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update gpt-2 paper url

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Sebastian Raschka 2024-09-20 07:00:06 -07:00
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ch04/01_main-chapter-code/ch04.ipynb
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@ -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,