[Docs] Model_doc structure/clarity improvements (#26876)

* first batch of structure improvements for model_docs

* second batch of structure improvements for model_docs

* more structure improvements for model_docs

* more structure improvements for model_docs

* structure improvements for cv model_docs

* more structural refactoring

* addressed feedback about image processors

docs/source/en/model_doc/electra.md

@@ -50,7 +50,9 @@ using 30x more compute) on the GLUE natural language understanding benchmark. Ou
 where it performs comparably to RoBERTa and XLNet while using less than 1/4 of their compute and outperforms them when
 using the same amount of compute.*
 
-Tips:
+This model was contributed by [lysandre](https://huggingface.co/lysandre). The original code can be found [here](https://github.com/google-research/electra).
+
+## Usage tips
 
 - ELECTRA is the pretraining approach, therefore there is nearly no changes done to the underlying model: BERT. The
   only change is the separation of the embedding size and the hidden size: the embedding size is generally smaller,
@@ -66,9 +68,7 @@ Tips:
   [`ElectraForPreTraining`] model (the classification head will be randomly initialized as it
   doesn't exist in the generator).
 
-This model was contributed by [lysandre](https://huggingface.co/lysandre). The original code can be found [here](https://github.com/google-research/electra).
-
-## Documentation resources
+## Resources
 
 - [Text classification task guide](../tasks/sequence_classification)
 - [Token classification task guide](../tasks/token_classification)
@@ -95,6 +95,9 @@ This model was contributed by [lysandre](https://huggingface.co/lysandre). The o
 
 [[autodoc]] models.electra.modeling_tf_electra.TFElectraForPreTrainingOutput
 
+<frameworkcontent>
+<pt>
+
 ## ElectraModel
 
 [[autodoc]] ElectraModel
@@ -135,6 +138,9 @@ This model was contributed by [lysandre](https://huggingface.co/lysandre). The o
 [[autodoc]] ElectraForQuestionAnswering
     - forward
 
+</pt>
+<tf>
+
 ## TFElectraModel
 
 [[autodoc]] TFElectraModel
@@ -170,6 +176,9 @@ This model was contributed by [lysandre](https://huggingface.co/lysandre). The o
 [[autodoc]] TFElectraForQuestionAnswering
     - call
 
+</tf>
+<jax>
+
 ## FlaxElectraModel
 
 [[autodoc]] FlaxElectraModel
@@ -209,3 +218,6 @@ This model was contributed by [lysandre](https://huggingface.co/lysandre). The o
 
 [[autodoc]] FlaxElectraForQuestionAnswering
     - __call__
+
+</jax>
+</frameworkcontent>

docs/source/en/model_doc/encodec.md

@@ -26,6 +26,9 @@ The abstract from the paper is the following:
 
 This model was contributed by [Matthijs](https://huggingface.co/Matthijs), [Patrick Von Platen](https://huggingface.co/patrickvonplaten) and [Arthur Zucker](https://huggingface.co/ArthurZ). 
 The original code can be found [here](https://github.com/facebookresearch/encodec).
+
+## Usage example 
+
 Here is a quick example of how to encode and decode an audio using this model:
 
 ```python 
@@ -45,7 +48,6 @@ Here is a quick example of how to encode and decode an audio using this model:
 >>> audio_values = model(inputs["input_values"], inputs["padding_mask"]).audio_values
 ```
 
-
 ## EncodecConfig
 
 [[autodoc]] EncodecConfig

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

@@ -149,20 +149,32 @@ were contributed by [ydshieh](https://github.com/ydshieh).
 
