Deciphering LLMs: From Transformers to Quantization

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Join us in the first video of our Mastering LLMs series, where we break down Large Language Models (LLMs), their uses, and the tasks they can perform. We explain the Transformer architecture, its importance, and how it works. Learn about different fine-tuning methods, including full fine-tuning and parameter-efficient fine-tuning, and discover how Quantization can save computational resources.
💡 What You’ll Learn:
Basics of LLMs and their uses.
Understanding the Transformer architecture.
Fine-tuning techniques for LLMs.
Introduction to Quantization in LLMs.
📘 Further Learning:
Check our blog link below for the topics that have been referenced in this video:
Understanding Attention Mechanism in Transformer Neural Networks - learnopencv.com/attention-mec...
Text Summarization using T5: Fine-Tuning and Building Gradio App - learnopencv.com/text-summariz...
BERT: Bidirectional Encoder Representations from Transformers - Unlocking the Power of Deep Contextualized Word Embeddings - learnopencv.com/bert-bidirect...
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Tags:
#LLMs #largelanguagemodel #Transformers #GenerativeAI #GenAI