Accelerate Agentic Tool Calling with Serverless Model Customization in Amazon SageMaker
This article discusses how to fine-tune the Qwen 2.5 7B Instruct model for tool calling using RLVR on Amazon SageMaker. It covers dataset preparation, reward function design, training configuration, and deployment.
Why it matters
This article showcases how to leverage large language models and serverless infrastructure on AWS to accelerate the development of specialized AI agents.
Key Points
- 1Fine-tuned Qwen 2.5 7B Instruct model for tool calling using RLVR
- 2Prepared dataset across three distinct agent behaviors
- 3Designed tiered scoring reward function
- 4Interpreted training results and evaluated on held-out data
- 5Deployed the customized model
Details
The article describes the process of fine-tuning the Qwen 2.5 7B Instruct model, a large language model, for the task of tool calling. The authors used Reinforcement Learning from Valuable Feedback (RLVR) to train the model on a dataset spanning three distinct agent behaviors. The reward function was designed with a tiered scoring system to incentivize the model's performance. The training configuration and results interpretation are discussed, followed by an evaluation on held-out data with unseen tools. Finally, the authors detail the deployment of the customized model.
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