28,7 +28,7 @@ You can do so by running the following command: `pip install -U transformers==4.
 
 ## Overview
 
-The Graphormer model was proposed in [Do Transformers Really Perform Bad for Graph Representation?](https://arxiv.org/abs/2106.05234)  by
+The Graphormer model was proposed in [Do Transformers Really Perform Bad for Graph Representation?](https://huggingface.co/papers/2106.05234)  by
 Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng, Guolin Ke, Di He, Yanming Shen and Tie-Yan Liu. It is a Graph Transformer model, modified to allow computations on graphs instead of text sequences by generating embeddings and features of interest during preprocessing and collation, then using a modified attention.
 
 The abstract from the paper is the following:

docs/source/en/model_doc/grounding-dino.md

@@ -22,7 +22,7 @@ rendered properly in your Markdown viewer.
 
 ## Overview
 
-The Grounding DINO model was proposed in [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://arxiv.org/abs/2303.05499) by Shilong Liu, Zhaoyang Zeng, Tianhe Ren, Feng Li, Hao Zhang, Jie Yang, Chunyuan Li, Jianwei Yang, Hang Su, Jun Zhu, Lei Zhang. Grounding DINO extends a closed-set object detection model with a text encoder, enabling open-set object detection. The model achieves remarkable results, such as 52.5 AP on COCO zero-shot.
+The Grounding DINO model was proposed in [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://huggingface.co/papers/2303.05499) by Shilong Liu, Zhaoyang Zeng, Tianhe Ren, Feng Li, Hao Zhang, Jie Yang, Chunyuan Li, Jianwei Yang, Hang Su, Jun Zhu, Lei Zhang. Grounding DINO extends a closed-set object detection model with a text encoder, enabling open-set object detection. The model achieves remarkable results, such as 52.5 AP on COCO zero-shot.
 
 The abstract from the paper is the following:
 
@@ -31,7 +31,7 @@ The abstract from the paper is the following:
 <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/grouding_dino_architecture.png"
 alt="drawing" width="600"/>
 
-<small> Grounding DINO overview. Taken from the <a href="https://arxiv.org/abs/2303.05499">original paper</a>. </small>
+<small> Grounding DINO overview. Taken from the <a href="https://huggingface.co/papers/2303.05499">original paper</a>. </small>
 
 This model was contributed by [EduardoPacheco](https://huggingface.co/EduardoPacheco) and [nielsr](https://huggingface.co/nielsr).
 The original code can be found [here](https://github.com/IDEA-Research/GroundingDINO).
@@ -85,7 +85,7 @@ Detected a cat with confidence 0.426 at location [11.74, 51.55, 316.51, 473.22]
 
 ## Grounded SAM
 
-One can combine Grounding DINO with the [Segment Anything](sam) model for text-based mask generation as introduced in [Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks](https://arxiv.org/abs/2401.14159). You can refer to this [demo notebook](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/Grounding%20DINO/GroundingDINO_with_Segment_Anything.ipynb) 🌍 for details.
+One can combine Grounding DINO with the [Segment Anything](sam) model for text-based mask generation as introduced in [Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks](https://huggingface.co/papers/2401.14159). You can refer to this [demo notebook](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/Grounding%20DINO/GroundingDINO_with_Segment_Anything.ipynb) 🌍 for details.
 
 <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/grounded_sam.png"
 alt="drawing" width="900"/>

docs/source/en/model_doc/groupvit.md

@@ -23,7 +23,7 @@ rendered properly in your Markdown viewer.
 
 ## Overview
 
-The GroupViT model was proposed in [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.
+The GroupViT model was proposed in [GroupViT: Semantic Segmentation Emerges from Text Supervision](https://huggingface.co/papers/2202.11094) by Jiarui Xu, Shalini De Mello, Sifei Liu, Wonmin Byeon, Thomas Breuel, Jan Kautz, Xiaolong Wang.
 Inspired by [CLIP](clip), GroupViT is a vision-language model that can perform zero-shot semantic segmentation on any given vocabulary categories.
 
