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Vocab Size 100k

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-07-01.)

Vocab Size 100k has 5 facts recorded in Dontopedia across 1 reference.

5 facts·5 predicates·1 sources

Mostly:greater than(1), numeric value(1), referenced in observation(1)

Maturity scale raw canonical shape-checked rule-derived certified

Greater ThangreaterThan

Numeric ValuenumericValue

  • 100000[1]sourceall time · 019a8f6a D43b 40ba Afc1 247f4b73c3a5

Referenced in ObservationreferencedInObservation

Rdfs:labelrdfs:label

  • 100K[1]sourceall time · 019a8f6a D43b 40ba Afc1 247f4b73c3a5

Rdf:typerdf:type

Inbound mentions (7)

Other subjects in dontopedia point AT this entity as a value. These are inverse relationships — e.g. "X motherOf this subject" — and answer questions the forward facts can't. Grouped by predicate.

lessThanLess Than(2)

atVocabSizeAt Vocab Size(1)

mentionsMentions(1)

mentionsVocabSizeMentions Vocab Size(1)

observedAtVocabSizeObserved at Vocab Size(1)

usedVocabSizeUsed Vocab Size(1)

Timeline

Timeline axis is valid_time — when each source says the fact was true in the world, not when Dontopedia learned about it. Retracted rows are kept for provenance; coloured stripes indicate the context kind.

5 undated
—
greaterThandocument/019a8f6a-d43b-40ba-afc1-247f4b73c3a5
ex:vocab-size-46k
—
numericValuedocument/019a8f6a-d43b-40ba-afc1-247f4b73c3a5
100000
—
labeldocument/019a8f6a-d43b-40ba-afc1-247f4b73c3a5
100K
—
typedocument/019a8f6a-d43b-40ba-afc1-247f4b73c3a5
ex:vocabulary-size
—
referencedInObservationdocument/019a8f6a-d43b-40ba-afc1-247f4b73c3a5
ex:model-attention-observation-100k

References (1)

1 references
  1. [1]xenonfun: well not that much speed up 46K now peak, think its memory bound already. 8K voc5 facts
    customctx:claims/document/019a8f6a-d43b-40ba-afc1-247f4b73c3a5
    • full textxenonfun: well not that much speed up 46K now peak, think its memory bound already. 8K voc
      text/plain680 Bdiscord:msg/a3126764-fdd1-42f8-9653-a5170ea5bdef
      Show excerpt
      xenonfun: well not that much speed up 46K now peak, think its memory bound already. 8K vocab is signifigantly worse output at same training but makes sense, model was too lopsided with that much embeddings, but at 100K it did observe intere…

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