Accelerate agentic tool calling with serverless model customization in Amazon SageMaker AI

1. 1Prepare dataset for three distinct agent behaviors
2. 2Design reward function with tiered scoring
3. 3Configure training and interpret results
4. 4Evaluate model on held-out data with unseen tools
5. 5Deploy fine-tuned model with serverless customization in Amazon SageMaker AI

AI engineers and machine learning researchers on a team can benefit from this technique to improve model performance, while product managers can leverage the results to enhance product capabilities

💡 Fine-tuning with RLVR can improve tool calling performance in AI models

🚀 Fine-tune Qwen 2.5 7B Instruct for tool calling with RLVR in SageMaker AI