 The abstract from the paper is the following:

docs/source/en/model_doc/hgnet_v2.md

@@ -19,7 +19,7 @@ rendered properly in your Markdown viewer.
 ## Overview
 
 A HGNet-V2 (High Performance GPU Net) image classification model.
-HGNet arhtictecture was proposed in [HGNET: A Hierarchical Feature Guided Network for Occupancy Flow Field Prediction](https://arxiv.org/abs/2407.01097) by
+HGNet arhtictecture was proposed in [HGNET: A Hierarchical Feature Guided Network for Occupancy Flow Field Prediction](https://huggingface.co/papers/2407.01097) by
 Zhan Chen, Chen Tang, Lu Xiong
 
 The abstract from the HGNET paper is the following:

docs/source/en/model_doc/hiera.md

@@ -22,7 +22,7 @@ rendered properly in your Markdown viewer.
 
 ## Overview
 
-Hiera was proposed in [Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles](https://arxiv.org/abs/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
+Hiera was proposed in [Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles](https://huggingface.co/papers/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
 
 The paper introduces "Hiera," a hierarchical Vision Transformer that simplifies the architecture of modern hierarchical vision transformers by removing unnecessary components without compromising on accuracy or efficiency. Unlike traditional transformers that add complex vision-specific components to improve supervised classification performance, Hiera demonstrates that such additions, often termed "bells-and-whistles," are not essential for high accuracy. By leveraging a strong visual pretext task (MAE) for pretraining, Hiera retains simplicity and achieves superior accuracy and speed both in inference and training across various image and video recognition tasks. The approach suggests that spatial biases required for vision tasks can be effectively learned through proper pretraining, eliminating the need for added architectural complexity. 
 
@@ -33,7 +33,7 @@ The abstract from the paper is the following:
 <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/hiera_overview.png"
 alt="drawing" width="600"/>
 
-<small> Hiera architecture. Taken from the <a href="https://arxiv.org/abs/2306.00989">original paper.</a> </small>
+<small> Hiera architecture. Taken from the <a href="https://huggingface.co/papers/2306.00989">original paper.</a> </small>
 
 This model was a joint contribution by [EduardoPacheco](https://huggingface.co/EduardoPacheco) and [namangarg110](https://huggingface.co/namangarg110). The original code can be found [here] (https://github.com/facebookresearch/hiera).
 

docs/source/en/model_doc/hubert.md

@@ -25,7 +25,7 @@ rendered properly in your Markdown viewer.
 
 ## Overview
 
-Hubert was proposed in [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
+Hubert was proposed in [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://huggingface.co/papers/2106.07447) by Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan
 Salakhutdinov, Abdelrahman Mohamed.
 
 The abstract from the paper is the following:

docs/source/en/model_doc/ibert.md

@@ -22,7 +22,7 @@ rendered properly in your Markdown viewer.
 
 ## Overview
 
-The I-BERT model was proposed in [I-BERT: Integer-only BERT Quantization](https://arxiv.org/abs/2101.01321) by
+The I-BERT model was proposed in [I-BERT: Integer-only BERT Quantization](https://huggingface.co/papers/2101.01321) by
 Sehoon Kim, Amir Gholami, Zhewei Yao, Michael W. Mahoney and Kurt Keutzer. It's a quantized version of RoBERTa running
 inference up to four times faster.
 

docs/source/en/model_doc/idefics2.md

@@ -24,7 +24,7 @@ rendered properly in your Markdown viewer.
 
 ## Overview
 
-The Idefics2 model was proposed in [What matters when building vision-language models?](https://arxiv.org/abs/2405.02246) by Léo Tronchon, Hugo Laurencon, Victor Sanh. The accompanying blog post can be found [here](https://huggingface.co/blog/idefics2).
+The Idefics2 model was proposed in [What matters when building vision-language models?](https://huggingface.co/papers/2405.02246) by Léo Tronchon, Hugo Laurencon, Victor Sanh. The accompanying blog post can be found [here](https://huggingface.co/blog/idefics2).
 
