Dontopedia

checkpoint_best.bin

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-06.)

checkpoint_best.bin has 7 facts recorded in Dontopedia across 3 references.

7 facts·6 predicates·3 sources

Mostly:iter mismatch with gen(1), resumed from(1), step(1)

Maturity scale raw canonical shape-checked rule-derived certified

Inbound mentions (6)

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.

areBestStateAre Best State(1)

causedSavingOfCaused Saving of(1)

reportsFileCreationReports File Creation(1)

savedBestCheckpointSaved Best Checkpoint(1)

savesBestCheckpointSaves Best Checkpoint(1)

usingCheckpointUsing Checkpoint(1)

Other facts (6)

The long tail: predicates that appear too rarely to warrant their own section. Filter or scroll to find a specific one. Each row links to its source.

6 facts
PredicateValueRef
Iter Mismatch With GenIter 941 Vs 940[1]
Resumed Fromcheckpoint_best.bin[1]
Step941[1]
Iteration941[1]
Best Loss5.4376[1]
Has Best Val Loss3.6677[2]

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.

iterMismatchWithGenblah/random/part-26
ex:iter-941-vs-940
resumedFromblah/random/part-26
checkpoint_best.bin
stepblah/random/part-26
941
iterationblah/random/part-26
941
bestLossblah/random/part-26
5.4376
hasBestValLossblah/random/part-28
3.6677
labelblah/safiersemantics/70
checkpoint_best.bin

References (3)

3 references
  1. [1]Part 265 facts
    ctx:discord/blah/random/part-26
  2. [2]Part 281 fact
    ctx:discord/blah/random/part-28
  3. [3]701 fact
    ctx:discord/blah/safiersemantics/70
    • full textsafiersemantics-70
      text/plain3 KBdoc:agent/safiersemantics-70/dbacde78-f635-4864-93c8-c2425e32c560
      Show excerpt
      [2026-02-19 20:25] xenonfun: model-ds being trained, asked it to optimize just on this training set what can be done without blowing out my 24GB limit and not exhausting the model from not enough data. (files: Screenshot_2026-02-19_at_3.23.

See also

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