Nits for model docs (#29795)

* Update llava_next.md

* Update seggpt.md

docs/source/en/model_doc/llava_next.md

@@ -98,7 +98,7 @@ print(processor.decode(output[0], skip_special_tokens=True))
 
 ### Quantization using Bitsandbytes
 
-The model can be loaded in 8 or 4 bits, greatly reducing the memory requirements while maintaining the performance of the original model. First make sure to install bitsandbytes, `pip install bitsandbytes`` and make sure to have access to a CUDA compatible GPU device. Simply change the snippet above with:
+The model can be loaded in 8 or 4 bits, greatly reducing the memory requirements while maintaining the performance of the original model. First make sure to install bitsandbytes, `pip install bitsandbytes` and make sure to have access to a CUDA compatible GPU device. Simply change the snippet above with:
 
 ```python
 from transformers import LlavaNextForConditionalGeneration, BitsAndBytesConfig

docs/source/en/model_doc/seggpt.md

@@ -36,7 +36,7 @@ import torch
 from datasets import load_dataset
 from transformers import SegGptImageProcessor, SegGptForImageSegmentation
 
-model_id = "BAAI/seggpt-vit-large"
+checkpoint = "BAAI/seggpt-vit-large"
 image_processor = SegGptImageProcessor.from_pretrained(checkpoint)
 model = SegGptForImageSegmentation.from_pretrained(checkpoint)