Migrate doc files to Markdown. (#24376)

* Rename index.mdx to index.md

* With saved modifs

* Address review comment

* Treat all files

* .mdx -> .md

* Remove special char

* Update utils/tests_fetcher.py

Co-authored-by: Lysandre Debut <lysandre.debut@reseau.eseo.fr>

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Co-authored-by: Lysandre Debut <lysandre.debut@reseau.eseo.fr>

docs/source/en/tflite.md

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+<!--Copyright 2023 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.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+
+# Export to TFLite
+
+[TensorFlow Lite](https://www.tensorflow.org/lite/guide) is a lightweight framework for deploying machine learning models 
+on resource-constrained devices, such as mobile phones, embedded systems, and Internet of Things (IoT) devices. 
+TFLite is designed to optimize and run models efficiently on these devices with limited computational power, memory, and 
+power consumption.
+A TensorFlow Lite model is represented in a special efficient portable format identified by the `.tflite` file extension. 
+
+🤗 Optimum offers functionality to export 🤗 Transformers models to TFLite through the `exporters.tflite` module. 
+For the list of supported model architectures, please refer to [🤗 Optimum documentation](https://huggingface.co/docs/optimum/exporters/tflite/overview).
+
+To export a model to TFLite, install the required dependencies:
+ 
+```bash
+pip install optimum[exporters-tf]
+```
+
+To check out all available arguments, refer to the [🤗 Optimum docs](https://huggingface.co/docs/optimum/main/en/exporters/tflite/usage_guides/export_a_model), 
+or view help in command line:
+
+```bash
+optimum-cli export tflite --help
+```
+
+To export a model's checkpoint from the 🤗 Hub, for example, `bert-base-uncased`, run the following command:
+
+```bash
+optimum-cli export tflite --model bert-base-uncased --sequence_length 128 bert_tflite/
+```
+
+You should see the logs indicating progress and showing where the resulting `model.tflite` is saved, like this:
+
+```bash
+Validating TFLite model...
+	-[✓] TFLite model output names match reference model (logits)
+	- Validating TFLite Model output "logits":
+		-[✓] (1, 128, 30522) matches (1, 128, 30522)
+		-[x] values not close enough, max diff: 5.817413330078125e-05 (atol: 1e-05)
+The TensorFlow Lite export succeeded with the warning: The maximum absolute difference between the output of the reference model and the TFLite exported model is not within the set tolerance 1e-05:
+- logits: max diff = 5.817413330078125e-05.
+ The exported model was saved at: bert_tflite
+ ```
+
+The example above illustrates exporting a checkpoint from 🤗 Hub. When exporting a local model, first make sure that you 
+saved both the model's weights and tokenizer files in the same directory (`local_path`). When using CLI, pass the 
+`local_path` to the `model` argument instead of the checkpoint name on 🤗 Hub.