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https://w3id.org/np/RAmEQd6Xc3YHZ6uH6_kOfPKx1SDVV-wn6aK8LrvcmNyZE

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Content

@prefix this: <https://w3id.org/np/RAmEQd6Xc3YHZ6uH6_kOfPKx1SDVV-wn6aK8LrvcmNyZE> .
@prefix sub: <https://w3id.org/np/RAmEQd6Xc3YHZ6uH6_kOfPKx1SDVV-wn6aK8LrvcmNyZE#> .
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@prefix np: <http://www.nanopub.org/nschema#> .
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@prefix dcterms: <http://purl.org/dc/terms/> .
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@prefix schema: <https://schema.org/> .
@prefix foaf: <http://xmlns.com/foaf/0.1/> .
@prefix orcid: <https://orcid.org/> .

sub:head {
  this: a np:Nanopublication;
    np:hasAssertion sub:assertion;
    np:hasProvenance sub:provenance;
    np:hasPublicationInfo sub:pubinfo .
}

sub:assertion {
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    dcterms:creator <https://w3id.org/np/RAoSadUw99CeqDlR2400018nqTzR_38fT86OrTzk16Vts>;
    rdfs:comment """ Scaling laws don't care about scale of the \"train\" models?
Did anyone else get this? 
When I predict a scaling law, the scale of the largest model matters, but the num-models for fitting matters much much much more. 
Initial results, scaling error by #models starting from largest https://twitter.com/LChoshen/status/1803401845626511568/photo/1

 Maybe more simply put:
You can predict a scaling law with 8 small models, and it would be better than 3 large ones (that costs a lot)

Is that something anyone else seen?

""";
    schema:keywords "AI", "cost", "initialresults", "models", "modelscale", "scalinglaws",
      "training" .
}

sub:provenance {
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  sub:assertion prov:linksTo <https://x.com/LChoshen/status/1803401845626511568>;
    prov:wasAssociatedWith <https://x.com/LChoshen>;
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      <https://x.com/LChoshen> .
}

sub:pubinfo {
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    dcterms:license <https://creativecommons.org/licenses/by/4.0/>;
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    foaf:name "Leshem Choshen ���� @ICML wanna talk?" .
}