Semblance
333 posts


🚨ANTHROPIC CEO: OPEN SOURCE AI IS GETTING DANGEROUS
Anthropic CEO Dario Amodei told lawmakers that open-source AI is moving down a “very dangerous path.”
His warns that once powerful models are released openly, companies lose the ability to monitor misuse, revoke access, or update safety guardrails.
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@coinbureau maybe training on free data was thieving
i think he is in desperate need of money,
,you can only terrorize up-to an extent ,and after that it feels like any normal day
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@shrav_10 in his voice:-The first lets you experiment with quick results.
The second comes after you've curated enough successful experiments into training data. At that point, you fine-tune the model so it learns those patterns, rather than depending on retrieval every time.
GIF
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Interviewed another candidate today.
I asked him what RAG is.
He replied:
RAG is a technique that allows an LLM to answer questions using external knowledge instead of relying only on what it learned during training.
Then I asked about fine-tuning.
He said:
Fine-tuning is the process of training a pre-trained language model on your own dataset so that it learns a new behavior, style, or task.
Then I asked a follow-up:
Since RAG can inject external knowledge and even mimic a user's persona through retrieved context, why do we still need fine-tuning? When would you choose one over the other?
What would your answer be?
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@levidiamode @blelbach @GPU_MODE so here is my question ,wont this put you on a loop
for trying and fixing one solution after the next ,idk it feels like that to me ,or are you guys giving it a step by step instructions ..
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171/365 of GPU Programming
Finally got below 2ms on the GPU Mode QR challenge. Still quite far away from CUDA colonel @blelbach, so I'm excited to read everyones' writeups once the competition is over.
I especially wonder how #1 is utilizing NVFP4 (or is the submission name a false flag?). Every lower precision attempt has not panned out for me so far unfortunately.
Very curious where the top 3 will converge in a week from now...
If you're still debating whether to participate, would highly recommend! You learn so much from just trying stuff and being in the discord. And @modal gives you $30 in monthly compute for free. Maybe we can get a bit more compute for the next challenge if we all ask @charles_irl nicely 😁😁

levi@levidiamode
170/365 of GPU Programming Just going through the motions of QR factorization and Gram Schmidt today while concepts around orthogonality, norms and projections. It's been a while since I studied Linear Algebra, so it's been fun reviewing some older concepts and learning new ones as well. Will spend some time learning more about Cholesky and Householder as well tomorrow.
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@Taniyatweets_ for a mac,128gb no doubt ,in this day and age get apple care too
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@Zinny_Edmund it helps u gain speed .test how it improved your system
then u wont need to do ,redo it
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@aadityansha_06 think about the matrices as a whole ,do u really need that much of data transfers :-
p.s:-Linear algebra
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Looking for sponsors for my upcoming article about "How I optimized 1024*1024 matrix from 58 sec to less than 0.2 sec on my i3 2nd gen outdated processor"
AADITYANSHA@aadityansha_06
Optimizing 1024 * 1024 matrix multiplication in pure C by just using standard library and so far i have achieved 0.588 sec from 58 sec in naive on my i3 2nd gen processor with dual core and still about to do tiling and prefetching.
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@Rishikesh04code medium articles are dope ,
they simplify a lot of things
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@karanbuidls @rohan_jvm if u still want a read , will drop u on DM's
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@karanbuidls @rohan_jvm the incomplete draft? ,its like not even touching the basics ,so u can really understand what's going on
and secondly it doesn't describe data's chracteristic.
like:-is the matrix symmetric or not
2ndly-if its diagonal dependent or dependent on
the off diagonal one
3rd r99
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