 Idefics2 is an open multimodal model that accepts arbitrary sequences of image and text inputs and produces text
 outputs. The model can answer questions about images, describe visual content, create stories grounded on multiple
@@ -39,7 +39,7 @@ The abstract from the paper is the following:
 <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/idefics2_architecture.png"
 alt="drawing" width="600"/>
 
-<small> Idefics2 architecture. Taken from the <a href="https://arxiv.org/abs/2405.02246">original paper.</a> </small>
+<small> Idefics2 architecture. Taken from the <a href="https://huggingface.co/papers/2405.02246">original paper.</a> </small>
 
 This model was contributed by [amyeroberts](https://huggingface.co/amyeroberts).
 The original code can be found [here](https://huggingface.co/HuggingFaceM4/idefics2).

docs/source/en/model_doc/ijepa.md

@@ -24,7 +24,7 @@ rendered properly in your Markdown viewer.
 
 ## Overview
 
-The I-JEPA model was proposed in [Image-based 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.
+The I-JEPA model was proposed in [Image-based Joint-Embedding Predictive Architecture](https://huggingface.co/papers/2301.08243) by Mahmoud Assran, Quentin Duval, Ishan Misra, Piotr Bojanowski, Pascal Vincent, Michael Rabbat, Yann LeCun, Nicolas Ballas.
 I-JEPA is a self-supervised learning method that predicts the representations of one part of an image based on other parts of the same image. This approach focuses on learning semantic features without relying on pre-defined invariances from hand-crafted data transformations, which can bias specific tasks, or on filling in pixel-level details, which often leads to less meaningful representations.
 
 The abstract from the paper is the following:
@@ -34,7 +34,7 @@ This paper demonstrates an approach for learning highly semantic image represent
 <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/ijepa_architecture.jpg"
 alt="drawing" width="600"/>
 
-<small> I-JEPA architecture. Taken from the <a href="https://arxiv.org/abs/2301.08243">original paper.</a> </small>
+<small> I-JEPA architecture. Taken from the <a href="https://huggingface.co/papers/2301.08243">original paper.</a> </small>
 
 This model was contributed by [jmtzt](https://huggingface.co/jmtzt).
 The original code can be found [here](https://github.com/facebookresearch/ijepa).

docs/source/en/model_doc/informer.md

@@ -22,7 +22,7 @@ rendered properly in your Markdown viewer.
 
 ## Overview
 
-The Informer model was proposed in [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://arxiv.org/abs/2012.07436) by Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang.
+The Informer model was proposed in [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://huggingface.co/papers/2012.07436) by Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang.
 
 This method introduces a Probabilistic Attention mechanism to select the "active" queries rather than the "lazy" queries and provides a sparse Transformer thus mitigating the quadratic compute and memory requirements of vanilla attention.
 

docs/source/en/model_doc/instructblip.md

@@ -18,7 +18,7 @@ specific language governing permissions and limitations under the License.
 
 ## Overview
 
-The InstructBLIP model was proposed in [InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning](https://arxiv.org/abs/2305.06500) by Wenliang Dai, Junnan Li, Dongxu Li, Anthony Meng Huat Tiong, Junqi Zhao, Weisheng Wang, Boyang Li, Pascale Fung, Steven Hoi.
+The InstructBLIP model was proposed in [InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning](https://huggingface.co/papers/2305.06500) by Wenliang Dai, Junnan Li, Dongxu Li, Anthony Meng Huat Tiong, Junqi Zhao, Weisheng Wang, Boyang Li, Pascale Fung, Steven Hoi.
 InstructBLIP leverages the [BLIP-2](blip2) architecture for visual instruction tuning.
 
