Quantization of LLM Model

In short, model quantization is a technique that reduces the precision of a machine learning model's numerical values (like weights and activations). Instead of using high-precision numbers (like 32-bit floating-point), it uses lower-precision numbers (like 8-bit integers).

Here's a simplified breakdown:

• What it does: Reduces the size of the model. Speeds up its execution. Decreases memory usage.
• Why it's useful: Makes models more efficient for deployment on devices with limited resources (like smartphones or IoT devices). Improves inference speed. Reduces power consumption.
• How it works: Essentially, it's about representing numerical values with fewer bits. This means less data to store and process.

Essentially, quantization makes AI models smaller and faster, making them more practical for real-world applications.

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