Learn AI with KodeKloud - 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. 🆓Join KodeKloud Community for FREE: https://kode.wiki/KodeKloudCommunity_YT ⬇️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 Check out our learning paths at KodeKloud to get started: ▶️ Cloud Computing: https://kode.wiki/CloudLearningPath_YT ▶️ Kubernetes: https://bit.ly/KubernetesLearningPath ▶️AWS: https://kode.wiki/awslearningpath_yt ▶️Azure: https://kode.wiki/azurelearningpath_yt ▶️Google Cloud Platform: https://kode.wiki/GCPlearningpath_YT ▶️ Linux: https://bit.ly/LinuxLearningPath ▶️ DevOps Learning Path: https://bit.ly/DevOpsLearningPath-YT #LargeLanguageModels #LLM #DevelopersGuide #ModelOptimization #Quantization #DevOps #CloudComputing #AI #MachineLearning #kodekloud For more updates on courses and tips, follow us on: 🌐 Website: https://kodekloud.com/ 🌐 LinkedIn: https://www.linkedin.com/company/kodekloud/ 🌐 Twitter: https://twitter.com/KodeKloudHQ 🌐 Facebook: https://www.facebook.com/KodeKloudHQ 🌐 Instagram: https://www.instagram.com/kodekloud/ 🌐 Blog: https://kodekloud.com/blog/