M

as donto believed on

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-08.)

M is Number of sub-quantizers.

35 facts·18 predicates·10 sources·4 in dispute

Mostly:rdf:type(9), affects(6), has value(2)

Inbound mentions (9)

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.

`hasParameter`Has Parameter(6)

Hnsw Indexex:hnsw-index
Index Ivfpqex:IndexIVFPQ
Index Ivfpqex:IndexIVFPQ
Index Ivfpqex:IndexIVFPQ
Ivfpq Indexex:ivfpq-index
Lambda Functionex:lambda-function

`consists-of`Consists of(1)

Ivf Pq Componentsex:ivf-pq-components

`ex:requires`Ex:requires(1)

Index Ivfpqex:IndexIVFPQ

`requiresTuning`Requires Tuning(1)

Index Parametersindex-parameters

Other facts (32)

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.

32 facts

Predicate	Value	Ref
Rdf:type	Parameter	[1]
Rdf:type	Parameter	[2]
Rdf:type	Subquantizer Count	[5]
Rdf:type	Index Parameter	[6]
Rdf:type	Parameter	[7]
Rdf:type	Index Parameter	[8]
Rdf:type	Sub Quantizer Count	[8]
Rdf:type	Index Parameter	[9]
Rdf:type	Quantization Parameter	[10]
Affects	Memory Footprint	[1]
Affects	Search Speed	[1]
Affects	Accuracy	[1]
Affects	Balance Speed Accuracy	[7]
Affects	Trade Off	[9]
Affects	Speed	[9]
Has Value	32	[3]
Has Value	8	[10]
Determines	Number of Links Per Node	[1]
Has Effect When Lowered	Reduce Memory Footprint	[1]
Has Potential Benefit	Potentially Speed Up Search	[1]
Has Potential Downside	May Reduce Accuracy	[1]
Is Parameter in Hnsw	Hnsw	[1]
Determines Specifically	Number of Links Per Node in Graph	[1]
Describes	Subquantizers	[2]
Ex:description	Number of subquantizers	[4]
Ex:value	8	[4]
Ex:typical Range	Variable M	[4]
Is Parameter of	Hnsw Index	[6]
Description	Number of sub-quantizers	[7]
Controls Sub Quantizer Count	true	[7]
Controls	Sub Quantizer Count	[8]
Is Number of	Subquantizers	[9]

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.

35 undated

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typebeam/59e50d81-63da-4940-a9ce-98f7f0ea5c33

