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Transformers (how LLMs work) explained visually | DL5
Breaking down how Large Language Models work
Instead of sponsored ad reads, these lessons are funded directly by viewers: https://3b1b.co/support
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
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
Звуковая дорожка на русском языке: Влад Бурмистров.
Timestamps
0:00 - Predict, sample, repeat
3:03 - Inside a transformer
6:36 - Chapter layout
7:20 - The premise of Deep Learning
12:27 - Word embeddings
18:25 - Embeddings beyond words
20:22 - Unembedding
22:22 - Softmax with temperature
26:03 - Up next
View all episodes
Breaking down how Large Language Models work
Instead of sponsored ad reads, these lessons are funded directly by viewers: https://3b1b.co/support
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
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
Звуковая дорожка на русском языке: Влад Бурмистров.
Timestamps
0:00 - Predict, sample, repeat
3:03 - Inside a transformer
6:36 - Chapter layout
7:20 - The premise of Deep Learning
12:27 - Word embeddings
18:25 - Embeddings beyond words
20:22 - Unembedding
22:22 - Softmax with temperature
26:03 - Up next