Introduction to Model Compression

Large Language Models (LLMs) are powerful but their significant size and computational demands often pose practical challenges. Deploying a model with billions of parameters requires substantial memory, processing power, and energy, limiting their use in resource-constrained environments like mobile devices or edge hardware, and increasing operational costs in cloud deployments.

Model compression addresses these challenges directly. It encompasses a range of techniques designed to reduce the storage footprint and computational cost of machine learning models, including LLMs, while aiming to minimize the impact on their predictive performance (like accuracy or perplexity). Think of it as making models leaner and more efficient.

The primary goals of model compression are:

1. Reduced Memory Usage: Smaller models require less RAM and storage, making them feasible for devices with limited memory.
2. Faster Inference: Compressed models often require fewer computations or can leverage specialized hardware operations, leading to quicker predictions.
3. Lower Energy Consumption: Reduced computations and memory access translate directly to lower power requirements, which is important for battery-powered devices and large-scale deployments.
4. Improved Deployment Flexibility: Smaller, faster models can be deployed in a wider variety of scenarios, from embedded systems to web browsers.

While this course concentrates on Quantization, which involves representing model parameters and/or activations with lower-precision numbers (like 8-bit integers instead of 32-bit floats), it's one technique within a broader set of compression strategies. Understanding these other methods provides valuable context:

• Pruning: This technique involves identifying and removing redundant or less important parameters (weights) or structures (like entire neurons or channels) from the model.
  • Unstructured Pruning: Removes individual weights, often resulting in sparse matrices that require specialized hardware or libraries for speedup.
  • Structured Pruning: Removes larger, regular blocks of weights (e.g., entire channels or filters), making it easier to gain speedups on standard hardware.
• Knowledge Distillation: Here, a smaller "student" model is trained to mimic the behavior of a larger, pre-trained "teacher" model. The student learns from the teacher's outputs (e.g., probability distributions over classes) or internal representations, effectively transferring the knowledge into a more compact form.
• Low-Rank Factorization: This method targets large weight matrices within the model (like those in fully connected or attention layers). It approximates these matrices by decomposing them into the product of smaller matrices, reducing the total number of parameters and associated computations. Techniques like Singular Value Decomposition (SVD) are often employed here.

A diagram illustrating common model compression techniques, highlighting quantization as the focus of this course.

Each of these techniques has its own set of trade-offs regarding the degree of compression achieved, the impact on model accuracy, the complexity of implementation, and the resulting inference speedup on different hardware platforms.

Quantization stands out, particularly for LLMs, because reducing numerical precision directly translates to lower memory bandwidth requirements (often a bottleneck) and can leverage highly optimized integer arithmetic operations available on many modern CPUs and GPUs. It often provides a good balance between compression ratio, performance improvement, and retained model accuracy.

The following sections will concentrate specifically on quantization, exploring why it's so effective for LLMs, the fundamental concepts behind representing numbers with fewer bits, and the different strategies used to apply it.