ex:parameter

—

labelbeam/59e50d81-63da-4940-a9ce-98f7f0ea5c33

—

labelbeam/59e50d81-63da-4940-a9ce-98f7f0ea5c33

Number of Links

—

determinesbeam/59e50d81-63da-4940-a9ce-98f7f0ea5c33

ex:number-of-links-per-node

—

affectsbeam/59e50d81-63da-4940-a9ce-98f7f0ea5c33

ex:memory-footprint

—

affectsbeam/59e50d81-63da-4940-a9ce-98f7f0ea5c33

ex:search-speed

—

affectsbeam/59e50d81-63da-4940-a9ce-98f7f0ea5c33

ex:accuracy

—

hasEffectWhenLoweredbeam/59e50d81-63da-4940-a9ce-98f7f0ea5c33

ex:reduce-memory-footprint

—

hasPotentialBenefitbeam/59e50d81-63da-4940-a9ce-98f7f0ea5c33

ex:potentially-speed-up-search

—

hasPotentialDownsidebeam/59e50d81-63da-4940-a9ce-98f7f0ea5c33

ex:may-reduce-accuracy

—

isParameterInHNSWbeam/59e50d81-63da-4940-a9ce-98f7f0ea5c33

ex:hnsw

—

determinesSpecificallybeam/59e50d81-63da-4940-a9ce-98f7f0ea5c33

ex:number-of-links-per-node-in-graph

—

typebeam/8e356af0-5214-4a1f-8615-f270ae5ec1c9

ex:Parameter

—

describesbeam/8e356af0-5214-4a1f-8615-f270ae5ec1c9

ex:subquantizers

—

hasValueblah/watt-activation/314

—

descriptionbeam/9f354551-a9f5-474b-a587-082e952c4a41

Number of subquantizers

—

valuebeam/9f354551-a9f5-474b-a587-082e952c4a41

—

typicalRangebeam/9f354551-a9f5-474b-a587-082e952c4a41

ex:variable-m

—

typebeam/276709e4-43dc-4dfa-a983-c23bf40e789f

ex:subquantizer-count

—

typebeam/b42513be-0688-405f-930a-67b6a556e65e

ex:IndexParameter

—

isParameterOfbeam/b42513be-0688-405f-930a-67b6a556e65e

ex:hnsw-index

—

typebeam/9aef4a43-c110-4730-bed6-18e6312b77ad

ex:Parameter

—

labelbeam/9aef4a43-c110-4730-bed6-18e6312b77ad

—

descriptionbeam/9aef4a43-c110-4730-bed6-18e6312b77ad

Number of sub-quantizers

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affectsbeam/9aef4a43-c110-4730-bed6-18e6312b77ad

ex:balance-speed-accuracy

—

controls-sub-quantizer-countbeam/9aef4a43-c110-4730-bed6-18e6312b77ad

true

—

typebeam/bd97afa1-16ea-42af-99e4-d1e90ad821ac

ex:IndexParameter

—

typebeam/bd97afa1-16ea-42af-99e4-d1e90ad821ac

ex:SubQuantizerCount

—

controlsbeam/bd97afa1-16ea-42af-99e4-d1e90ad821ac

ex:sub-quantizer-count

—

affectsbeam/8928fff6-028a-4c31-9801-9484b10c9c03

ex:trade-off

—

isNumberOfbeam/8928fff6-028a-4c31-9801-9484b10c9c03

ex:subquantizers

—

affectsbeam/8928fff6-028a-4c31-9801-9484b10c9c03

ex:speed

—

typebeam/8928fff6-028a-4c31-9801-9484b10c9c03

ex:IndexParameter

—

hasValuebeam/9170f193-72c4-43d3-9c09-87f869d91b8b

—

typebeam/9170f193-72c4-43d3-9c09-87f869d91b8b

ex:Quantization-parameter

References (10)

