30 Days of LLMs: Day 6 — Sampling Methods in LLMs Explained
Darek Kleczek unravels sampling methods like greedy decoding & top P sampling. Understand how these techniques shape text generation. Enroll & Discover More.
Created on December 8|Last edited on December 10
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Day 6: Sampling Methods in LLMs Explained
Welcome to the sixth installment of our free course "Building LLM-Powered Apps," presented by Weights & Biases. In this chapter, our machine learning engineer, Darek Kleczek, clarifies the various sampling methods used 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.
Key Course Information
- No deep machine learning knowledge is needed, just some familiarity with Python programming.
- Strategies for continual enhancement of your LLM applications.
- Unique perspectives on the LLM tools used by Weights & Biases.
Free Enrollment
Preview of the Next Chapter
In the next chapter, we'll engage in hands-on experiments using temperature and Top P sampling methods.
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