In short
Reducing the numerical precision of model weights (e.g. FP16 → INT8/INT4) to shrink memory footprint and speed up inference, with a small accuracy trade-off.
How to learn this
- 1Quantize a model with a library like `bitsandbytes` and measure the memory savings.
- 2Compare output quality before and after quantization on the same prompts.
- 3Learn the difference between post-training quantization and quantization-aware training.
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