 [[autodoc]] EncoderDecoderConfig
 
+<frameworkcontent>
+<pt>
+
 ## EncoderDecoderModel
 
 [[autodoc]] EncoderDecoderModel
     - forward
     - from_encoder_decoder_pretrained
 
+</pt>
+<tf>
+
 ## TFEncoderDecoderModel
 
 [[autodoc]] TFEncoderDecoderModel
     - call
     - from_encoder_decoder_pretrained
 
+</tf>
+<jax>
+
 ## FlaxEncoderDecoderModel
 
 [[autodoc]] FlaxEncoderDecoderModel
     - __call__
     - from_encoder_decoder_pretrained
+
+</jax>
+</frameworkcontent>

docs/source/en/model_doc/ernie.md

@@ -23,7 +23,7 @@ including [ERNIE1.0](https://arxiv.org/abs/1904.09223), [ERNIE2.0](https://ojs.a
 
 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).
 
-### How to use
+### Usage example
 Take `ernie-1.0-base-zh` as an example:
 
 ```Python
@@ -32,7 +32,7 @@ tokenizer = AutoTokenizer.from_pretrained("nghuyong/ernie-1.0-base-zh")
 model = AutoModel.from_pretrained("nghuyong/ernie-1.0-base-zh")
 ```
 
-### Supported Models
+### Model checkpoints
 
 |     Model Name      | Language |           Description           |
 |:-------------------:|:--------:|:-------------------------------:|
@@ -51,7 +51,7 @@ You can find all the supported models from huggingface's model hub: [huggingface
 repo: [PaddleNLP](https://paddlenlp.readthedocs.io/zh/latest/model_zoo/transformers/ERNIE/contents.html)
 and [ERNIE](https://github.com/PaddlePaddle/ERNIE/blob/repro).
 
-## Documentation resources
+## Resources
 
 - [Text classification task guide](../tasks/sequence_classification)
 - [Token classification task guide](../tasks/token_classification)

docs/source/en/model_doc/ernie_m.md

@@ -25,18 +25,17 @@ Hao Tian, Hua Wu, Haifeng Wang.
 The abstract from the paper is the following:
 
 *Recent studies have demonstrated that pre-trained cross-lingual models achieve impressive performance in downstream cross-lingual tasks. This improvement benefits from learning a large amount of monolingual and parallel corpora. Although it is generally acknowledged that parallel corpora are critical for improving the model performance, existing methods are often constrained by the size of parallel corpora, especially for lowresource languages. In this paper, we propose ERNIE-M, a new training method that encourages the model to align the representation of multiple languages with monolingual corpora, to overcome the constraint that the parallel corpus size places on the model performance. Our key insight is to integrate back-translation into the pre-training process. We generate pseudo-parallel sentence pairs on a monolingual corpus to enable the learning of semantic alignments between different languages, thereby enhancing the semantic modeling of cross-lingual models. Experimental results show that ERNIE-M outperforms existing cross-lingual models and delivers new state-of-the-art results in various cross-lingual downstream tasks.*
+This model was contributed by [Susnato Dhar](https://huggingface.co/susnato). The original code can be found [here](https://github.com/PaddlePaddle/PaddleNLP/tree/develop/paddlenlp/transformers/ernie_m).
 
-Tips:
-
-1. Ernie-M is a BERT-like model so it is a stacked Transformer Encoder.
-2. Instead of using MaskedLM for pretraining (like BERT) the authors used two novel techniques: `Cross-attention Masked Language Modeling` and `Back-translation Masked Language Modeling`. For now these two LMHead objectives are not implemented here.
-3. It is a multilingual language model.
-4. Next Sentence Prediction was not used in pretraining process.
 
+## Usage tips
 
-This model was contributed by [Susnato Dhar](https://huggingface.co/susnato). The original code can be found [here](https://github.com/PaddlePaddle/PaddleNLP/tree/develop/paddlenlp/transformers/ernie_m).
+- Ernie-M is a BERT-like model so it is a stacked Transformer Encoder.
+- Instead of using MaskedLM for pretraining (like BERT) the authors used two novel techniques: `Cross-attention Masked Language Modeling` and `Back-translation Masked Language Modeling`. For now these two LMHead objectives are not implemented here.
+- It is a multilingual language model.
+- Next Sentence Prediction was not used in pretraining process.
 
