Attention in transformers, step-by-step | DL6

Demystifying attention, the key mechanism inside transformers and LLMs.
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Demystifying self-attention, multiple heads, and cross-attention.
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The first pass for the translated subtitles here is machine-generated and, therefore, notably imperfect. To contribute edits or fixes, visit www.criblate.com/

Звуковая дорожка на русском языке: Влад Бурмистров.

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Here are a few other relevant resources

Build a GPT from scratch, by Andrej Karpathy
   • Let's build GPT: from scratch, in code, sp...  

If you want a conceptual understanding of language models from the ground up, @vcubingx just started a short series of videos on the topic:
   • What does it mean for computers to underst...  

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.
transformer-circuits.pub/2021/framework/index.html

Site with exercises related to ML programming and GPTs
www.gptandchill.ai/codingproblems

History of language models by Brit Cruise,  @ArtOfTheProblem 
   • The 35 Year History of ChatGPT  

An early paper on how directions in embedding spaces have meaning:
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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github.com/ManimCommunity/manim/

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The music is by Vincent Rubinetti.
www.vincentrubinetti.com/
vincerubinetti.bandcamp.com/album/the-music-of-3bl…
open.spotify.com/album/1dVyjwS8FBqXhRunaG5W5u

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