Revolutionizing Model Fine-Tuning: A Unified Platform for Model Comparison

In recent years, Generative AI has made significant strides across industries, making it easier for individuals and organizations to create applications involving artificial intelligence. While better prompt engineering addresses many issues, some domain-specific challenges are tough to tackle with just traditional prompt engineering. This is where fine-tuning proves to be useful. Fine-tuning involves training a pre-trained model on a specific dataset to improve its performance within that particular domain. However, fine-tuning can be very resource-intensive and costly. Organizations are increasingly adopting model fine-tuning to address specific use cases and to add a personalized touch. Fine-tuning open-source models, particularly on smaller datasets, has proven to deliver enhanced performance while maintaining data privacy—a crucial advantage in today’s data-sensitive landscape. This paper presents a unified platform that enables the fine-tuning of multiple open-source models on small datasets and compares their performance across various parameters. By leveraging quantized models and dynamic hyperparameter tuning, we adapt the fine-tuning process based on the complexity of the use case and the amount of data available.

Our methodology incorporates a dynamic data sampling approach called stratified random sampling. We employ Parameter-Efficient Fine-Tuning (PEFT), specifically QLoRA, for the fine-tuning process while automatically adjusting the prompt template and data format based on the model, use case, and data characteristics. This ensures compliance with responsible AI practices, data security, and efficient resource utilization. The pipeline preprocesses the data, splits it into training and validation sets based on a configurable ratio, and evaluates the model’s post-fine-tuning for accuracy, time consumption, and other performance metrics. Our intensive research and testing with models demonstrate the platform’s capability to provide a comprehensive analysis. Metrics like loss at each epoch, accuracy, F1 score, perplexity, and hallucination score are calculated for each model, offering insights that enable users to make informed decisions on the most suitable model for their specific use case.

Additionally, the platform outputs optimal hyperparameters, representative datasets, and other valuable byproducts. This study aims to make finetuning models less expensive and more optimized. Organizations end up spending a lot of resources on finetuning multiple models until they end up achieving the best model for their use case. With just a fraction of this cost, our approach can provide workable insights on the best models for any approach. This will enable the businesses to save significant time and resources in the entire finetuning process. It will also give an upfront comparison of all the models and their respective performance analysis. This research contributes to the field by offering a robust, adaptable solution to common challenges in business analytics, illustrating the transformative potential of generative AI.