-## Documentation resources
+## Resources
 
 - [Text classification task guide](../tasks/sequence_classification)
 - [Token classification task guide](../tasks/token_classification)

docs/source/en/model_doc/esm.md

@@ -17,6 +17,7 @@ rendered properly in your Markdown viewer.
 # ESM
 
 ## Overview
+
 This page provides code and pre-trained weights for Transformer protein language models from Meta AI's Fundamental 
 AI Research Team, providing the state-of-the-art ESMFold and ESM-2, and the previously released ESM-1b and ESM-1v.
 Transformer protein language models were introduced in the paper [Biological structure and function emerge from scaling
@@ -73,11 +74,6 @@ sequences with low perplexity that are well understood by the language model. ES
 order of magnitude faster than AlphaFold2, enabling exploration of the structural space of metagenomic
 proteins in practical timescales.*
 
-
-Tips:
-
-- ESM models are trained with a masked language modeling (MLM) objective.
-
 The original code can be found [here](https://github.com/facebookresearch/esm) and was
 was developed by the Fundamental AI Research team at Meta AI.
 ESM-1b, ESM-1v and ESM-2 were contributed to huggingface by [jasonliu](https://huggingface.co/jasonliu)
@@ -87,10 +83,12 @@ ESMFold was contributed to huggingface by [Matt](https://huggingface.co/Rocketkn
 [Sylvain](https://huggingface.co/sgugger), with a big thank you to Nikita Smetanin, Roshan Rao and Tom Sercu for their
 help throughout the process!
 
-The HuggingFace port of ESMFold uses portions of the [openfold](https://github.com/aqlaboratory/openfold) library.
-The `openfold` library is licensed under the Apache License 2.0.
+## Usage tips
 
-## Documentation resources
+- ESM models are trained with a masked language modeling (MLM) objective.
+- The HuggingFace port of ESMFold uses portions of the [openfold](https://github.com/aqlaboratory/openfold) library. The `openfold` library is licensed under the Apache License 2.0.
+
+## Resources
 
 - [Text classification task guide](../tasks/sequence_classification)
 - [Token classification task guide](../tasks/token_classification)
@@ -109,6 +107,8 @@ The `openfold` library is licensed under the Apache License 2.0.
     - create_token_type_ids_from_sequences
     - save_vocabulary
 
+<frameworkcontent>
+<pt>
 
 ## EsmModel
 
@@ -135,6 +135,9 @@ The `openfold` library is licensed under the Apache License 2.0.
 [[autodoc]] EsmForProteinFolding
     - forward
 
+</pt>
+<tf>
+
 ## TFEsmModel
 
 [[autodoc]] TFEsmModel
@@ -154,3 +157,6 @@ The `openfold` library is licensed under the Apache License 2.0.
 
 [[autodoc]] TFEsmForTokenClassification
     - call
+
+</tf>
+</frameworkcontent>

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

@@ -48,6 +48,10 @@ Google has released the following variants:
 
 - [google/flan-t5-xxl](https://huggingface.co/google/flan-t5-xxl).
 
-One can refer to [T5's documentation page](t5) for all tips, code examples and notebooks. As well as the FLAN-T5 model card for more details regarding training and evaluation of the model.
-
 The original checkpoints can be found [here](https://github.com/google-research/t5x/blob/main/docs/models.md#flan-t5-checkpoints).
+
+<Tip>
+
+Refer to [T5's documentation page](t5) for all API reference, code examples and notebooks. For more details regarding training and evaluation of the FLAN-T5, refer to the model card.
+
+</Tip>
\ No newline at end of file

docs/source/en/model_doc/flan-ul2.md

@@ -21,7 +21,6 @@ rendered properly in your Markdown viewer.
 Flan-UL2 is an encoder decoder model based on the T5 architecture. It uses the same configuration as the [UL2](ul2) model released earlier last year. 
 It was fine tuned using the "Flan" prompt tuning and dataset collection. Similar to `Flan-T5`,  one can directly use FLAN-UL2 weights without finetuning the model:
 
-
 According to the original blog here are the notable improvements:
 
