Fine-tune OpenAI GPT-OSS models on Amazon SageMaker AI using Hugging Face libraries

This article provides a comprehensive guide to fine-tuning OpenAI's GPT-OSS models on Amazon SageMaker AI using Hugging Face libraries. Key highlights include:

• OpenAI released GPT-OSS models (20B and 120B) with Mixture-of-Experts architecture
• Models support 128,000 context length and specialized reasoning capabilities
• Fine-tuning process uses:
  • Hugging Face TRL library for supervised fine-tuning
  • Parameter-Efficient Fine-Tuning (PEFT) with LoRA
  • Distributed training with Hugging Face Accelerate and DeepSpeed ZeRO-3
• Demonstrated fine-tuning on a multilingual reasoning dataset
• Supports experiment tracking with MLflow and optional quantization techniques

The workflow enables enterprises to adapt GPT-OSS models to specific domains efficiently, with options for cost-effective parameter-efficient or full fine-tuning approaches.