Amazon SageMaker JumpStart now supports fine-tuning of Foundation Models with domain adaptation
Starting today, Amazon SageMaker JumpStart provides the capability to fine-tune a large language model, particularly a text generation model on a domain-specific data set. Customers can now fine-tune models with their custom data set to improve performance in specific domains. For example, this blog describes how to use domain adaption to fine tune a GPT-J 6B model on publicly available financial data from the Security and Exchange Commission so that the model can generate more relevant text for financial services use cases. Customers can fine-tune Foundation Models such as GPT-J 6B and GPT-J 6B FP16 models for domain adaptation on JumpStart inside Amazon SageMaker Studio through UI, and through SageMaker Python SDK.
Starting today, Amazon SageMaker JumpStart provides the capability to fine-tune a large language model, particularly a text generation model on a domain-specific data set. Customers can now fine-tune models with their custom data set to improve performance in specific domains. For example, this blog describes how to use domain adaption to fine tune a GPT-J 6B model on publicly available financial data from the Security and Exchange Commission so that the model can generate more relevant text for financial services use cases. Customers can fine-tune Foundation Models such as GPT-J 6B and GPT-J 6B FP16 models for domain adaptation on JumpStart inside Amazon SageMaker Studio through UI, and through SageMaker Python SDK.
This feature for fine tuning of Foundation Models with domain adaptation on SageMaker JumpStart can be used in all regions where Amazon SageMaker JumpStart is available.
To learn how to use this new feature, please see SageMaker JumpStart documentation and the example notebook SageMaker JumpStart Foundation Models - Fine-tuning text generation GPT-J 6B model on domain specific dataset.