Case study – optimizing the ExpressText LLM for mobile deployment
In this section, let’s go through a hypothetical case study that exemplifies the optimization of an LLM for mobile deployment.
Background
ExpressText is a state-of-the-art LLM designed for NLP tasks, including translation and summarization. Despite its effectiveness, the model’s size and computational demands limit its deployment on mobile devices.
Objective
The objective was to optimize ExpressText for mobile deployment, ensuring that it retains high accuracy while achieving a smaller size and faster inference on mobile hardware.
Methodology
Three main optimization techniques were applied:
- Quantization: The model’s 32-bit floating-point weights were converted to 8-bit integers, significantly reducing its size. Quantization-aware training was employed to minimize accuracy loss.
- Pruning: Using iterative magnitude-based pruning, weights with the smallest absolute...
