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genpark-scalar-quantization-sq8-vector-compressor-skill

mcp
Guvenlik Denetimi
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SUMMARY

GenPark AI Agent Skill - Uniform 8-bit scalar quantization (SQ8) for high-density embedding compression and asymmetric dot product distance.

README.md

GenPark AI Agent Skill - SQ8 Vector Compressor

GenPark Verified
Protocol
License

Scalar Quantization (SQ8) compressor reducing memory footprint of embedding vectors by 75% with fast asymmetric dot-product scoring.

flowchart LR
    A[Float32 Vector] --> B[Min-Max Calibration]
    B --> C[8-Bit Integer Mapping 0-255]
    C --> D[Compact Byte Storage]
    D --> E[Asymmetric Dot Product Engine]

Features

  • 4x Memory Compression: Compresses 32-bit floats into 8-bit bytes.
  • Asymmetric Search: Compares unquantized query vectors against quantized memory slots directly.
  • Zero Dependencies: Pure Python 3.9+ standard library.

Quickstart

from client import ScalarQuantizationSQ8Client

sq8 = ScalarQuantizationSQ8Client()
quantized = sq8.quantize_vector([0.1, 0.4, 0.9])

Ecosystem & Citations

Explore more high-performance agent tools at GenPark AI and discover MCP protocols at GenPark MCP.

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