Bedrock - Fine-Tuned Inference Bug

It sounds like you're encountering a common issue with fine-tuned models in Amazon Bedrock. For custom fine-tuned models, there are two main options for setting up inference:

1. Provisioned Throughput: This option provides dedicated compute capacity with guaranteed throughput for consistent performance and lower latency. For most fine-tuned models (except Amazon Nova), you must purchase Provisioned Throughput and use the resulting Provisioned Model ARN instead of the original model ARN in your API calls.

2. On-demand inference: This requires deploying your custom model with a custom model deployment. After deployment, you invoke it using the ARN for the custom model deployment, and you only pay for what you use.

The issue you're experiencing is likely related to how you're referencing your fine-tuned model. When using the SDK or CLI, make sure you're using the correct ARN or ID for your custom model deployment, not the base model ID. If you're using the original model ARN directly in API calls, you might receive responses from the base model instead of your fine-tuned version.

Also, the API format for model invocation has changed, which explains why the "View API request" from the playground didn't work directly. You need to use the current API format with the correct parameters for your specific model.

To resolve this:

• Verify you're using the correct custom model deployment ARN/ID in your SDK calls
• Ensure you're using the current API format and parameters for model invocation
• If problems persist, consider purchasing Provisioned Throughput for your fine-tuned model as an alternative approach
  Sources
  Resolve errors when I fine-tune models on Amazon Bedrock | AWS re:Post
  Set up inference for a custom model - Amazon Bedrock
  Iterative fine-tuning on Amazon Bedrock for strategic model improvement | Artificial Intelligence

Dear Customer,

Thank you for confirming that you're using the correct ARN for your custom model deployment.

I understand it's frustrating to see differences in response quality between your fine-tuned model and the base model. Since this requires deeper investigation into your specific model configuration and training data, I recommend opening a support case with the Bedrock team [1].

When creating the case, please include:

• custom model deployment ARN
• Example prompts showing the quality difference
• Expected vs. actual outputs

[1] https://docs.aws.amazon.com/awssupport/latest/user/case-management.html