[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/mluke.md

@@ -37,6 +37,10 @@ representations into the input allows us to extract more language-agnostic featu
 multilingual cloze prompt task with the mLAMA dataset. We show that entity-based prompt elicits correct factual
 knowledge more likely than using only word representations.*
 
+This model was contributed by [ryo0634](https://huggingface.co/ryo0634). The original code can be found [here](https://github.com/studio-ousia/luke).
+
+## Usage tips
+
 One can directly plug in the weights of mLUKE into a LUKE model, like so:
 
 ```python
@@ -53,10 +57,12 @@ from transformers import MLukeTokenizer
 tokenizer = MLukeTokenizer.from_pretrained("studio-ousia/mluke-base")
 ```
 
+<Tip>
+
 As mLUKE's architecture is equivalent to that of LUKE, one can refer to [LUKE's documentation page](luke) for all
 tips, code examples and notebooks.
 
-This model was contributed by [ryo0634](https://huggingface.co/ryo0634). The original code can be found [here](https://github.com/studio-ousia/luke).
+</Tip>
 
 ## MLukeTokenizer