Visualizing Attention, a Transformer's Heart | Chapter 6, Deep Learning
Open with LBRYDemystifying attention, the key mechanism inside transformers and LLMs.
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Demystifying self-attention, multiple heads, and cross-attention.
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Here are a few other relevant resources
Build a GPT from scratch, by Andrej Karpathy
https://youtu.be/kCc8FmEb1nY
If you want a conceptual understanding of language models from the ground up, @vcubingx just started a short series of videos on the topic:
https://youtu.be/1il-s4mgNdI?si=XaVxj6bsdy3VkgEX
If you're interested in the herculean task of interpreting what these large networks might actually be doing, the Transformer Circuits posts by Anthropic are great. In particular, it was only after reading one of these that I started thinking of the combination of the value and output matrices as being a combined low-rank map from the embedding space to itself, which, at least in my mind, made things much clearer than other sources.
https://transformer-circuits.pub/2021/framework/index.html
Site with exercises related to ML programming and GPTs
https://www.gptandchill.ai/codingproblems
History of language models by Brit Cruise, @ArtOfTheProblem
https://youtu.be/OFS90-FX6pg
An early paper on how directions in embedding spaces have meaning:
https://arxiv.org/pdf/1301.3781.pdf
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Timestamps:
0:00 - Recap on embeddings
1:39 - Motivating examples
4:29 - The attention pattern
11:08 - Masking
12:42 - Context size
13:10 - Values
15:44 - Counting parameters
18:21 - Cross-attention
19:19 - Multiple heads
22:16 - The output matrix
23:19 - Going deeper
24:54 - Ending
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