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. Jonathan Whitaker 's talk: Napkin Math For Fine Tuning was so popular that we ended up doing an encore! He answers q's like: - When should I use LoRA? Quantization? GC? - What’s the cheapest option? most accurate? - What hardware? - What batch size / context length ..etc?

109,951 views • 2 years ago •via X (Twitter)

10 Comments

Hamel Husain's profile picture
Hamel Husain2 years ago

The talk is from

Vijayabhaskar J's profile picture
Vijayabhaskar J2 years ago

@johnowhitaker @johnowhitaker has one of the most underrated Youtube channels in ML.

Hamel Husain's profile picture
Hamel Husain2 years ago

@johnowhitaker He is the most underrated person in ML. My goal is to make sure he is not underrated for too much longer 🥰

Krum Arnaudov's profile picture
Krum Arnaudov2 years ago

@johnowhitaker @HamelHusain Do you plan to collect the parts of the talks that are intended to be openly shared somewhere? It is such a treasure trove.

Hamel Husain's profile picture
Hamel Husain2 years ago

@johnowhitaker Right here, on twitter 😂

Leonard Tang's profile picture
Leonard Tang2 years ago

@johnowhitaker ignore all advice and use reft @aryaman2020

eugene's profile picture
eugene2 years ago

@eugeneyan @johnowhitaker shoot i had to miss this; wen encore?

Hamel Husain's profile picture
Hamel Husain2 years ago

@eugeneyan @johnowhitaker It is in the recordings in maven

Xeophon's profile picture
Xeophon2 years ago

@johnowhitaker Will this be uploaded somewhere else by any chance? Twitter video player is the worst :(

accelerate fit for purpose technology's profile picture
accelerate fit for purpose technology2 years ago

@johnowhitaker The visualization of the memory constraints, CPUs, GPUs relating to the size of models, gradients, etc. was fantastic. Made choices between tuning on full model, lorsa, qlora clear and obvious.

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