Merge pull request #858 from sergiopaniego/fix-name

Fixed links inside `<Tip>` rendering

Author: burtenshaw

chapters/en/chapter12/1.mdx

@@ -79,7 +79,7 @@ Don't worry if you're missing some of these – we'll explain key concepts as we
 
 <Tip>
 
-If you don't have all the prerequisites, check out this [course](chapter1/1.mdx) from units 1 to 11
+If you don't have all the prerequisites, check out this [course](chapter1/1.mdx) from units 1 to 11.
 
 </Tip>
 

chapters/en/chapter12/2.mdx

@@ -5,7 +5,9 @@ Welcome to the first page!
 We're going to start our journey into the exciting world of Reinforcement Learning (RL) and discover how it's revolutionizing the way we train Language Models like the ones you might use every day.
 
 <Tip>
+
 In this chapter, we are focusing on reinforcement learning for language models. However, reinforcement learning is a broad field with many applications beyond language models. If you're interested in learning more about reinforcement learning, you should check out the [Deep Reinforcement Learning course](https://huggingface.co/courses/deep-rl-course/en/unit1/introduction).
+
 </Tip>
 
 This page will give you a friendly and clear introduction to RL, even if you've never encountered it before. We'll break down the core ideas and see why RL is becoming so important in the field of Large Language Models (LLMs).

chapters/en/chapter12/3.mdx

@@ -11,7 +11,9 @@ In the next chapter, we will build on this knowledge and implement GRPO in pract
 The initial goal of the paper was to explore whether pure reinforcement learning could develop reasoning capabilities without supervised fine-tuning. 
 
 <Tip>
+
 Up until that point, all the popular LLMs required some supervised fine-tuning, which we explored in [chapter 11](/chapters/en/chapter11/1).
+
 </Tip>
 
 ## The Breakthrough 'Aha' Moment

chapters/en/chapter12/3a.mdx

@@ -1,7 +1,9 @@
 # Advanced Understanding of Group Relative Policy Optimization (GRPO) in DeepSeekMath
 
 <Tip>
+
 This section dives into the technical and mathematical details of GRPO. It was authored by [Shirin Yamani](https://github.com/shirinyamani).
+
 </Tip>
 
 Let's deepen our understanding of GRPO so that we can improve our model's training process.

chapters/en/chapter12/4.mdx

@@ -5,7 +5,9 @@ In this page, we'll learn how to implement Group Relative Policy Optimization (G
 We'll explore the core concepts of GRPO as they are embodied in TRL's GRPOTrainer, using snippets from the official TRL documentation to guide us.
 
 <Tip>
+
 This chapter is aimed at TRL beginners. If you are already familiar with TRL, you might want to also check out the [Open R1 implementation](https://github.com/huggingface/open-r1/blob/main/src/open_r1/grpo.py) of GRPO.
+
 </Tip>
 
 First, let's remind ourselves of some of the important concepts of GRPO algorithm:

chapters/en/chapter12/5.mdx

@@ -9,7 +9,9 @@
 Now that you've seen the theory, let's put it into practice! In this exercise, you'll fine-tune a model with GRPO.
 
 <Tip>
+
 This exercise was written by LLM fine-tuning expert [@mlabonne](https://huggingface.co/mlabonne).
+
 </Tip>
 
 ## Install dependencies

chapters/en/chapter12/6.mdx

@@ -71,7 +71,9 @@ model = FastLanguageModel.get_peft_model(
 This code loads the model in 4-bit quantization to save memory and applies LoRA (Low-Rank Adaptation) for efficient fine-tuning. The `target_modules` parameter specifies which layers of the model to fine-tune, and `use_gradient_checkpointing` enables training with longer contexts.
 
 <Tip>
+
 We won't cover the details of LoRA in this chapter, but you can learn more in [Chapter 11](/en/chapter11/3).
+
 </Tip>
 
 ## Data Preparation