 The abstract from the paper is the following:
@@ -28,7 +28,7 @@ The abstract from the paper is the following:
 <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/instructblip_architecture.jpg"
 alt="drawing" width="600"/>
 
-<small> InstructBLIP architecture. Taken from the <a href="https://arxiv.org/abs/2305.06500">original paper.</a> </small>
+<small> InstructBLIP architecture. Taken from the <a href="https://huggingface.co/papers/2305.06500">original paper.</a> </small>
 
 This model was contributed by [nielsr](https://huggingface.co/nielsr).
 The original code can be found [here](https://github.com/salesforce/LAVIS/tree/main/projects/instructblip).

docs/source/en/model_doc/instructblipvideo.md

@@ -18,7 +18,7 @@ specific language governing permissions and limitations under the License.
 
 ## Overview
 
-The InstructBLIPVideo is an extension of the models proposed in [InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning](https://arxiv.org/abs/2305.06500) by Wenliang Dai, Junnan Li, Dongxu Li, Anthony Meng Huat Tiong, Junqi Zhao, Weisheng Wang, Boyang Li, Pascale Fung, Steven Hoi.
+The InstructBLIPVideo is an extension of the models proposed in [InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning](https://huggingface.co/papers/2305.06500) by Wenliang Dai, Junnan Li, Dongxu Li, Anthony Meng Huat Tiong, Junqi Zhao, Weisheng Wang, Boyang Li, Pascale Fung, Steven Hoi.
 InstructBLIPVideo uses the same architecture as [InstructBLIP](instructblip) and works with the same checkpoints as [InstructBLIP](instructblip). The only difference is the ability to process videos.
 
 The abstract from the paper is the following:
@@ -28,7 +28,7 @@ The abstract from the paper is the following:
 <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/instructblip_architecture.jpg"
 alt="drawing" width="600"/>
 
-<small> InstructBLIPVideo architecture. Taken from the <a href="https://arxiv.org/abs/2305.06500">original paper.</a> </small>
+<small> InstructBLIPVideo architecture. Taken from the <a href="https://huggingface.co/papers/2305.06500">original paper.</a> </small>
 
 This model was contributed by [RaushanTurganbay](https://huggingface.co/RaushanTurganbay).
 The original code can be found [here](https://github.com/salesforce/LAVIS/tree/main/projects/instructblip).

docs/source/en/model_doc/janus.md

@@ -18,7 +18,7 @@ rendered properly in your Markdown viewer.
 
 ## Overview
 
-The Janus Model was originally proposed in [Janus: Decoupling Visual Encoding for Unified Multimodal Understanding and Generation](https://arxiv.org/abs/2410.13848) by DeepSeek AI team and later refined in [Janus-Pro: Unified Multimodal Understanding and Generation with Data and Model Scaling](https://arxiv.org/abs/2501.17811). Janus is a vision-language model that can generate both image and text output, it can also take both images and text as input.
+The Janus Model was originally proposed in [Janus: Decoupling Visual Encoding for Unified Multimodal Understanding and Generation](https://huggingface.co/papers/2410.13848) by DeepSeek AI team and later refined in [Janus-Pro: Unified Multimodal Understanding and Generation with Data and Model Scaling](https://huggingface.co/papers/2501.17811). Janus is a vision-language model that can generate both image and text output, it can also take both images and text as input.
 
 > [!NOTE]
 > The model doesn't generate both images and text in an interleaved format. The user has to pass a parameter indicating whether to generate text or image.

docs/source/en/model_doc/jetmoe.md

@@ -26,7 +26,7 @@ rendered properly in your Markdown viewer.
 
 **JetMoe-8B** is an 8B Mixture-of-Experts (MoE) language model developed by [Yikang Shen](https://scholar.google.com.hk/citations?user=qff5rRYAAAAJ) and [MyShell](https://myshell.ai/).
 JetMoe project aims to provide a LLaMA2-level performance and efficient language model with a limited budget.
-To achieve this goal, JetMoe uses a sparsely activated architecture inspired by the [ModuleFormer](https://arxiv.org/abs/2306.04640). 
+To achieve this goal, JetMoe uses a sparsely activated architecture inspired by the [ModuleFormer](https://huggingface.co/papers/2306.04640). 
 Each JetMoe block consists of two MoE layers: Mixture of Attention Heads and Mixture of MLP Experts.
 Given the input tokens, it activates a subset of its experts to process them.
 This sparse activation schema enables JetMoe to achieve much better training throughput than similar size dense models. 

docs/source/en/model_doc/jukebox.md

@@ -29,7 +29,7 @@ You can do so by running the following command: `pip install -U transformers==4.
 