10 references

[1]59e50d81 63da 4940 A9ce 98f7f0ea5c3312 facts
ctx:claims/beam/59e50d81-63da-4940-a9ce-98f7f0ea5c33
- full textbeam-chunk
  text/plain1 KBdoc:beam/59e50d81-63da-4940-a9ce-98f7f0ea5c33
  Show excerpt
  For real-time search applications, **HNSW** is typically more suitable due to its faster search speed and ability to handle dynamic updates efficiently. However, if memory efficiency and scalability are critical, **IVFPQ** can be a better c…
[2]8e356af0 5214 4a1f 8615 F270ae5ec1c92 facts
ctx:claims/beam/8e356af0-5214-4a1f-8615-f270ae5ec1c9
- full textbeam-chunk
  text/plain1 KBdoc:beam/8e356af0-5214-4a1f-8615-f270ae5ec1c9
  Show excerpt
  - `efConstruction` and `efSearch` parameters control the construction and search phases, respectively. 2. **IVFPQ Index**: - `IndexIVFPQ`: Creates an IVFPQ index with a specified number of clusters (`nlist`), subquantizers (`m`), and…
[3]3141 fact
ctx:discord/blah/watt-activation/314
- full textwatt-activation-314
  text/plain2 KBdoc:agent/watt-activation-314/ded594ee-5f01-46ec-83f4-35b5ddd1c3f6
  Show excerpt
  [2026-03-15 01:41] xenonfun: ``` ⏺ Topology Speed Comparison (compiled, exact training config) ┌──────────────┬───────┬─────────┐ │ Topology │ Tok/s │ ms/step │ ├──────────────┼───────┼─────────┤ │ complete+KAN │ 7,221 │ 567 …
[4]9f354551 A9f5 474b A587 082e952c4a413 facts
ctx:claims/beam/9f354551-a9f5-474b-a587-082e952c4a41
- full textbeam-chunk
  text/plain1 KBdoc:beam/9f354551-a9f5-474b-a587-082e952c4a41
  Show excerpt
  faiss.omp_set_num_threads(4) # Adjust based on your system's capabilities # Create an IVFFlat index quantizer = faiss.IndexFlatL2(128) index = faiss.IndexIVFFlat(quantizer, 128, nlist, faiss.METRIC_L2) # Train the index index.train(vecto…
[5]276709e4 43dc 4dfa A983 C23bf40e789f1 fact
ctx:claims/beam/276709e4-43dc-4dfa-a983-c23bf40e789f
- full textbeam-chunk
  text/plain1 KBdoc:beam/276709e4-43dc-4dfa-a983-c23bf40e789f
  Show excerpt
  - Try different values for `nlist` and `nprobe` to find the optimal balance between speed and accuracy. - For example, you might try `nlist = 200` and `nprobe = 5` or `nprobe = 20`. 2. **Monitor Performance**: - Use `time` or `cPr…
[6]B42513be 0688 405f 930a 67b6a556e65e2 facts
ctx:claims/beam/b42513be-0688-405f-930a-67b6a556e65e
- full textbeam-chunk
  text/plain1 KBdoc:beam/b42513be-0688-405f-930a-67b6a556e65e
  Show excerpt
  - **Index Type**: Choose an appropriate index type based on your use case. For example, `IVF_FLAT` or `HNSW` are commonly used for high-dimensional vector data. - **Index Parameters**: Tune the index parameters such as `nlist` for `IV…
[7]9aef4a43 C110 4730 Bed6 18e6312b77ad5 facts
ctx:claims/beam/9aef4a43-c110-4730-bed6-18e6312b77ad
[8]Bd97afa1 16ea 42af 99e4 D1e90ad821ac3 facts
ctx:claims/beam/bd97afa1-16ea-42af-99e4-d1e90ad821ac
- full textbeam-chunk
  text/plain1 KBdoc:beam/bd97afa1-16ea-42af-99e4-d1e90ad821ac
  Show excerpt
  - **Use Approximate Methods**: Use `IndexIVFPQ` or `IndexHNSW` to find a balance between speed and accuracy. ### Example Implementation Here's an optimized version of your code that addresses these potential roadblocks: ```python import …
[9]8928fff6 028a 4c31 9801 9484b10c9c034 facts
ctx:claims/beam/8928fff6-028a-4c31-9801-9484b10c9c03
- full textbeam-chunk
  text/plain1 KBdoc:beam/8928fff6-028a-4c31-9801-9484b10c9c03
  Show excerpt
  To further optimize the query time, you can adjust the parameters: - **`nlist`**: Increasing `nlist` can improve accuracy but may increase memory usage and query time. - **`m`**: The number of subquantizers affects the trade-off between sp…
[10]9170f193 72c4 43d3 9c09 87f869d91b8b2 facts
ctx:claims/beam/9170f193-72c4-43d3-9c09-87f869d91b8b
- full textbeam-chunk
  text/plain1 KBdoc:beam/9170f193-72c4-43d3-9c09-87f869d91b8b
  Show excerpt
  index.nprobe = nprobe return index # Example usage: vectors = np.random.rand(10000, 128).astype(np.float32) index = create_ivfpq_index(vectors, nlist=200, m=8, nprobe=15) print(index.ntotal) # Test the index query_vectors = np.ran…

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M

Inbound mentions (9)

hasParameterHas Parameter(6)

consists-ofConsists of(1)

ex:requiresEx:requires(1)

requiresTuningRequires Tuning(1)

Other facts (32)

Timeline

References (10)

See also

Keep researching

`hasParameter`Has Parameter(6)

`consists-of`Consists of(1)

`ex:requires`Ex:requires(1)

`requiresTuning`Requires Tuning(1)