Optimizing Models for Low-Configuration Devices

Learn how to optimize AI models using pruning, quantization, and distillation techniques to run efficiently on low-configuration devices.

Welcome to the "Practical Application of AI Large Language Model Systems" Series

Table of Contents

In the previous session, "Building a 100M Parameter Transformer Model from Scratch," I trained a model with 5MB of data that took up about 500MB of storage and had approximately 120 million parameters. To save time, I ran a simple test, resulting in significant parameter wastage. For instance:

\(f(x)=k_{1}x_{1} + k_{2}x_{2} + k_{3}x_{3} + \ldots + k_{1000}x_{1000}\)

After training with limited data, we only determine k1, k2, ... k100. The rest are unused. This formula can be optimized by removing all parameters after k100 or keeping only those before k300.

In models, parameter count relates to both data and network design. Deep models can waste parameters, so optimizing models to reduce complexity and resource usage is crucial. This allows running models on lower-spec devices.

Now, let’s learn some model optimization techniques.