Merge pull request huggingface#1071 from csernakakos/patch-1

Update 6.mdx

Author: 7d1xfw5gs2ifmc

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"