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0:43
YouTube
Syntax
The full pipeline of LLMs explained #LLM #ML #Programming #Explainer #howitsmade
How does an LLM work? Your prompt gets broken into tokens, vectorized / embedded and passed through transformer blocks. A probability distribution comes out the other end, we sample to pick a token, append the token to the input prompt and repeat. Watch the full video here: https://www.youtube.com/watch?v=YmLp8qe87A0 ...
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Andrej Karpathy's explaination of Bigram Language Model explained Bigram LMs, though simple, it provides powerful insight into the inner mechanics of how tokens are processed in language models. This is a pre-amble for what's next: GPT, which is the 2nd part of the series The Bigram model here incorporates: Tokenization, Vocabulary, Negative Loss Function, Cross Entropy, Logits, SoftMax, Optimizer, and AdamW These are essential ingredients to understand in order to build our knowledge on how LLM
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