Improve your agent’s tool-calling accuracy with SFT and DPO on Amazon SageMaker AI
Quick Take
This article discusses how to enhance tool-calling accuracy in small language models using Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO) on Amazon SageMaker AI. It emphasizes evaluating model performance through comparisons with fine-tuned variants, allowing for informed decisions on model quality without managing infrastructure.
Key Points
- Utilizes Amazon SageMaker AI to streamline training without infrastructure management.
- Combines SFT and DPO to enhance small language model accuracy.
- Evaluates tool-calling accuracy against several fine-tuned model variants.
- Supports data-driven decisions for improving model quality.
Article Excerpt
From source RSS / original summaryIn 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.
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