LLM Explained | Common LLM Terms You Should Know | KodeKloud
Discover essential terms and considerations for selecting LLMs, delve into quantization techniques for optimizing memory and compute costs, and explore the nuances of context size in model performance. Learn how to fine-tune LLM outputs using temperature, top P, and top K parameters, unlocking creativity while maintaining control. Join us as we demystify the world of LLMs and empower developers to leverage their full potential.
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⬇️Below are the topics we are going to discuss in this video:
00:00 - Introduction
00:33 - LLM Selection
01:46 - Quantization
02:25 - Context Size or Context Window
03:34 - Temperature
04:20 - Top P or Nucleus Sampling
04:59 - Top K
06:06 - Multi-Modal and RAG
06:47 - Conclusion
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