What are the limits of quantization in PyTorch?

What are the limits of quantization in PyTorch?

Quantization in PyTorch can reduce model size and improve inference speed, but it may lead to accuracy loss.

How does quantization affect model accuracy?

Quantization can introduce errors due to reduced precision, which may affect the model's performance, especially in sensitive tasks.

Are there specific models that are more affected by quantization?

Yes, models that rely heavily on fine-grained details, like those in image classification, may suffer more from quantization.

What techniques can mitigate accuracy loss during quantization?

Techniques like quantization-aware training and post-training quantization can help maintain accuracy while quantizing.