Merge pull request #1080 from gkmngrgn/patch-1

LLM Course, Chapter 1 - 6: Fix internal doc links.

Author: lunarflu

chapters/en/chapter1/6.mdx

@@ -28,7 +28,7 @@ Encoder models are best suited for tasks requiring an understanding of the full
 
 <Tip>
 
-As we saw in [How 🤗 Transformers solve tasks](/chapter1/5), encoder models like BERT excel at understanding text because they can look at the entire context in both directions. This makes them perfect for tasks where comprehension of the whole input is important.
+As we saw in [How 🤗 Transformers solve tasks](https://huggingface.co/learn/llm-course/chapter1/5), encoder models like BERT excel at understanding text because they can look at the entire context in both directions. This makes them perfect for tasks where comprehension of the whole input is important.
 
 </Tip>
 
@@ -50,7 +50,7 @@ These models are best suited for tasks involving text generation.
 
 <Tip>
 
-Decoder models like GPT are designed to generate text by predicting one token at a time. As we explored in [How 🤗 Transformers solve tasks](/chapter1/5), they can only see previous tokens, which makes them excellent for creative text generation but less ideal for tasks requiring bidirectional understanding.
+Decoder models like GPT are designed to generate text by predicting one token at a time. As we explored in [How 🤗 Transformers solve tasks](https://huggingface.co/learn/llm-course/chapter1/5), they can only see previous tokens, which makes them excellent for creative text generation but less ideal for tasks requiring bidirectional understanding.
 
 </Tip>
 
@@ -106,7 +106,7 @@ Sequence-to-sequence models are best suited for tasks revolving around generatin
 
 <Tip>
 
-As we saw in [How 🤗 Transformers solve tasks](/chapter1/5), encoder-decoder models like BART and T5 combine the strengths of both architectures. The encoder provides deep bidirectional understanding of the input, while the decoder generates appropriate output text. This makes them perfect for tasks that transform one sequence into another, like translation or summarization.  
+As we saw in [How 🤗 Transformers solve tasks](https://huggingface.co/learn/llm-course/chapter1/5), encoder-decoder models like BART and T5 combine the strengths of both architectures. The encoder provides deep bidirectional understanding of the input, while the decoder generates appropriate output text. This makes them perfect for tasks that transform one sequence into another, like translation or summarization.  
 
 </Tip>
 
@@ -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.