LLM Optimization LLM Part 2 - Large Language Model to Small Language Model

Concise Guide to Large Language Model Quantization
#modelparameters #ModelQuantization #LLM #slm
LLMs are revolutionizing the way we interact with machines, but their immense power comes at a cost - massive computational demands and storage requirements. This can limit their deployment on various devices and hinder real-world applications. Here's where Quantization steps in as a game-changer.
We'll unpack the core concept of quantization - the art of representing complex data with less precision while maintaining accuracy. You'll understand how it applies to LLMs, specifically focusing on weight quantization, the heart of the process.
Chapter 1: Why we need Model Compression
Chapter 2: Model Compression Techniques
Chapter 3: What is Quantization?
Chapter 4: Advantages of Quantization?
Chapter 5: Quantization Techniques
Chapter 6: LLM on CPUs
Chapter 7: Conclusion and Callout
This video is part of the LLM Optimization Series
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LLM Optimization LLM Part 2 - Large Language Model to Small Language Model - Quantization simplified
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LLM Optimization Part 1 - Calculating the True Cost of LLM
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