@@ -256,7 +256,7 @@ \part{Password Cracking}
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You do not even need to make them yourself anymore (if you don't want) because you can download them on the internet... they are not hard to find.
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- They are large, yes, but in the 25 - 900 GB range.
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+ They are large, yes, but in the 25-- 900 GB range.
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\end {frame }
@@ -297,7 +297,7 @@ \part{Password Cracking}
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\begin {itemize }
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\item Table generation on a GTX295 core for MD5 proceeds at around 430M links/sec.
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- \item Cracking a password 'K\#n\&r4Z' : real: 1m51.962s, user: 1m4.740s. sys: 0m15.320s
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+ \item Cracking a password 'K\#n\&r4Z' : \\ \qquad real: 1m51.962s, user: 1m4.740s. sys: 0m15.320s
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\end {itemize }
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Yikes.
@@ -426,7 +426,7 @@ \part{Large Language Models}
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Such large language models have existed before, but ChatGPT ended up a hit because it's pretty good at being `` conversational'' .
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- This is referred to as Natural Language Processing (NLP).
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+ This is within the ambit of Natural Language Processing (NLP).
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\end {frame }
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@@ -700,7 +700,7 @@ \part{Large Language Models}
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\begin {frame }
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\frametitle {Next Idea}
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- We seem to be memory limited -- let's see what we can do?
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+ We seem to be memory limited--- let's see what we can do?
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First idea: \alert {gradient accumulation}.
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@@ -731,7 +731,7 @@ \part{Large Language Models}
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\begin {frame }
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\frametitle {I got Suspicious}
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- I got suspicious about the 128 dropoff in memory usage and it made me think about other indicators -- is it getting worse somehow?
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+ I got suspicious about the 128 dropoff in memory usage and it made me think about other indicators--- is it getting worse somehow?
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The output talks about training loss...
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@@ -791,7 +791,7 @@ \part{Large Language Models}
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\end {tabular }
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\end {center }
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- Interesting results -- maybe a little concerning?
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+ Interesting results--- maybe a little concerning?
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\end {frame }
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