diff --git a/docs/source/en/model_doc/llava_next.md b/docs/source/en/model_doc/llava_next.md
index 0c25ed32db..d0558be764 100644
--- a/docs/source/en/model_doc/llava_next.md
+++ b/docs/source/en/model_doc/llava_next.md
@@ -55,12 +55,12 @@ The original code can be found [here](https://github.com/haotian-liu/LLaVA/tree/
 
 - Note that each checkpoint has been trained with a specific prompt format, depending on which large language model (LLM) was used. You can use the processor's `apply_chat_template` to format your prompts correctly. For that you have to construct a conversation history, passing a plain string will not format your prompt. Each message in the conversation history for chat templates is a dictionary with keys "role" and "content". The "content" should be a list of dictionaries, for "text" and "image" modalities. Below is an example of how to do that and the list of formats accepted by each checkpoint.
 
-We will use [llava-v1.6-mistral-7b-hf](https://huggingface.co/llava-hf/llava-hf/llava-v1.6-mistral-7b-hf) and a conversation history of text and image. Each content field has to be a list of dicts, as follows:
+We will use [llava-v1.6-mistral-7b-hf](https://huggingface.co/llava-hf/llava-v1.6-mistral-7b-hf) and a conversation history of text and image. Each content field has to be a list of dicts, as follows:
 
 ```python
 from transformers import LlavaNextProcessor
 
-processor = LlavaNextProcessor.from_pretrained("llava-hf/llava-hf/llava-v1.6-mistral-7b-hf")
+processor = LlavaNextProcessor.from_pretrained("llava-hf/llava-v1.6-mistral-7b-hf")
 
 conversation = [
     {