 - The original UL2 model was only trained with receptive field of 512, which made it non-ideal for N-shot prompting where N is large.
@@ -29,9 +28,6 @@ According to the original blog here are the notable improvements:
 - The original UL2 model also had mode switch tokens that was rather mandatory to get good performance. However, they were a little cumbersome as this requires often some changes during inference or finetuning. In this update/change, we continue training UL2 20B for an additional 100k steps (with small batch) to forget “mode tokens” before applying Flan instruction tuning. This Flan-UL2 checkpoint does not require mode tokens anymore.
 Google has released the following variants:
 
-
-One can refer to [T5's documentation page](t5) for all tips, code examples and notebooks. As well as the FLAN-T5 model card for more details regarding training and evaluation of the model.
-
 The original checkpoints can be found [here](https://github.com/google-research/t5x/blob/main/docs/models.md#flan-ul2-checkpoints).
 
 
@@ -51,6 +47,8 @@ The model is pretty heavy (~40GB in half precision) so if you just want to run t
 ['In a large skillet, brown the ground beef and onion over medium heat. Add the garlic']
 ```
 
-## Inference
+<Tip>
+
+Refer to [T5's documentation page](t5) for API reference, tips, code examples and notebooks. 
 
-The inference protocol is exactly the same as any `T5` model, please have a look at the [T5's documentation page](t5) for more details.
+</Tip>

docs/source/en/model_doc/flaubert.md

@@ -50,7 +50,7 @@ This model was contributed by [formiel](https://huggingface.co/formiel). The ori
 Tips:
 - Like RoBERTa, without the sentence ordering prediction (so just trained on the MLM objective).
 
-## Documentation resources
+## Resources
 
 - [Text classification task guide](../tasks/sequence_classification)
 - [Token classification task guide](../tasks/token_classification)
@@ -66,6 +66,9 @@ Tips:
 
 [[autodoc]] FlaubertTokenizer
 
+<frameworkcontent>
+<pt>
+
 ## FlaubertModel
 
 [[autodoc]] FlaubertModel
@@ -101,6 +104,9 @@ Tips:
 [[autodoc]] FlaubertForQuestionAnswering
     - forward
 
+</pt>
+<tf>
+
 ## TFFlaubertModel
 
 [[autodoc]] TFFlaubertModel
@@ -130,3 +136,9 @@ Tips:
 
 [[autodoc]] TFFlaubertForQuestionAnsweringSimple
     - call
+
+</tf>
+</frameworkcontent>
+
+
+

docs/source/en/model_doc/flava.md

@@ -33,10 +33,8 @@ at once -- a true vision and language foundation model should be good at vision
 cross- and multi-modal vision and language tasks. We introduce FLAVA as such a model and demonstrate
 impressive performance on a wide range of 35 tasks spanning these target modalities.*
 
-
 This model was contributed by [aps](https://huggingface.co/aps). The original code can be found [here](https://github.com/facebookresearch/multimodal/tree/main/examples/flava).
 
-
 ## FlavaConfig
 
 [[autodoc]] FlavaConfig

docs/source/en/model_doc/fnet.md

@@ -37,15 +37,15 @@ sequence lengths on GPUs (and across relatively shorter lengths on TPUs). Finall
 and is particularly efficient at smaller model sizes; for a fixed speed and accuracy budget, small FNet models
 outperform Transformer counterparts.*
 
-Tips on usage:
+This model was contributed by [gchhablani](https://huggingface.co/gchhablani). The original code can be found [here](https://github.com/google-research/google-research/tree/master/f_net).
 
-- The model was trained without an attention mask as it is based on Fourier Transform. The model was trained with
-  maximum sequence length 512 which includes pad tokens. Hence, it is highly recommended to use the same maximum
-  sequence length for fine-tuning and inference.
+## Usage tips
 
-This model was contributed by [gchhablani](https://huggingface.co/gchhablani). The original code can be found [here](https://github.com/google-research/google-research/tree/master/f_net).
+The model was trained without an attention mask as it is based on Fourier Transform. The model was trained with 
+maximum sequence length 512 which includes pad tokens. Hence, it is highly recommended to use the same maximum 
+sequence length for fine-tuning and inference.
 
-## Documentation resources
+## Resources
 
 - [Text classification task guide](../tasks/sequence_classification)
 - [Token classification task guide](../tasks/token_classification)

docs/source/en/model_doc/focalnet.md

@@ -27,14 +27,9 @@ The abstract from the paper is the following:
