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_posts/2023-06-20-gpt2-torch/gpt2.Rmd

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-Whatever your take on Large Language Models (LLMs) -- are they beneficial? dangerous? a short-lived fashion, like crypto? -- they are *here*, *now*. And that means, it is a good thing to know (at a level one needs to decide for oneself) how they work. Two days ago, I published [TBD](file:///home/key/code/rstudio/ai-blog/_posts/2023-06-20-llm-intro), intended for a more general audience, not necessarily too familiar with deep learning. Today, I'd like to address deep learning practitioners, walking through a `torch` implementation of GPT-2 [@Radford2019LanguageMA], the second in OpenAI's succession of ever-larger models trained on ever-more-vast text corpora. You'll see that a complete model implementation fits in fewer than 250 lines of R code.
+Whatever your take on Large Language Models (LLMs) -- are they beneficial? dangerous? a short-lived fashion, like crypto? -- they are *here*, *now*. And that means, it is a good thing to know (at a level one needs to decide for oneself) how they work. On this same day, I am publishing [What are Large Language Models? What are they not?](https://blogs.rstudio.com/ai/posts/2023-06-20-llm-intro), intended for a more general audience. In this post, I'd like to address deep learning practitioners, walking through a `torch` implementation of GPT-2 [@Radford2019LanguageMA], the second in OpenAI's succession of ever-larger models trained on ever-more-vast text corpora. You'll see that a complete model implementation fits in fewer than 250 lines of R code.
 
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