diff --git a/chapters/en/chapter1/4.mdx b/chapters/en/chapter1/4.mdx
index 3870b541f..22a06a5c1 100644
--- a/chapters/en/chapter1/4.mdx
+++ b/chapters/en/chapter1/4.mdx
@@ -28,14 +28,13 @@ The [Transformer architecture](https://arxiv.org/abs/1706.03762) was introduced
 
 - **October 2018**: [BERT](https://arxiv.org/abs/1810.04805), another large pretrained model, this one designed to produce better summaries of sentences (more on this in the next chapter!)
 
-- **February 2019**: [GPT-2](https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf), an improved (and bigger) version of GPT that was not immediately publicly released due to ethical concerns
+- **February 2019**: [GPT-2](https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf), an improved (and bigger) version of GPT that was not immediately publicly released due to ethical concerns.
 
 - **October 2019**: [T5](https://huggingface.co/papers/1910.10683), A multi-task focused implementation of the sequence-to-sequence Transformer architecture.
 
 - **May 2020**, [GPT-3](https://huggingface.co/papers/2005.14165), an even bigger version of GPT-2 that is able to perform well on a variety of tasks without the need for fine-tuning (called _zero-shot learning_)
 
-- **January 2022**: [InstructGPT](https://huggingface.co/papers/2203.02155), a version of GPT-3 that was trained to follow instructions better
-This list is far from comprehensive, and is just meant to highlight a few of the different kinds of Transformer models. Broadly, they can be grouped into three categories:
+- **January 2022**: [InstructGPT](https://huggingface.co/papers/2203.02155), a version of GPT-3 that was trained to follow instructions better.
 
 - **January 2023**: [Llama](https://huggingface.co/papers/2302.13971), a large language model that is able to generate text in a variety of languages.
 
@@ -45,6 +44,7 @@ This list is far from comprehensive, and is just meant to highlight a few of the
 
 - **November 2024**: [SmolLM2](https://huggingface.co/papers/2502.02737), a state-of-the-art small language model (135 million to 1.7 billion parameters) that achieves impressive performance despite its compact size, and unlocking new possibilities for mobile and edge devices.
 
+This list is far from comprehensive, and is just meant to highlight a few of the different kinds of Transformer models. Broadly, they can be grouped into three categories:
 - GPT-like (also called _auto-regressive_ Transformer models)
 - BERT-like (also called _auto-encoding_ Transformer models) 
 - T5-like (also called _sequence-to-sequence_ Transformer models)