AWS Walkthrough: Fine-tune Qwen3-8B with SFT+GRPO for AI Product Tagging on SageMaker
AWS ML Blog · rss · 2026-09-16
AWS ML Blog published an end-to-end walkthrough: fine-tune Qwen3-8B on Amazon SageMaker serverless model customization to build an AI-powered retail product tagging system. The core argument: when the taxonomy is stable and outputs can be programmatically scored, a customized small model beats prompt-engineered frontier models on cost.
Three-stage pipeline:
- Data prep: transform the Kaggle Amazon Sales Dataset (1,000+ products) via SageMaker Processing into versioned SFT/RLVR JSONL datasets with a nine-category tagging schema.
- Training: SFTTrainer (LoRA, rank 16, 3 epochs) teaches the schema, then RLVRTrainer with GRPO optimizes the missing-vs-extra-tag tradeoff against a deterministic reward—no GPU selection needed, capacity is managed by AWS.
- Deployment: the model package is served on an ml.g6.2xlarge asynchronous inference endpoint for batch catalog enrichment, with an optional custom vLLM image.
Full code included; differences from the older Training Jobs approach are explained.
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