What are efficient methods for post-training quantization to compress generative model sizes

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Name the effective methods for post-training quantization to compress generative model size.
Nov 22 in Generative AI by Ashutosh
• 6,050 points
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Efficient methods for post-training quantization in generative models reduce model size are as follows:

  • Dynamic Quantization:

    • Weights are quantized to lower precision during inference.
    • Minimal accuracy loss, fast implementation
  • Static Quantization:

    • Requires calibration with a dataset to map activations into quantized ranges.
    • Produces better results than dynamic quantization for fixed workloads.
  • Quantization-Aware Training (QAT):

    • Simulates quantization during training to minimize accuracy loss.
    • Best for high accuracy on low-bit models but computationally expensive.
  • Weight Sharing:

    • Groups weigh into clusters and store shared indices, reducing memory usage.

Hence, by referring to the above methods, you can post-training quantization to compress generative model sizes.

answered Nov 22 by Ashutosh
• 6,050 points

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