Mistral AI Dequantization

Dequantization

mistral-cookbookembeddingsmistral

alph-notebooks/mistral-cookbook / dequantization.ipynb

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Dequantization embeddings

This notebook demonstrates how to dequantize various quantized representations (int8, uint8, binary, ubinary) of embeddings back to float values using PyTorch and NumPy.

Installation

Install the required packages:

[1]

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Installing collected packages: nvidia-nvjitlink-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12
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  Attempting uninstall: nvidia-cusparse-cu12
    Found existing installation: nvidia-cusparse-cu12 12.5.1.3
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  Attempting uninstall: nvidia-cudnn-cu12
    Found existing installation: nvidia-cudnn-cu12 9.3.0.75
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  Attempting uninstall: nvidia-cusolver-cu12
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Successfully installed nvidia-cublas-cu12-12.4.5.8 nvidia-cuda-cupti-cu12-12.4.127 nvidia-cuda-nvrtc-cu12-12.4.127 nvidia-cuda-runtime-cu12-12.4.127 nvidia-cudnn-cu12-9.1.0.70 nvidia-cufft-cu12-11.2.1.3 nvidia-curand-cu12-10.3.5.147 nvidia-cusolver-cu12-11.6.1.9 nvidia-cusparse-cu12-12.3.1.170 nvidia-nvjitlink-cu12-12.4.127

define the dequantization functions

[2]

Examples

[3]

[4]

tensor([255., 255.,   1.,   1., 255., 255.,   1.,   1., 255.,   1.,   1., 255.,
,        255., 255.,   1.,   1.,   1., 255.])

[5]

tensor([255., 255.,   1.,   1., 255., 255.,   1.,   1., 255.,   1.,   1., 255.,
,        255., 255.,   1.,   1.,   1., 255.])

[6]

tensor([-0.1181, -0.2756,  0.0394,  0.3071, -0.1496, -0.1181,  0.0787,  0.0866,
,        -0.0394,  0.2205,  0.0000,  0.0000, -0.0157, -0.0551,  0.0472,  0.0157,
,         0.0315, -0.0236])

[7]

tensor([-0.1216, -0.2706,  0.0431,  0.3020, -0.1451, -0.1216,  0.0824,  0.0824,
,        -0.0431,  0.2235,  0.0039, -0.0039, -0.0118, -0.0510,  0.0510,  0.0196,
,         0.0275, -0.0275])

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