🤖 Explore LLM Sampling in Chapter 6: Darek Kleczek unravels sampling methods like greedy decoding & top P sampling. Understand how these techniques shape text generation. 🧑🏾‍🎓 Full course with certification and class materials available free at http://wandb.me/building-llm-powered-apps 🏆 Daily swag draw and grand prize Airpods draw from Dec 1 and 31, 2023. Details at http://wandb.me/llm-apps-contest 🗣️ Join the course conversation on our Discord channel at http://wandb.me/course-discord 🏫 This is chapter 6 of 30 in the Building LLM-Powered Apps course. Episode Description Welcome to the next chapter of our free "Building LLM-Powered Apps" course offered by Weights & Biases. In this episode, our seasoned machine learning engineer, Darek Kleczek, demystifies the world of sampling methods in large language models (LLMs). 🌟* Chapter Highlights* -Sampling Techniques Unveiled: Dive into the core concept of sampling methods in LLMs, understanding how text is generated through token probabilities. -Greedy Decoding & Beam Search: Learn about greedy decoding and beam search, their limitations, and why they may not always produce the most natural text. -Temperature-Based Sampling: Discover how adjusting the temperature parameter can influence the diversity and utility of the generated text. -Top P Sampling: Get introduced to the concept of top P sampling, a technique that selects tokens based on a threshold, often leading to higher-quality outputs. -Practical Insights: Prepare for hands-on experiments in upcoming videos, where these theories will be put into action. 🎓 Enroll for Free: Join us on this educational journey to master the art of building LLM-powered applications. Enroll at http://wandb.me/building-llm-powered-apps. 👉 Next Chapter Sneak Peek: Stay tuned for our next chapter, where we'll conduct practical experiments with temperature and Top P sampling techniques.