 ## Overview
 
-The Jukebox model was proposed in [Jukebox: A generative model for music](https://arxiv.org/pdf/2005.00341.pdf)
+The Jukebox model was proposed in [Jukebox: A generative model for music](https://huggingface.co/papers/2005.00341)
 by Prafulla Dhariwal, Heewoo Jun, Christine Payne, Jong Wook Kim, Alec Radford,
 Ilya Sutskever. It introduces a generative music model which can produce minute long samples that can be conditioned on
 an artist, genres and lyrics.
@@ -38,7 +38,7 @@ The abstract from the paper is the following:
 
 *We introduce Jukebox, a model that generates music with singing in the raw audio domain. We tackle the long context of raw audio using a multiscale VQ-VAE to compress it to discrete codes, and modeling those using autoregressive Transformers. We show that the combined model at scale can generate high-fidelity and diverse songs with coherence up to multiple minutes. We can condition on artist and genre to steer the musical and vocal style, and on unaligned lyrics to make the singing more controllable. We are releasing thousands of non cherry-picked samples, along with model weights and code.*
 
-As shown on the following figure, Jukebox is made of 3 `priors` which are decoder only models. They follow the architecture described in [Generating Long Sequences with Sparse Transformers](https://arxiv.org/abs/1904.10509), modified to support longer context length.
+As shown on the following figure, Jukebox is made of 3 `priors` which are decoder only models. They follow the architecture described in [Generating Long Sequences with Sparse Transformers](https://huggingface.co/papers/1904.10509), modified to support longer context length.
 First, a autoencoder is used to encode the text lyrics. Next, the first (also called `top_prior`) prior attends to the last hidden states extracted from the lyrics encoder. The priors are linked to the previous priors respectively via an `AudioConditioner` module. The`AudioConditioner` upsamples the outputs of the previous prior to raw tokens at a certain audio frame per second resolution.
 The metadata such as *artist, genre and timing* are passed to each prior, in the form of a start token and positional embedding for the timing data.  The hidden states are mapped to the closest codebook vector from the VQVAE in order to convert them to raw audio.
 

docs/source/en/model_doc/kosmos-2.md

@@ -22,7 +22,7 @@ rendered properly in your Markdown viewer.
 
 ## Overview
 
-The KOSMOS-2 model was proposed in [Kosmos-2: Grounding Multimodal Large Language Models to the World](https://arxiv.org/abs/2306.14824) by Zhiliang Peng, Wenhui Wang, Li Dong, Yaru Hao, Shaohan Huang, Shuming Ma, Furu Wei.
+The KOSMOS-2 model was proposed in [Kosmos-2: Grounding Multimodal Large Language Models to the World](https://huggingface.co/papers/2306.14824) by Zhiliang Peng, Wenhui Wang, Li Dong, Yaru Hao, Shaohan Huang, Shuming Ma, Furu Wei.
 
 KOSMOS-2 is a Transformer-based causal language model and is trained using the next-word prediction task on a web-scale
 dataset of grounded image-text pairs [GRIT](https://huggingface.co/datasets/zzliang/GRIT). The spatial coordinates of
@@ -37,7 +37,7 @@ The abstract from the paper is the following:
 <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/kosmos_2_overview.jpg"
 alt="drawing" width="600"/>
 
-<small> Overview of tasks that KOSMOS-2 can handle. Taken from the <a href="https://arxiv.org/abs/2306.14824">original paper</a>. </small>
+<small> Overview of tasks that KOSMOS-2 can handle. Taken from the <a href="https://huggingface.co/papers/2306.14824">original paper</a>. </small>
 