 *We propose focal modulation networks (FocalNets in short), where self-attention (SA) is completely replaced by a focal modulation mechanism for modeling token interactions in vision. Focal modulation comprises three components: (i) hierarchical contextualization, implemented using a stack of depth-wise convolutional layers, to encode visual contexts from short to long ranges, (ii) gated aggregation to selectively gather contexts for each query token based on its
 content, and (iii) element-wise modulation or affine transformation to inject the aggregated context into the query. Extensive experiments show FocalNets outperform the state-of-the-art SA counterparts (e.g., Swin and Focal Transformers) with similar computational costs on the tasks of image classification, object detection, and segmentation. Specifically, FocalNets with tiny and base size achieve 82.3% and 83.9% top-1 accuracy on ImageNet-1K. After pretrained on ImageNet-22K in 224 resolution, it attains 86.5% and 87.3% top-1 accuracy when finetuned with resolution 224 and 384, respectively. When transferred to downstream tasks, FocalNets exhibit clear superiority. For object detection with Mask R-CNN, FocalNet base trained with 1\times outperforms the Swin counterpart by 2.1 points and already surpasses Swin trained with 3\times schedule (49.0 v.s. 48.5). For semantic segmentation with UPerNet, FocalNet base at single-scale outperforms Swin by 2.4, and beats Swin at multi-scale (50.5 v.s. 49.7). Using large FocalNet and Mask2former, we achieve 58.5 mIoU for ADE20K semantic segmentation, and 57.9 PQ for COCO Panoptic Segmentation. Using huge FocalNet and DINO, we achieved 64.3 and 64.4 mAP on COCO minival and test-dev, respectively, establishing new SoTA on top of much larger attention-based models like Swinv2-G and BEIT-3.*
 
-Tips:
-
-- One can use the [`AutoImageProcessor`] class to prepare images for the model.
-
 This model was contributed by [nielsr](https://huggingface.co/nielsr).
 The original code can be found [here](https://github.com/microsoft/FocalNet).
 
-
 ## FocalNetConfig
 
 [[autodoc]] FocalNetConfig

docs/source/en/model_doc/fsmt.md

@@ -16,9 +16,6 @@ rendered properly in your Markdown viewer.
 
 # FSMT
 
-**DISCLAIMER:** If you see something strange, file a [Github Issue](https://github.com/huggingface/transformers/issues/new?assignees=&labels=&template=bug-report.md&title) and assign
-@stas00.
-
 ## 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.

docs/source/en/model_doc/funnel.md

@@ -47,7 +47,9 @@ via a decoder. Empirically, with comparable or fewer FLOPs, Funnel-Transformer o
 a wide variety of sequence-level prediction tasks, including text classification, language understanding, and reading
 comprehension.*
 
-Tips:
+This model was contributed by [sgugger](https://huggingface.co/sgugger). The original code can be found [here](https://github.com/laiguokun/Funnel-Transformer).
+
+## Usage tips
 
 - Since Funnel Transformer uses pooling, the sequence length of the hidden states changes after each block of layers. This way, their length is divided by 2, which speeds up the computation of the next hidden states.
   The base model therefore has a final sequence length that is a quarter of the original one. This model can be used
@@ -62,9 +64,7 @@ Tips:
   [`FunnelBaseModel`], [`FunnelForSequenceClassification`] and
   [`FunnelForMultipleChoice`].
 
-This model was contributed by [sgugger](https://huggingface.co/sgugger). The original code can be found [here](https://github.com/laiguokun/Funnel-Transformer).
-
-## Documentation resources
+## Resources
 
 - [Text classification task guide](../tasks/sequence_classification)
 - [Token classification task guide](../tasks/token_classification)
@@ -95,6 +95,9 @@ This model was contributed by [sgugger](https://huggingface.co/sgugger). The ori
 
 [[autodoc]] models.funnel.modeling_tf_funnel.TFFunnelForPreTrainingOutput
 
+<frameworkcontent>
+<pt>
+
 ## FunnelBaseModel
 
 [[autodoc]] FunnelBaseModel
@@ -135,6 +138,9 @@ This model was contributed by [sgugger](https://huggingface.co/sgugger). The ori
 [[autodoc]] FunnelForQuestionAnswering
     - forward
 
+</pt>
+<tf>
+
 ## TFFunnelBaseModel
 
 [[autodoc]] TFFunnelBaseModel
@@ -174,3 +180,6 @@ This model was contributed by [sgugger](https://huggingface.co/sgugger). The ori
 
 [[autodoc]] TFFunnelForQuestionAnswering
     - call
+
+</tf>
+</frameworkcontent>

docs/source/en/model_doc/git.md

