Dequantize a Matrix

Reconstructs an approximate floating-point matrix from a quantized representation produced by mlx_quantize().

Usage

mlx_dequantize(
  w,
  scales,
  biases = NULL,
  group_size = 64L,
  bits = 4L,
  mode = "affine",
  device = mlx_default_device()
)

Arguments

w

An mlx array (the quantized weight matrix)

scales

An mlx array (the quantization scales)

biases

An optional mlx array (the quantization biases for affine mode). Default: NULL

group_size

The group size used during quantization. Default: 64

bits

The number of bits used during quantization. Default: 4

mode

The quantization mode used: "affine" or "mxfp4". Default: "affine"

device

Execution target: supply "gpu", "cpu", or an mlx_stream created via mlx_new_stream(). Defaults to the current mlx_default_device() unless noted otherwise (helpers that act on an existing array typically reuse that array's device or stream).

Value

An mlx array with the dequantized (approximate) floating-point weights

Details

Dequantization unpacks the low-precision quantized weights and applies the scales (and biases) to reconstruct approximate floating-point values. Note that some precision is lost during quantization and cannot be recovered.

See also

mlx_quantize(), mlx_quantized_matmul()

Examples

w <- mlx_rand_normal(c(64, 32))
quant <- mlx_quantize(w, group_size = 32)
w_reconstructed <- mlx_dequantize(quant$w_q, quant$scales, quant$biases, group_size = 32)