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hackerllama - The Random Transformer

Understand how transformers work by demystifying all the math behind them

The Illustrated GPT-2 (Visualizing Transformer Language Models)

Discussions: Hacker News (64 points, 3 comments), Reddit r/MachineLearning (219 points, 18 comments) Translations: Simplified Chinese, French, Korean, Russian, Turkish This year, we saw a dazzling application of machine learning. The OpenAI GPT-2 exhibited impressive ability of writing coherent and passionate essays that exceed what we anticipated current language models are able to produce. The GPT-2 wasn’t a particularly novel architecture – it’s architecture is very similar to the decoder-only transformer. The GPT2 was, however, a very large, transformer-based language model trained on a massive dataset. In this post, we’ll look at the architecture that enabled the model to produce its results. We will go into the depths of its self-attention layer. And then we’ll look at applications for the decoder-only transformer beyond language modeling. My goal here is to also supplement my earlier post, The Illustrated Transformer, with more visuals explaining the inner

GitHub - mlabonne/llm-course: Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks

Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks. - GitHub - mlabonne/llm-course: Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.

I made a transformer by hand (no training!)

To better understand how transformers work, I hand-assigned all the weights to predict a simple sequence.

Ask HN: Can someone ELI5 Transformers and the Attention is all we need paper

Ask HN: Can someone ELI5 Transformers and the Attention is all we need paper
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