Customize a model with fine-tuning in Amazon Bedrock - Amazon Bedrock
Services or capabilities described in AWS documentation might vary by Region. To see the differences applicable to the AWS European Sovereign Cloud Region, see the AWS European Sovereign Cloud User Guide.

Customize a model with fine-tuning in Amazon Bedrock

With Amazon Bedrock, you can train a foundation model to improve performance on specific tasks (known as fine-tuning). For information about fine-tuning Amazon Nova models, see Fine-tuning Amazon Nova models.

Supported models and Regions for fine-tuning

The following table shows the foundation models that you can fine-tune:

Provider Model Model ID Single-region model support
Amazon Nova 2 Lite amazon.nova-2-lite-v1:0:256k

us-east-1

Amazon Nova Canvas amazon.nova-canvas-v1:0

us-east-1

Amazon Nova Lite amazon.nova-lite-v1:0:300k

us-east-1

Amazon Nova Micro amazon.nova-micro-v1:0:128k

us-east-1

Amazon Nova Pro amazon.nova-pro-v1:0:300k

us-east-1

Amazon Titan Image Generator G1 amazon.titan-image-generator-v1:0

us-east-1

Amazon Titan Image Generator G1 v2 amazon.titan-image-generator-v2:0

us-east-1

us-west-2

Amazon Titan Multimodal Embeddings G1 amazon.titan-embed-image-v1:0

us-east-1

us-west-2

Amazon Titan Text G1 - Express amazon.titan-text-express-v1:0:8k

us-east-1

us-west-2

Anthropic Claude 3 Haiku anthropic.claude-3-haiku-20240307-v1:0:200k

us-west-2

Meta Llama 3.1 70B Instruct meta.llama3-1-70b-instruct-v1:0:128k

us-west-2

Meta Llama 3.1 8B Instruct meta.llama3-1-8b-instruct-v1:0:128k

us-west-2

Meta Llama 3.2 11B Instruct meta.llama3-2-11b-instruct-v1:0:128k

us-west-2

Meta Llama 3.2 1B Instruct meta.llama3-2-1b-instruct-v1:0:128k

us-west-2

Meta Llama 3.2 3B Instruct meta.llama3-2-3b-instruct-v1:0:128k

us-west-2

Meta Llama 3.2 90B Instruct meta.llama3-2-90b-instruct-v1:0:128k

us-west-2

Meta Llama 3.3 70B Instruct meta.llama3-3-70b-instruct-v1:0:128k

us-west-2

For information about model customization hyperparameters for each model, see Custom model hyperparameters.