Optimize the Llama-2 Model with Gretel’s Text SQS

This blog discusses how Gretel's Text SQS can be used to optimize the Llama-2 model during fine-tuning and generation of synthetic text data. The experiment uses a sample of 1000 records from the SAMSum dataset, which is a text summarization dataset containing dialogues and human-written summaries in English. Gretel's Text Synthetic Quality Score (SQS) is used to evaluate the quality of generated text records during fine-tuning. Results show that increasing steps in lower ranges improves learning and results in better text SQS, with scores above 80 considered "excellent." The experiment demonstrates how Gretel's synthetic text evaluation score can precisely measure the quality of generated records, allowing developers to focus on building and uncovering insights.