Dontopedia

d_model

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

d_model has 14 facts recorded in Dontopedia across 8 references, with 1 live disagreement.

14 facts·8 predicates·8 sources·1 in dispute

Mostly:rdf:type(4), must be greater or equal to(1), provides headroom for context mixing(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.

staysAtDimensionStays at Dimension(2)

evaluatesEfficiencyEvaluates Efficiency(1)

hasNoDModelHas No D Model(1)

lacksDmodelLacks Dmodel(1)

toDimTo Dim(1)

Other facts (11)

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.

11 facts
PredicateValueRef
Rdf:typeModel Dimension[5]
Rdf:typeHyperparameter[6]
Rdf:typeHyperparameter[7]
Rdf:typeHyperparameter[8]
Must Be Greater or Equal toG×H × 4-8[1]
Provides Headroom for Context Mixingtrue[1]
Example Value832[2]
Is Projection TargetCurrent Pipeline[3]
Is Least Efficient to ScaleScaling Lever[4]
Scaling Helpstrue[7]
Scaling Effectfill the GPU[7]

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.

mustBeGreaterOrEqualToblah/watt-activation/part-323
G×H × 4-8
providesHeadroomForContextMixingblah/watt-activation/part-323
true
exampleValueblah/watt-activation/part-322
832
isProjectionTargetblah/watt-activation/part-321
ex:current-pipeline
isLeastEfficientToScaleblah/watt-activation/part-355
ex:scaling-lever
typeblah/watt-activation/319
ex:ModelDimension
labelblah/watt-activation/319
d_model
typeblah/watt-activation/353
ex:Hyperparameter
labelblah/watt-activation/353
d_model
labelblah/watt-activation/355
d_model
typeblah/watt-activation/355
ex:Hyperparameter
scalingHelpsblah/watt-activation/355
true
scalingEffectblah/watt-activation/355
fill the GPU
typeblah/watt-activation/392
ex:Hyperparameter

References (8)

8 references
  1. [1]Part 3232 facts
    ctx:discord/blah/watt-activation/part-323
  2. [2]Part 3221 fact
    ctx:discord/blah/watt-activation/part-322
  3. [3]Part 3211 fact
    ctx:discord/blah/watt-activation/part-321
  4. [4]Part 3551 fact
    ctx:discord/blah/watt-activation/part-355
  5. [5]3192 facts
    ctx:discord/blah/watt-activation/319
    • full textwatt-activation-319
      text/plain2 KBdoc:agent/watt-activation-319/f54ddf34-a21b-47fb-8296-277054f2ccaa
      Show excerpt
      [2026-03-15 02:58] lisamegawatts: You're right — the whole point of QPSK isn't to be sparse, it's to be bandwidth-efficient. In telecom, QPSK packs 2 bits into one symbol period because the receiver only needs to distinguish 4 phase states,
  6. [6]3532 facts
    ctx:discord/blah/watt-activation/353
    • full textwatt-activation-353
      text/plain3 KBdoc:agent/watt-activation-353/cc7a24c1-66ae-472e-a74c-30bb70fe2a69
      Show excerpt
      [2026-03-17 09:19] xenonfun: ``` ============================================================ K4_cur10 K=4 curriculum=10% ============================================================ step 1000/5000 BPB=3.173 719,581 tok/s step 2
  7. [7]3554 facts
    ctx:discord/blah/watt-activation/355
    • full textwatt-activation-355
      text/plain3 KBdoc:agent/watt-activation-355/e62c81a8-1082-4c07-b675-3759a8600d0e
      Show excerpt
      [2026-03-17 15:27] xenonfun: ``` Key findings: 1. Depth scaling is smooth and strong: BPB drops monotonically 3.00→2.53 from D=6→D=32. DC@16 rises 72%→91%. 2. Retrieval reach = 128 for ALL configs — every model retrieves across the f
  8. [8]3921 fact
    ctx:discord/blah/watt-activation/392
    • full textwatt-activation-392
      text/plain1 KBdoc:agent/watt-activation-392/ddbc1ecc-c3dc-4acc-b74a-05cca6e39186
      Show excerpt
      [2026-03-19 03:47] xenonfun: ``` ❯ do we not have concept of SNR and bandwidth in these or they pure rotation and geometry? ⏺ They're pure rotation and geometry right now — no SNR or bandwidth concepts. The ResonantWireLM has no attention,

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