[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/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>
 
-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.
+Refer to [T5's documentation page](t5) for API reference, tips, code examples and notebooks. 
+
+</Tip>