@@ -27,11 +27,6 @@ The abstract from the paper is the following:
 
 *In this paper, we design and train a Generative Image-to-text Transformer, GIT, to unify vision-language tasks such as image/video captioning and question answering. While generative models provide a consistent network architecture between pre-training and fine-tuning, existing work typically contains complex structures (uni/multi-modal encoder/decoder) and depends on external modules such as object detectors/taggers and optical character recognition (OCR). In GIT, we simplify the architecture as one image encoder and one text decoder under a single language modeling task. We also scale up the pre-training data and the model size to boost the model performance. Without bells and whistles, our GIT establishes new state of the arts on 12 challenging benchmarks with a large margin. For instance, our model surpasses the human performance for the first time on TextCaps (138.2 vs. 125.5 in CIDEr). Furthermore, we present a new scheme of generation-based image classification and scene text recognition, achieving decent performance on standard benchmarks.*
 
-Tips:
-
-- GIT is implemented in a very similar way to GPT-2, the only difference being that the model is also conditioned on `pixel_values`.
-- One can use [`GitProcessor`] to prepare images for the model, and the `generate` method for autoregressive generation.
-
 <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/git_architecture.jpg"
 alt="drawing" width="600"/>
 
@@ -40,6 +35,10 @@ alt="drawing" width="600"/>
 This model was contributed by [nielsr](https://huggingface.co/nielsr).
 The original code can be found [here](https://github.com/microsoft/GenerativeImage2Text).
 
+## Usage tips
+
+- GIT is implemented in a very similar way to GPT-2, the only difference being that the model is also conditioned on `pixel_values`.
+
 ## Resources
 
 A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with GIT.

docs/source/en/model_doc/glpn.md

@@ -33,10 +33,6 @@ The abstract from the paper is the following:
 
 *Depth estimation from a single image is an important task that can be applied to various fields in computer vision, and has grown rapidly with the development of convolutional neural networks. In this paper, we propose a novel structure and training strategy for monocular depth estimation to further improve the prediction accuracy of the network. We deploy a hierarchical transformer encoder to capture and convey the global context, and design a lightweight yet powerful decoder to generate an estimated depth map while considering local connectivity. By constructing connected paths between multi-scale local features and the global decoding stream with our proposed selective feature fusion module, the network can integrate both representations and recover fine details. In addition, the proposed decoder shows better performance than the previously proposed decoders, with considerably less computational complexity. Furthermore, we improve the depth-specific augmentation method by utilizing an important observation in depth estimation to enhance the model. Our network achieves state-of-the-art performance over the challenging depth dataset NYU Depth V2. Extensive experiments have been conducted to validate and show the effectiveness of the proposed approach. Finally, our model shows better generalisation ability and robustness than other comparative models.*
 
-Tips:
-
-- One can use [`GLPNImageProcessor`] to prepare images for the model.
-
 <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/glpn_architecture.jpg"
 alt="drawing" width="600"/>
 

docs/source/en/model_doc/gpt-sw3.md

@@ -32,12 +32,8 @@ causal language modeling (CLM) objective utilizing the NeMo Megatron GPT impleme
 
 This model was contributed by [AI Sweden](https://huggingface.co/AI-Sweden).
 
-The implementation uses the [GPT2Model](https://huggingface.co/docs/transformers/model_doc/gpt2) coupled
-with our `GPTSw3Tokenizer`. This means that `AutoTokenizer` and `AutoModelForCausalLM` map to our tokenizer
-implementation and the corresponding GPT2 model implementation respectively.
-*Note that sentencepiece is required to use our tokenizer and can be installed with:* `pip install transformers[sentencepiece]` or `pip install sentencepiece`
+## Usage example
 
-Example usage:
 ```python
 >>> from transformers import AutoTokenizer, AutoModelForCausalLM
 
