Problem statement: When is adapting model parameters necessary?
Effectively harnessing the power of LLMs is crucial for generating meaningful and accurate outputs. However, the process of tuning these models to produce the desired results can be challenging, particularly when dealing with a wide array of parameters that influence the model’s behavior.
In this chapter, we aim to address the complexities involved in adjusting LLMs by adapting key parameters such as the temperature, top_k, top_p, and various penalties. The main goal is understanding how these parameters interact and affect the model’s output, and how they can be adjusted to deliver the desired output.
To equip you with the knowledge and practical skills, we will do the following:
- Cover the mathematical foundations behind LLM parameters
- Learn how to manipulate these parameters to achieve specific outcomes
- Apply this knowledge through practical exercises to see the real-world...