 ## Example
 

docs/source/en/model_doc/layoutlm.md

@@ -26,7 +26,7 @@ rendered properly in your Markdown viewer.
 ## Overview
 
 The LayoutLM model was proposed in the paper [LayoutLM: Pre-training of Text and Layout for Document Image
-Understanding](https://arxiv.org/abs/1912.13318) by Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, and
+Understanding](https://huggingface.co/papers/1912.13318) by Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, and
 Ming Zhou. It's a simple but effective pretraining method of text and layout for document image understanding and
 information extraction tasks, such as form understanding and receipt understanding. It obtains state-of-the-art results
 on several downstream tasks:

docs/source/en/model_doc/layoutlmv2.md

@@ -22,7 +22,7 @@ rendered properly in your Markdown viewer.
 
 ## Overview
 
-The LayoutLMV2 model was proposed in [LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding](https://arxiv.org/abs/2012.14740) by Yang Xu, Yiheng Xu, Tengchao Lv, Lei Cui, Furu Wei, Guoxin Wang, Yijuan Lu,
+The LayoutLMV2 model was proposed in [LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding](https://huggingface.co/papers/2012.14740) by Yang Xu, Yiheng Xu, Tengchao Lv, Lei Cui, Furu Wei, Guoxin Wang, Yijuan Lu,
 Dinei Florencio, Cha Zhang, Wanxiang Che, Min Zhang, Lidong Zhou. LayoutLMV2 improves [LayoutLM](layoutlm) to obtain
 state-of-the-art results across several document image understanding benchmarks:
 
@@ -34,7 +34,7 @@ state-of-the-art results across several document image understanding benchmarks:
   documents for testing).
 - document image classification: the [RVL-CDIP](https://www.cs.cmu.edu/~aharley/rvl-cdip/) dataset (a collection of
   400,000 images belonging to one of 16 classes).
-- document visual question answering: the [DocVQA](https://arxiv.org/abs/2007.00398) dataset (a collection of 50,000
+- document visual question answering: the [DocVQA](https://huggingface.co/papers/2007.00398) dataset (a collection of 50,000
   questions defined on 12,000+ document images).
 
 The abstract from the paper is the following:
@@ -65,7 +65,7 @@ python -m pip install torchvision tesseract
 - The main difference between LayoutLMv1 and LayoutLMv2 is that the latter incorporates visual embeddings during
   pre-training (while LayoutLMv1 only adds visual embeddings during fine-tuning).
 - LayoutLMv2 adds both a relative 1D attention bias as well as a spatial 2D attention bias to the attention scores in
-  the self-attention layers. Details can be found on page 5 of the [paper](https://arxiv.org/abs/2012.14740).
+  the self-attention layers. Details can be found on page 5 of the [paper](https://huggingface.co/papers/2012.14740).
 - Demo notebooks on how to use the LayoutLMv2 model on RVL-CDIP, FUNSD, DocVQA, CORD can be found [here](https://github.com/NielsRogge/Transformers-Tutorials).
 - LayoutLMv2 uses Facebook AI's [Detectron2](https://github.com/facebookresearch/detectron2/) package for its visual
   backbone. See [this link](https://detectron2.readthedocs.io/en/latest/tutorials/install.html) for installation

docs/source/en/model_doc/layoutlmv3.md

@@ -18,7 +18,7 @@ rendered properly in your Markdown viewer.
 
 ## Overview
 
-The LayoutLMv3 model was proposed in [LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking](https://arxiv.org/abs/2204.08387) by Yupan Huang, Tengchao Lv, Lei Cui, Yutong Lu, Furu Wei.
+The LayoutLMv3 model was proposed in [LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking](https://huggingface.co/papers/2204.08387) by Yupan Huang, Tengchao Lv, Lei Cui, Yutong Lu, Furu Wei.
 LayoutLMv3 simplifies [LayoutLMv2](layoutlmv2) by using patch embeddings (as in [ViT](vit)) instead of leveraging a CNN backbone, and pre-trains the model on 3 objectives: masked language modeling (MLM), masked image modeling (MIM)
 and word-patch alignment (WPA).
 
