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30 Days of LLMs: Day 4 — LLM Foundations: Understanding Tokenization & Training

In Day 4 of the Weights & Biases 30 Days of LLMs, we dive into a variety of use cases for text generation by algorithms and LLMs, and explore the theory of the training processes and reinforcement learning.
Created on December 8|Last edited on December 10
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Day 4: LLM Foundations: Understanding Tokenization & Training

Begin your exploration into the complexities of Large Language Models (LLMs) with the fourth part of our complimentary course, "Building LLM-Powered Apps," offered by Weights & Biases. Participate alongside our machine learning engineer, Darek Kleczek, who will guide you through the intricate details and varied uses of LLMs.

Chapter Highlights

  • Exploring LLM Applications: Delve into a variety of use cases for text generation by algorithms and LLMs.
  • Insights into LLM Mechanics: Darek offers an inside look at the process, illustrating that deep expertise in LLMs is not a prerequisite for app development.
  • Understanding Tokenization: Discover the essential step of tokenization, which converts text into numerical tokens for LLM processing.
  • Overview of Training Stages: Learn about the two principal training stages of LLMs: pre-training and supervised fine-tuning.
  • Exploring Reinforcement Learning: Investigate further stages like reinforcement learning with human feedback, where models like GPT-4 are refined to produce responses that align more with human preferences.

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

Embark on this educational adventure to excel in crafting LLM-powered applications. Sign up now.

Preview of the Next Chapter

Join the practical session in our next chapter, focusing on hands-on LLM experiments using a Jupyter Notebook.
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