Update 6.mdx

Fixes two typos

Author: csernakakos

chapters/en/chapter1/6.mdx

@@ -5,7 +5,7 @@
 
 # Transformer Architectures[[transformer-architectures]]
 
-In the previous sections, we introduced the general Transformer architecture and explored how these models can solve various tasks. Now, let's take a closer look at the three main architectural variants of Transformer models and understand when to use each one. Then, we looked at how those architectures are applied to different language tasks. 
+In the previous sections, we introduced the general Transformer architecture and explored how these models can solve various tasks. Now, let's take a closer look at the three main architectural variants of Transformer models and understand when to use each one. Then, we look at how those architectures are applied to different language tasks. 
 
 In this section, we're going to dive deeper into the three main architectural variants of Transformer models and understand when to use each one.
 
@@ -85,7 +85,7 @@ Modern decoder-based LLMs have demonstrated impressive capabilities:
 | Reasoning | Working through problems step by step | Solving math problems or logical puzzles |
 | Few-shot learning | Learning from a few examples in the prompt | Classifying text after seeing just 2-3 examples |
 
-You can experiment with decoder-based LLMs directly in your browser via model repo pages on the Hub. Here's an an example with the classic [GPT-2](https://huggingface.co/openai-community/gpt2) (OpenAI's finest open source model!):
+You can experiment with decoder-based LLMs directly in your browser via model repo pages on the Hub. Here's an example with the classic [GPT-2](https://huggingface.co/openai-community/gpt2) (OpenAI's finest open source model!):
 
 <iframe
 	src="https://huggingface.co/openai-community/gpt2"
@@ -221,4 +221,4 @@ in E2.
 
 In this section, we've explored the three main Transformer architectures and some specialized attention mechanisms. Understanding these architectural differences is crucial for selecting the right model for your specific NLP task.
 
-As we move forward in the course, you'll get hands-on experience with these different architectures and learn how to fine-tune them for your specific needs. In the next section, we'll look at some of the limitations and biases present in these models that you should be aware of when deploying them.
+As we move forward in the course, you'll get hands-on experience with these different architectures and learn how to fine-tune them for your specific needs. In the next section, we'll look at some of the limitations and biases present in these models that you should be aware of when deploying them.