@@ -52,12 +48,21 @@ Example usage:
 Träd är fina för att de är färgstarka. Men ibland är det fint
 ```
 
-## Documentation resources
+## Resources
 
 - [Text classification task guide](../tasks/sequence_classification)
 - [Token classification task guide](../tasks/token_classification)
 - [Causal language modeling task guide](../tasks/language_modeling)
 
+<Tip>
+
+The implementation uses the `GPT2Model` coupled with our `GPTSw3Tokenizer`. Refer to [GPT2Model documentation](gpt2) 
+for API reference and examples.  
+
+Note that sentencepiece is required to use our tokenizer and can be installed with `pip install transformers[sentencepiece]` or `pip install sentencepiece`
+
+</Tip>
+
 ## GPTSw3Tokenizer
 
 [[autodoc]] GPTSw3Tokenizer

docs/source/en/model_doc/gpt2.md

@@ -39,7 +39,13 @@ text. The diversity of the dataset causes this simple goal to contain naturally
 across diverse domains. GPT-2 is a direct scale-up of GPT, with more than 10X the parameters and trained on more than
 10X the amount of data.*
 
-Tips:
+[Write With Transformer](https://transformer.huggingface.co/doc/gpt2-large) is a webapp created and hosted by
+Hugging Face showcasing the generative capabilities of several models. GPT-2 is one of them and is available in five
+different sizes: small, medium, large, xl and a distilled version of the small checkpoint: *distilgpt-2*.
+
+This model was contributed by [thomwolf](https://huggingface.co/thomwolf). The original code can be found [here](https://openai.com/blog/better-language-models/).
+
+## Usage tips
 
 - GPT-2 is a model with absolute position embeddings so it's usually advised to pad the inputs on the right rather than
   the left.
@@ -54,12 +60,6 @@ Tips:
 - Enabling the *scale_attn_by_inverse_layer_idx* and *reorder_and_upcast_attn* flags will apply the training stability
   improvements from [Mistral](https://github.com/stanford-crfm/mistral/) (for PyTorch only).
 
-[Write With Transformer](https://transformer.huggingface.co/doc/gpt2-large) is a webapp created and hosted by
-Hugging Face showcasing the generative capabilities of several models. GPT-2 is one of them and is available in five
-different sizes: small, medium, large, xl and a distilled version of the small checkpoint: *distilgpt-2*.
-
-This model was contributed by [thomwolf](https://huggingface.co/thomwolf). The original code can be found [here](https://openai.com/blog/better-language-models/).
-
 ## Resources
 
 A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with GPT2. If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
@@ -100,6 +100,9 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
 
 [[autodoc]] models.gpt2.modeling_tf_gpt2.TFGPT2DoubleHeadsModelOutput
 
+<frameworkcontent>
+<pt>
+
 ## GPT2Model
 
 [[autodoc]] GPT2Model
@@ -130,6 +133,9 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
 [[autodoc]] GPT2ForTokenClassification
     - forward
 
+</pt>
+<tf>
+
 ## TFGPT2Model
 
 [[autodoc]] TFGPT2Model
@@ -158,6 +164,9 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
 
 [[autodoc]] TFGPT2Tokenizer
 
+</tf>
+<jax>
+
 ## FlaxGPT2Model
 
 [[autodoc]] FlaxGPT2Model
@@ -167,3 +176,6 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
 
 [[autodoc]] FlaxGPT2LMHeadModel
     - __call__
+
+</jax>
+</frameworkcontent>

docs/source/en/model_doc/gpt_bigcode.md

@@ -20,13 +20,13 @@ rendered properly in your Markdown viewer.
 
