Accelerate agentic tool calling with serverless model customization in Amazon SageMaker AI
📰 AWS Machine Learning
Fine-tune Qwen 2.5 7B Instruct for tool calling with RLVR in Amazon SageMaker AI
Action Steps
- Prepare dataset for three distinct agent behaviors
- Design reward function with tiered scoring
- Configure training and interpret results
- Evaluate model on held-out data with unseen tools
- Deploy fine-tuned model with serverless customization in Amazon SageMaker AI
Who Needs to Know This
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
Key Insight
💡 Fine-tuning with RLVR can improve tool calling performance in AI models
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🚀 Fine-tune Qwen 2.5 7B Instruct for tool calling with RLVR in SageMaker AI
Key Takeaways
Fine-tune Qwen 2.5 7B Instruct for tool calling with RLVR in Amazon SageMaker AI
Full Article
In this post, we walk through how we fine-tuned Qwen 2.5 7B Instruct for tool calling using RLVR. We cover dataset preparation across three distinct agent behaviors, reward function design with tiered scoring, training configuration and results interpretation, evaluation on held-out data with unseen tools, and deployment.
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