Model Speedup Event 46k
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-07-01.)
Model Speedup Event 46k has 8 facts recorded in Dontopedia across 1 reference.
Mostly:has speedup level(1), at vocab size(1), assesses model(1)
Maturity scale
raw canonical shape-checked rule-derived certifiedHas Speedup LevelhasSpeedupLevel
- Not That Much Speed Up[1]sourceall time · 019a8f6a D43b 40ba Afc1 247f4b73c3a5
At Vocab SizeatVocabSize
- Vocab Size 46k[1]sourceall time · 019a8f6a D43b 40ba Afc1 247f4b73c3a5
Assesses ModelassessesModel
Rdfs:labelrdfs:label
- not that much speed up 46K now peak[1]sourceall time · 019a8f6a D43b 40ba Afc1 247f4b73c3a5
Likely Constrained bylikelyConstrainedBy
- Memory Bound State[1]all time · 019a8f6a D43b 40ba Afc1 247f4b73c3a5
Constrained by StateconstrainedByState
- Memory Bound State[1]all time · 019a8f6a D43b 40ba Afc1 247f4b73c3a5
At Performance StateatPerformanceState
- Peak State[1]all time · 019a8f6a D43b 40ba Afc1 247f4b73c3a5
Rdf:typerdf:type
- Speedup Assessment Event[1]all time · 019a8f6a D43b 40ba Afc1 247f4b73c3a5
Inbound mentions (1)
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.
describesAssessmentOfDescribes Assessment of(1)
- Not That Much Speed Up
ex:not-that-much-speed-up
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.
References (1)
- [1]xenonfun: well not that much speed up 46K now peak, think its memory bound already. 8K voc8 factscustom
ctx:claims/document/019a8f6a-d43b-40ba-afc1-247f4b73c3a5- full textxenonfun: well not that much speed up 46K now peak, think its memory bound already. 8K voctext/plain680 B
discord:msg/a3126764-fdd1-42f8-9653-a5170ea5bdefShow 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…
See also
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