krzysztof/landing_page
1
0
Fork 0
mirror of https://github.com/qdrant/landing_page.git synced 2026-09-25 22:18:30 +02:00
Code Issues Packages Projects Releases Wiki Activity

properly assign language to snippets

Browse Source
This commit is contained in:
generall
2023-07-13 01:45:36 +02:00
parent f460c8a7c0
commit 80c97b6ac2
2 changed files with 2 additions and 2 deletions
Download Patch File Download Diff File Expand all files Collapse all files
qdrant-landing/content/articles/quantum-quantization.md
+1 -1
View File
@@ -23,7 +23,7 @@ Quantum quantization is a novel approach that leverages the power of quantum com
The conversion of float32 vectors to qbit vectors can be represented by the following formula: The conversion of float32 vectors to qbit vectors can be represented by the following formula:
``` ```text
qbit_vector = Q( float32_vector ) qbit_vector = Q( float32_vector )
``` ```
qdrant-landing/content/articles/scalar-quantization.md
+1 -1
View File
@@ -29,7 +29,7 @@ So a single number needs 4 bytes of the memory and a 512-dimensional vector occu
2 kB. That's only the memory used to store the vector. There is also an overhead of the 2 kB. That's only the memory used to store the vector. There is also an overhead of the
HNSW graph, so as a rule of thumb we estimate the memory size with the following formula: HNSW graph, so as a rule of thumb we estimate the memory size with the following formula:
``` ```text
memory_size = 1.5 * number_of_vectors * vector_dimension * 4 bytes memory_size = 1.5 * number_of_vectors * vector_dimension * 4 bytes
``` ```