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: 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: kode.wiki/CloudLearningPath_YT
▶️ Kubernetes: bit.ly/KubernetesLearningPath
▶️AWS: kode.wiki/awslearningpath_yt
▶️Azure: kode.wiki/azurelearningpath_yt
▶️Google Cloud Platform: kode.wiki/GCPlearningpath_YT
▶️ Linux: bit.ly/LinuxLearningPath
▶️ DevOps Learning Path: 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: kodekloud.com/
🌐 LinkedIn: / kodekloud
🌐 Twitter: / kodekloudhq
🌐 Facebook: / kodekloudhq
🌐 Instagram: / kodekloud
🌐 Blog: kodekloud.com/blog/

[@cykovisuals]

This is great! Love the flowcharts and infographics. Very useful! 😊

[@lukegvoigt]

Thanks for the video. I've recently gotten interested in Local models. Coming from ChatGPT, I didn't know terms like parameters, top p, etc. Thanks for the to-the-point presentation.

[@Kevin.Kawchak]

Thank you

[@kent_calvin]

Fantastic 🎉

[@SergiySubota]

Sorry, perhaps 1234 is not the best example for an 8-bit integer ranging 0..255 unsigned (1:59)