Overview of LLM Quantization Techniques & Where to Learn Each of Them?

Model Quantization enhances the efficiency of large language models (LLMs) by representing their parameters in low-precision data types. This article presents an overview of LLM quantization techniques and resources for learning each of them.

The article covers different quantization methods, including GGUF, AWQ, PTQ, GPTQ, and QAT, elucidating their mechanisms and applications in LLM optimization.

Each section provides learning resources, including tutorials, specifications, and practical guides, facilitating a deeper understanding of the quantization techniques.

This article serves as a comprehensive guide for individuals interested in exploring LLM quantization, offering insights into various techniques and resources for continued learning and professional development.

Table of Contents:

1. Introduction to Quantization
2. GGUF
3. AWQ
4. PTQ
5. GPTQ
6. QAT

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1. Introduction to Quantization

Model Quantization is a topic that has been gaining popularity recently. The concept of quantization in AI or specifically neural networks, is a technique to represent the weights, biases, and activations…