Based on the following scenario, identify the most appropriate model compression technique and justify your choice.

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- Model Pruning: Model pruning refers to removing part of neural network (e.g., weights, neurons, layers, channels, attention heads), thereby achieving the effects of reducing the model size and speeding up inference time.

- Quantization: It refers to the compression of higher precision parameters to lower precision.

- Knowledge Distillation: This is a compression technique in which a small model called student model is trained to reproduce the behaviors of a large model called teacher model.

- Module Replacing: It reduces the model size by replacing the large modules of original PTMs with more compact substitutes.

- Early Exit: It allows the model to exit early at an off-ramp instead of passing through the entire model. The number of layers to be executed is conditioned on the input.

- Parameter Sharing: Reduces model size by using the same set of parameters across multiple layers, which also saves memory during inference.

Ways to compress PTMs

A research team is tasked with deploying a large language model on edge devices with limited memory and processing power. Their primary goal is to reduce the model's memory footprint. They achieve this by converting the model's 32-bit floating-point weights and activations into 8-bit integers. While this significantly reduces the model's size, they observe a minor drop in performance. Which model compression technique does this scenario describe?

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