Convert model files from rst to mdx (#14865)

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

docs/source/model_doc/ibert.mdx

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+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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+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.
+-->
+
+# I-BERT
+
+## Overview
+
+The I-BERT model was proposed in [I-BERT: Integer-only BERT Quantization](https://arxiv.org/abs/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.
+
+The abstract from the paper is the following:
+
+*Transformer based models, like BERT and RoBERTa, have achieved state-of-the-art results in many Natural Language
+Processing tasks. However, their memory footprint, inference latency, and power consumption are prohibitive for
+efficient inference at the edge, and even at the data center. While quantization can be a viable solution for this,
+previous work on quantizing Transformer based models use floating-point arithmetic during inference, which cannot
+efficiently utilize integer-only logical units such as the recent Turing Tensor Cores, or traditional integer-only ARM
+processors. In this work, we propose I-BERT, a novel quantization scheme for Transformer based models that quantizes
+the entire inference with integer-only arithmetic. Based on lightweight integer-only approximation methods for
+nonlinear operations, e.g., GELU, Softmax, and Layer Normalization, I-BERT performs an end-to-end integer-only BERT
+inference without any floating point calculation. We evaluate our approach on GLUE downstream tasks using
+RoBERTa-Base/Large. We show that for both cases, I-BERT achieves similar (and slightly higher) accuracy as compared to
+the full-precision baseline. Furthermore, our preliminary implementation of I-BERT shows a speedup of 2.4 - 4.0x for
+INT8 inference on a T4 GPU system as compared to FP32 inference. The framework has been developed in PyTorch and has
+been open-sourced.*
+
+This model was contributed by [kssteven](https://huggingface.co/kssteven). The original code can be found [here](https://github.com/kssteven418/I-BERT).
+
+
+## IBertConfig
+
+[[autodoc]] IBertConfig
+
+## IBertModel
+
+[[autodoc]] IBertModel
+    - forward
+
+## IBertForMaskedLM
+
+[[autodoc]] IBertForMaskedLM
+    - forward
+
+## IBertForSequenceClassification
+
+[[autodoc]] IBertForSequenceClassification
+    - forward
+
+## IBertForMultipleChoice
+
+[[autodoc]] IBertForMultipleChoice
+    - forward
+
+## IBertForTokenClassification
+
+[[autodoc]] IBertForTokenClassification
+    - forward
+
+## IBertForQuestionAnswering
+
+[[autodoc]] IBertForQuestionAnswering
+    - forward