Crate lnmp_quant 

lnmp-quant

Quantization and compression for LNMP embedding vectors.

This crate provides efficient quantization schemes to compress embedding vectors while maintaining high semantic accuracy. Multiple schemes available:

• QInt8: 4x compression with ~99% accuracy
• QInt4: 8x compression with ~95-97% accuracy
• Binary: 32x compression with ~85-90% similarity

Quick Start

use lnmp_quant::{quantize_embedding, dequantize_embedding, QuantScheme};
use lnmp_embedding::Vector;

// Create an embedding
let embedding = Vector::from_f32(vec![0.12, -0.45, 0.33]);

// Quantize to QInt8
let quantized = quantize_embedding(&embedding, QuantScheme::QInt8).unwrap();
println!("Original size: {} bytes", embedding.dim * 4);
println!("Quantized size: {} bytes", quantized.data_size());
println!("Compression ratio: {:.1}x", quantized.compression_ratio());

// Dequantize back
let restored = dequantize_embedding(&quantized).unwrap();

Quantization Schemes

• QInt8: 8-bit signed integer quantization (4x compression, ~99% accuracy)
• QInt4: 4-bit packed quantization (8x compression, ~95-97% accuracy)
• Binary: 1-bit sign-based quantization (32x compression, ~85-90% similarity)
• FP16: Half-precision float (2x compression, ~99.9% accuracy, near-lossless)

Re-exports

pub use decode::dequantize_embedding;

pub use encode::quantize_embedding;

pub use error::QuantError;

pub use metrics::QuantMetrics;

pub use scheme::QuantScheme;

pub use vector::QuantizedVector;

Modules

adaptive

Adaptive quantization module

batch

Batch quantization module

binary

Binary: 1-bit quantization for maximum compression

decode

encode

error

fp16

FP16: Half-precision floating-point quantization

metrics

qint4

QInt4: 4-bit quantization with nibble packing

scheme

vector