Quick Start Guide to Large Language Models: Strategies and Best Practices for using ChatGPT and Other LLMs (for True Epub) by Sinan Ozdemir

Author:Sinan Ozdemir
Language: eng
Format: epub
ISBN: 9780138199425
Publisher: Addison-Wesley Professional
Published: 2023-06-15T00:00:00+00:00

Hyperparameter Selection and Optimization

With our JSONL document created and OpenAI CLI installed, we are ready to select our hyperparameters! Here's a list of key hyperparameters and their definitions:

Learning rate: The learning rate determines the size of the steps the model takes during optimization. A smaller learning rate leads to slower convergence but potentially better accuracy, while a larger learning rate speeds up training but may cause the model to overshoot the optimal solution.

Batch size: Batch size refers to the number of training examples used in a single iteration of model updates. A larger batch size can lead to more stable gradients and faster training, while a smaller batch size may result in a more accurate model but slower convergence.

Training epochs: An epoch is a complete pass through the entire training dataset. The number of training epochs determines how many times the model will iterate over the data, allowing it to learn and refine its parameters.

OpenAI has done a lot of work to find optimal settings for most cases, so we will lean on their recommendations for our first attempt. The only thing we will change is to train for 1 epoch instead of the default 4. We're doing this because we want to see how the performance looks before investing too much time and money. Experimenting with different values and using techniques like grid search will help you find the optimal hyperparameter settings for your task and dataset, but be mindful that this process can be time-consuming and costly.

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