How Decoder-Only Transformers (like GPT) Work

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Learn about encoders, cross attention and masking for LLMs as SuperDataScience Founder Kirill Eremenko returns to the SuperDataScience podcast, to speak with @JonKrohnLearns about transformer architectures and why they are a new frontier for generative AI. If you’re interested in applying LLMs to your business portfolio, you’ll want to pay close attention to this episode!

  1. Super Data Science: ML & AI Podcast with Jon Krohn
  2. How Decoder-Only Transformers (like GPT) Work
  3. transformer encoder decoder explained
  4. encoder decoder transformer
  5. transformer architecture llm
  6. transformer architecture nlp
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Value vectors are scaled by the amount of attention weights. Say weight is 6. Then value vector[1, 2, 3] * 6 is [6, 12, 18]
Attention weights are achieved by dot product of Q. K. Inner product will be always a scaler 6 in our case

Ash-bcvw
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...I listened to Podcast #747 at least 10 times ! I wish every policy maker and general manager listened to Podcast #747 - the podcast will dispel any confusion about the term "AI" and other anthropomorphic terms as the "pre-training", "training" and other terms that may seem to you that are humanistic in nature. It's really just assigning number values to words. Then using statistics you area able to attribute a "vector" point in space. Therefore since space is limitless then can and assign a huge number of parameters the show the location of that vector point and all the points near the point and far away from the point.

energyexecs
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Masked self attention should be discussed here

Ash-bcvw