 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 abstract from the paper is the following:uery
+The abstract from the paper is the following:
 
 *The BigCode project is an open-scientific collaboration working on the responsible development of large language models for code. This tech report describes the progress of the collaboration until December 2022, outlining the current state of the Personally Identifiable Information (PII) redaction pipeline, the experiments conducted to de-risk the model architecture, and the experiments investigating better preprocessing methods for the training data. We train 1.1B parameter models on the Java, JavaScript, and Python subsets of The Stack and evaluate them on the MultiPL-E text-to-code benchmark. We find that more aggressive filtering of near-duplicates can further boost performance and, surprisingly, that selecting files from repositories with 5+ GitHub stars deteriorates performance significantly. Our best model outperforms previous open-source multilingual code generation models (InCoder-6.7B and CodeGen-Multi-2.7B) in both left-to-right generation and infilling on the Java, JavaScript, and Python portions of MultiPL-E, despite being a substantially smaller model. All models are released under an OpenRAIL license at [this https URL.](https://huggingface.co/bigcode)*
 
-The model is a an optimized [GPT2 model](https://huggingface.co/docs/transformers/model_doc/gpt2) with support for Multi-Query Attention.
+The model is an optimized [GPT2 model](https://huggingface.co/docs/transformers/model_doc/gpt2) with support for Multi-Query Attention.
 
-## Technical details
+## Implementation details
 
 The main differences compared to GPT2.
 - Added support for Multi-Query Attention.
@@ -85,7 +85,6 @@ Below is a expected speedup diagram that compares pure inference time between th
 
 [[autodoc]] GPTBigCodeConfig
 
-
 ## GPTBigCodeModel
 
 [[autodoc]] GPTBigCodeModel
@@ -96,7 +95,6 @@ Below is a expected speedup diagram that compares pure inference time between th
 [[autodoc]] GPTBigCodeForCausalLM
     - forward
 
-
 ## GPTBigCodeForSequenceClassification
 
 [[autodoc]] GPTBigCodeForSequenceClassification

docs/source/en/model_doc/gpt_neo.md

@@ -27,7 +27,7 @@ The architecture is similar to GPT2 except that GPT Neo uses local attention in
 
 This model was contributed by [valhalla](https://huggingface.co/valhalla).
 
-### Generation
+## Usage example
 
 The `generate()` method can be used to generate text using GPT Neo model.
 
@@ -54,7 +54,7 @@ The `generate()` method can be used to generate text using GPT Neo model.
 >>> gen_text = tokenizer.batch_decode(gen_tokens)[0]
 ```
 
-## Documentation resources
+## Resources
 
 - [Text classification task guide](../tasks/sequence_classification)
 - [Causal language modeling task guide](../tasks/language_modeling)
@@ -63,6 +63,10 @@ The `generate()` method can be used to generate text using GPT Neo model.
 
 [[autodoc]] GPTNeoConfig
 
+
+<frameworkcontent>
+<pt>
+
 ## GPTNeoModel
 
 [[autodoc]] GPTNeoModel
@@ -88,6 +92,9 @@ The `generate()` method can be used to generate text using GPT Neo model.
 [[autodoc]] GPTNeoForTokenClassification
     - forward
 
+</pt>
+<jax>
+
 ## FlaxGPTNeoModel
 
 [[autodoc]] FlaxGPTNeoModel
@@ -97,3 +104,8 @@ The `generate()` method can be used to generate text using GPT Neo model.
 
 [[autodoc]] FlaxGPTNeoForCausalLM
     - __call__
+
+</jax>
+</frameworkcontent>
+
+