What is the difference between model pruning and quantization?
An example of an AI chatbot conversation about model pruning and quantization in AI.
What is the difference between model pruning and quantization?
Model pruning reduces the number of parameters in a model, while quantization reduces the precision of those parameters.
Can you explain how model pruning works?
Sure! Model pruning involves removing weights that contribute least to the model's performance, making it smaller and faster.
And what about quantization? How does that help?
Quantization converts weights from floating-point to lower precision formats, which reduces memory usage and speeds up inference.
Are there any trade-offs with these techniques?
Yes, both can lead to a loss in accuracy, so it's important to balance efficiency and performance.
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