diff --git a/ch04/01_main-chapter-code/ch04.ipynb b/ch04/01_main-chapter-code/ch04.ipynb
index 4a4c1da..26b9506 100644
--- a/ch04/01_main-chapter-code/ch04.ipynb
+++ b/ch04/01_main-chapter-code/ch04.ipynb
@@ -106,7 +106,7 @@
    "source": [
     "- In previous chapters, we used small embedding dimensions for token inputs and outputs for ease of illustration, ensuring they fit on a single page\n",
     "- In this chapter, we consider embedding and model sizes akin to a small GPT-2 model\n",
-    "- We'll specifically code the architecture of the smallest GPT-2 model (124 million parameters), as outlined in Radford et al.'s [Language Models are Unsupervised Multitask Learners](https://scholar.google.com/citations?view_op=view_citation&hl=en&user=dOad5HoAAAAJ&citation_for_view=dOad5HoAAAAJ:YsMSGLbcyi4C) (note that the initial report lists it as 117M parameters, but this was later corrected in the model weight repository)\n",
+    "- We'll specifically code the architecture of the smallest GPT-2 model (124 million parameters), as outlined in Radford et al.'s [Language Models are Unsupervised Multitask Learners](https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) (note that the initial report lists it as 117M parameters, but this was later corrected in the model weight repository)\n",
     "- Chapter 6 will show how to load pretrained weights into our implementation, which will be compatible with model sizes of 345, 762, and 1542 million parameters"
    ]
   },
@@ -1509,7 +1509,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.10.10"
   }
  },
  "nbformat": 4,