@@ -29,7 +29,7 @@ The abstract from the paper is the following:
 <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/layoutlmv3_architecture.png"
 alt="drawing" width="600"/>
 
-<small> LayoutLMv3 architecture. Taken from the <a href="https://arxiv.org/abs/2204.08387">original paper</a>. </small>
+<small> LayoutLMv3 architecture. Taken from the <a href="https://huggingface.co/papers/2204.08387">original paper</a>. </small>
 
 This model was contributed by [nielsr](https://huggingface.co/nielsr). The TensorFlow version of this model was added by [chriskoo](https://huggingface.co/chriskoo), [tokec](https://huggingface.co/tokec), and [lre](https://huggingface.co/lre). The original code can be found [here](https://github.com/microsoft/unilm/tree/master/layoutlmv3).
 

docs/source/en/model_doc/layoutxlm.md

@@ -22,8 +22,8 @@ rendered properly in your Markdown viewer.
 
 ## Overview
 
-LayoutXLM was proposed in [LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding](https://arxiv.org/abs/2104.08836) by Yiheng Xu, Tengchao Lv, Lei Cui, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha
-Zhang, Furu Wei. It's a multilingual extension of the [LayoutLMv2 model](https://arxiv.org/abs/2012.14740) trained
+LayoutXLM was proposed in [LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding](https://huggingface.co/papers/2104.08836) by Yiheng Xu, Tengchao Lv, Lei Cui, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha
+Zhang, Furu Wei. It's a multilingual extension of the [LayoutLMv2 model](https://huggingface.co/papers/2012.14740) trained
 on 53 languages.
 
 The abstract from the paper is the following:

docs/source/en/model_doc/led.md

@@ -23,7 +23,7 @@ rendered properly in your Markdown viewer.
 
 ## Overview
 
-The LED model was proposed in [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150) by Iz
+The LED model was proposed in [Longformer: The Long-Document Transformer](https://huggingface.co/papers/2004.05150) by Iz
 Beltagy, Matthew E. Peters, Arman Cohan.
 
 The abstract from the paper is the following:

docs/source/en/model_doc/levit.md

@@ -22,7 +22,7 @@ rendered properly in your Markdown viewer.
 
 ## Overview
 
-The LeViT model was proposed in [LeViT: Introducing Convolutions to Vision Transformers](https://arxiv.org/abs/2104.01136) by Ben Graham, Alaaeldin El-Nouby, Hugo Touvron, Pierre Stock, Armand Joulin, Hervé Jégou, Matthijs Douze. LeViT improves the [Vision Transformer (ViT)](vit) in performance and efficiency by a few architectural differences such as activation maps with decreasing resolutions in Transformers and the introduction of an attention bias to integrate positional information.
+The LeViT model was proposed in [LeViT: Introducing Convolutions to Vision Transformers](https://huggingface.co/papers/2104.01136) by Ben Graham, Alaaeldin El-Nouby, Hugo Touvron, Pierre Stock, Armand Joulin, Hervé Jégou, Matthijs Douze. LeViT improves the [Vision Transformer (ViT)](vit) in performance and efficiency by a few architectural differences such as activation maps with decreasing resolutions in Transformers and the introduction of an attention bias to integrate positional information.
 
 The abstract from the paper is the following:
 
@@ -40,7 +40,7 @@ to the speed/accuracy tradeoff. For example, at 80% ImageNet top-1 accuracy, LeV
 <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/levit_architecture.png"
 alt="drawing" width="600"/>
 
-<small> LeViT Architecture. Taken from the <a href="https://arxiv.org/abs/2104.01136">original paper</a>.</small>
+<small> LeViT Architecture. Taken from the <a href="https://huggingface.co/papers/2104.01136">original paper</a>.</small>
 
 This model was contributed by [anugunj](https://huggingface.co/anugunj). The original code can be found [here](https://github.com/facebookresearch/LeViT).