Following the theoretical exploration of advanced quantization techniques in the previous chapter, this section focuses on the practical application of these methods using common software libraries. The objective is to move from understanding concepts like low-bit quantization (e.g., ) and algorithms such as GPTQ and AWQ to actually performing these operations on Large Language Models.
You will work with widely-used toolkits designed for LLM quantization:
bitsandbytes facilitates efficient low-bit operations.Transformers and Accelerate.AutoGPTQ and AutoAWQ libraries, respectively.Throughout this chapter, we will cover the necessary steps to quantize models using these tools, examine how to compare the results and performance characteristics obtained from different libraries, and address potential compatibility challenges between models and toolkits. By the end, you will have hands-on experience using these libraries to prepare LLMs for efficient deployment.
2.1 Overview of LLM Quantization Libraries
2.2 Using bitsandbytes for Low-Bit Operations
2.3 Quantization with Hugging Face Transformers and Accelerate
2.4 Applying GPTQ using AutoGPTQ
2.5 Applying AWQ using AutoAWQ
2.6 Comparing Toolkit Outputs and Performance
2.7 Handling Model Compatibility Issues
2.8 Practice: Quantizing Models with Multiple Toolkits