fix style

docs/source/en/llm_optims.md

@@ -29,7 +29,7 @@ To optimize this, you can use a kv-cache to store the past keys and values inste
 The *static kv-cache* solves this issue by pre-allocating the kv-cache size to a maximum value which allows you to combine it with torch.compile for up to a 4x speed up.
 
 > [!WARNING]
-> Currently, only [Command R](./model_doc/cohere), [Gemma](./model_doc/gemma), [Llama](./model_doc/llama2) and [Mistral](./model_doc/mistral.md) models support static kv-cache and torch.compile.
+> Currently, only [Llama](./model_doc/llama2) and a few other models support static kv-cache and torch.compile. Check [this issue](https://github.com/huggingface/transformers/issues/28981) for a live model compatibility list.
 
 For this example, let's load the [Gemma](https://hf.co/google/gemma-2b) model.
 

examples/pytorch/object-detection/run_object_detection.py

@@ -244,9 +244,7 @@ class DataTrainingArguments:
     )
     image_square_size: Optional[int] = field(
         default=600,
-        metadata={
-            "help": "Image longest size will be resized to this value, then image will be padded to square."
-        },
+        metadata={"help": "Image longest size will be resized to this value, then image will be padded to square."},
     )
     max_train_samples: Optional[int] = field(
         default=None,