Fine-tune OpenAI GPT-OSS models using Amazon SageMaker HyperPod recipes

This article provides a comprehensive guide to fine-tuning OpenAI GPT-OSS models using Amazon SageMaker HyperPod recipes and training jobs, focusing on distributed training of large language models.

• Key features of the solution include:
  • Fine-tuning GPT-OSS models on a multilingual reasoning dataset
  • Using SageMaker HyperPod recipes for simplified distributed training
  • Supporting both persistent HyperPod clusters and on-demand training jobs
  • Deploying fine-tuned models to SageMaker endpoints with vLLM
• Training options:
  • SageMaker HyperPod: Persistent, preconfigured cluster for continuous development
  • SageMaker Training Jobs: Fully managed, on-demand compute resources
• Deployment highlights:
  • Custom vLLM container for SageMaker endpoints
  • OpenAI-style API compatibility
  • Support for model artifacts from S3 or Hugging Face Hub

The workflow simplifies complex distributed training of large language models, reducing setup time from weeks to minutes while providing enterprise-grade inference capabilities.