Convert model files from rst to mdx (#14865)

* First pass

* Apply suggestions from code review

* Apply suggestions from code review

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

docs/source/model_doc/distilbert.mdx

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+<!--Copyright 2020 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+
+# DistilBERT
+
+## Overview
+
+The DistilBERT model was proposed in the blog post [Smaller, faster, cheaper, lighter: Introducing DistilBERT, a
+distilled version of BERT](https://medium.com/huggingface/distilbert-8cf3380435b5), and the paper [DistilBERT, a
+distilled version of BERT: smaller, faster, cheaper and lighter](https://arxiv.org/abs/1910.01108). DistilBERT is a
+small, fast, cheap and light Transformer model trained by distilling BERT base. It has 40% less parameters than
+*bert-base-uncased*, runs 60% faster while preserving over 95% of BERT's performances as measured on the GLUE language
+understanding benchmark.
+
+The abstract from the paper is the following:
+
+*As Transfer Learning from large-scale pre-trained models becomes more prevalent in Natural Language Processing (NLP),
+operating these large models in on-the-edge and/or under constrained computational training or inference budgets
+remains challenging. In this work, we propose a method to pre-train a smaller general-purpose language representation
+model, called DistilBERT, which can then be fine-tuned with good performances on a wide range of tasks like its larger
+counterparts. While most prior work investigated the use of distillation for building task-specific models, we leverage
+knowledge distillation during the pretraining phase and show that it is possible to reduce the size of a BERT model by
+40%, while retaining 97% of its language understanding capabilities and being 60% faster. To leverage the inductive
+biases learned by larger models during pretraining, we introduce a triple loss combining language modeling,
+distillation and cosine-distance losses. Our smaller, faster and lighter model is cheaper to pre-train and we
+demonstrate its capabilities for on-device computations in a proof-of-concept experiment and a comparative on-device
+study.*
+
+Tips:
+
+- DistilBERT doesn't have `token_type_ids`, you don't need to indicate which token belongs to which segment. Just
+  separate your segments with the separation token `tokenizer.sep_token` (or `[SEP]`).
+- DistilBERT doesn't have options to select the input positions (`position_ids` input). This could be added if
+  necessary though, just let us know if you need this option.
+
+This model was contributed by [victorsanh](https://huggingface.co/victorsanh). This model jax version was
+contributed by [kamalkraj](https://huggingface.co/kamalkraj). The original code can be found [here](https://github.com/huggingface/transformers/tree/master/examples/research_projects/distillation).
+
+
+## DistilBertConfig
+
+[[autodoc]] DistilBertConfig
+
+## DistilBertTokenizer
+
+[[autodoc]] DistilBertTokenizer
+
+## DistilBertTokenizerFast
+
+[[autodoc]] DistilBertTokenizerFast
+
+## DistilBertModel
+
+[[autodoc]] DistilBertModel
+    - forward
+
+## DistilBertForMaskedLM
+
+[[autodoc]] DistilBertForMaskedLM
+    - forward
+
+## DistilBertForSequenceClassification
+
+[[autodoc]] DistilBertForSequenceClassification
+    - forward
+
+## DistilBertForMultipleChoice
+
+[[autodoc]] DistilBertForMultipleChoice
+    - forward
+
+## DistilBertForTokenClassification
+
+[[autodoc]] DistilBertForTokenClassification
+    - forward
+
+## DistilBertForQuestionAnswering
+
+[[autodoc]] DistilBertForQuestionAnswering
+    - forward
+
+## TFDistilBertModel
+
+[[autodoc]] TFDistilBertModel
+    - call
+
+## TFDistilBertForMaskedLM
+
+[[autodoc]] TFDistilBertForMaskedLM
+    - call
+
+## TFDistilBertForSequenceClassification
+
+[[autodoc]] TFDistilBertForSequenceClassification
+    - call
+
+## TFDistilBertForMultipleChoice
+
+[[autodoc]] TFDistilBertForMultipleChoice
+    - call
+
+## TFDistilBertForTokenClassification
+
+[[autodoc]] TFDistilBertForTokenClassification
+    - call
+
+## TFDistilBertForQuestionAnswering
+
+[[autodoc]] TFDistilBertForQuestionAnswering
+    - call
+
+## FlaxDistilBertModel
+
+[[autodoc]] FlaxDistilBertModel
+    - __call__
+
+## FlaxDistilBertForMaskedLM
+
+[[autodoc]] FlaxDistilBertForMaskedLM
+    - __call__
+
+## FlaxDistilBertForSequenceClassification
+
+[[autodoc]] FlaxDistilBertForSequenceClassification
+    - __call__
+
+## FlaxDistilBertForMultipleChoice
+
+[[autodoc]] FlaxDistilBertForMultipleChoice
+    - __call__
+
+## FlaxDistilBertForTokenClassification
+
+[[autodoc]] FlaxDistilBertForTokenClassification
+    - __call__
+
+## FlaxDistilBertForQuestionAnswering
+
+[[autodoc]] FlaxDistilBertForQuestionAnswering
+    - __call__