Data Preparation and Analysis for Chat Model Fine-Tuning
This guide focuses on the preprocessing and analysis of chat datasets to ensure they are ready for fine-tuning chat models like GPT-3.5-turbo. It includes methods for checking dataset formatting, calculating basic statistics, and estimating token counts to anticipate fine-tuning costs.
Loading and Analyzing the Dataset
Begin by loading your chat dataset from a JSONL file. The dataset is then analyzed to determine the number of examples and review the structure of the first conversation.
Validating Dataset Format
The dataset undergoes a series of checks to ensure proper formatting. These checks identify and categorize errors, making it easier to troubleshoot and prepare the data for fine-tuning. Key validations include verifying data types, ensuring the presence of necessary fields, and checking the content structure.
Token Counting and Data Warnings
After validation, the dataset is analyzed to identify potential issues like missing messages. Token counts are also calculated to understand the distribution and estimate fine-tuning costs. This analysis helps in assessing the dataset's suitability for fine-tuning and adjusting if necessary.
Cost Estimation
Finally, the total number of tokens is estimated to provide an approximation of the fine-tuning cost. The number of epochs for training is also determined based on the dataset's size and specific thresholds.