Artificial Intelligence
Author: Amin Dashti
Amin Dashti is a Senior Data Scientist and researcher at AWS who bridges deep theoretical insight with practical machine learning expertise. With a background in theoretical physics and over eight years of experience, he has designed and deployed scalable models across domains — from predictive analytics and statistical inference in financial systems to computer vision (CV) and natural language processing (NLP).
Improve your agent’s tool-calling accuracy with SFT and DPO on Amazon SageMaker AI
In this post, you learn how to use Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO) together to improve the tool-calling accuracy of a small language model (SLM). The example uses Amazon SageMaker AI training jobs, so you can focus on training code instead of managing your own training infrastructure. You also learn how to evaluate tool-calling accuracy and compare a base model to several fine-tuned variants, so you can make data-driven decisions about model quality.