Dontopedia

dtype

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-10.)

dtype has 11 facts recorded in Dontopedia across 4 references, with 3 live disagreements.

11 facts·5 predicates·4 sources·3 in dispute

Mostly:rdf:type(3), value(2), has value(1)

Maturity scale raw canonical shape-checked rule-derived certified

Inbound mentions (4)

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.

containsContains(1)

hasParameterHas Parameter(1)

setsParameterSets Parameter(1)

takesArgumentTakes Argument(1)

Other facts (8)

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.

8 facts
PredicateValueRef
Rdf:typeParameter[2]
Rdf:typeParameter[3]
Rdf:typeParameter[4]
Valuenp.float32[2]
ValueData Type Int64[3]
Has ValueTorch Qint8[1]
Used inLil Matrix Initialization[2]
Parameter ValueTorch Qint8[4]

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.

hasValuebeam/5a883f10-cd51-4320-9b90-c929f1dad36d
ex:torch-qint8
typebeam/306c29bb-24f7-454f-9101-afe06f337d8e
ex:Parameter
labelbeam/306c29bb-24f7-454f-9101-afe06f337d8e
dtype Parameter
usedInbeam/306c29bb-24f7-454f-9101-afe06f337d8e
ex:lil_matrix-initialization
valuebeam/306c29bb-24f7-454f-9101-afe06f337d8e
np.float32
typebeam/58335043-7a28-4310-8bc8-6b38b5011f99
ex:Parameter
labelbeam/58335043-7a28-4310-8bc8-6b38b5011f99
dtype
valuebeam/58335043-7a28-4310-8bc8-6b38b5011f99
ex:data-type-int64
typebeam/893846b7-2485-431d-970b-b70aaf9c7c59
ex:Parameter
labelbeam/893846b7-2485-431d-970b-b70aaf9c7c59
dtype
parameterValuebeam/893846b7-2485-431d-970b-b70aaf9c7c59
ex:torch-qint8

References (4)

4 references
  1. ctx:claims/beam/5a883f10-cd51-4320-9b90-c929f1dad36d
    • full textbeam-chunk
      text/plain1 KBdoc:beam/5a883f10-cd51-4320-9b90-c929f1dad36d
      Show excerpt
      quantized_net = torch.quantization.quantize_dynamic(net, {nn.Linear}, dtype=torch.qint8) # Example usage: output = quantized_net(input_tensor) print(output) ``` Can you help me evaluate the trade-offs between different optimization techniq
  2. ctx:claims/beam/306c29bb-24f7-454f-9101-afe06f337d8e
  3. ctx:claims/beam/58335043-7a28-4310-8bc8-6b38b5011f99
    • full textbeam-chunk
      text/plain1 KBdoc:beam/58335043-7a28-4310-8bc8-6b38b5011f99
      Show excerpt
      Here's how you can set up and use Milvus to store and retrieve document embeddings: ### Step-by-Step Guide 1. **Install Milvus**: - Install Milvus using Docker or from source. - Ensure you have a running Milvus instance. 2. **Desig
  4. ctx:claims/beam/893846b7-2485-431d-970b-b70aaf9c7c59

See also

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