While Post-Training Quantization (PTQ) offers a straightforward way to reduce model size and accelerate inference, the basic methods discussed earlier can sometimes lead to significant accuracy degradation, particularly when aiming for very low precision like 4-bit integers ($INT4$). Simple calibration might not capture enough information to preserve the model's performance effectively.

This chapter focuses on advanced PTQ techniques specifically developed to achieve better accuracy, often approaching the performance of the original full-precision model, without requiring retraining.

You will learn about:

• GPTQ (Generalized PTQ): Understand how this method uses approximate second-order information (Hessian) to quantize weights more accurately layer by layer.
• AWQ (Activation-aware Weight Quantization): Learn how AWQ identifies and preserves important weights based on activation magnitudes, scaling weights to make quantization easier.
• SmoothQuant: Study this technique that tackles the challenge of quantizing activations with large outliers by smoothing the distribution between weights and activations.
• Comparison and Implementation: Analyze the trade-offs between these methods and discuss practical considerations for applying them.
• Hands-on Practice: Apply the GPTQ algorithm to quantize a large language model.

By the end of this chapter, you will understand the principles behind these advanced techniques and be equipped to apply them for more effective LLM quantization.