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Explore the concept of quantization in large language models (LLMs) and its importance in model optimization. Learn how to apply quantization techniques during training, fine-tuning, and inference processes. Understand the significance of model size and its impact on performance, as well as the scaling laws that govern LLMs. Access practical examples through provided Jupyter notebooks, covering quantization implementation and data type considerations. Gain insights into the theoretical aspects of quantization through an accompanying PowerPoint presentation. Enhance your understanding of LLM optimization techniques and their practical applications in machine learning engineering.
Syllabus
LLM Quantization why size matters
Taught by
The Machine Learning Engineer