Fine-tune open-weight models using OpenAI-compatible APIs
Amazon Bedrock provides OpenAI compatible API endpoints for fine-tuning foundation models. These endpoints allow you to use familiar OpenAI SDKs and tools to create, monitor, and manage fine-tuning jobs with Amazon Bedrock models. This page highlights using these APIs for reinforcement fine-tuning.
Key capabilities
-
Upload training files – Use the Files API to upload and manage training data for fine-tuning jobs
-
Create fine-tuning jobs – Start fine-tuning jobs with custom training data and reward functions
-
List and retrieve jobs – View all fine-tuning jobs and get detailed information about specific jobs
-
Monitor job events – Track fine-tuning progress through detailed event logs
-
Access checkpoints – Retrieve intermediate model checkpoints created during training
-
Immediate inference – After fine-tuning completes, use the resulting fine-tuned model for on-demand inference through Amazon Bedrock's OpenAI-compatible APIs (Responses/chat completions API) without additional deployment steps
-
Easy migration – Compatible with existing OpenAI SDK codebases
Reinforcement fine-tuning workflow for open-weight models
Before fine-tuning, ensure you have the pre-requisites as Amazon Bedrock needs specific permissions to create and manage the fine-tuning process. For comprehensive security and permissions information, see Access and security for open-weight models.
Run reinforcement fine-tuning for open-weight models in 5 steps:
-
Upload Training Dataset – Use the Files API to upload prompts in required format (e.g., JSONL) with purpose "fine-tune" as the reinforcement fine-tuning training dataset. For more information, see Prepare data for open-weight models.
-
Configure Reward Function – Define a grader to score model responses based on correctness, structure, tone, or other objectives using Lambda functions. For more information, see Setting up reward functions for open-weight models.
-
Create Fine-tuning Job – Launch the reinforcement fine-tuning job using the OpenAI-compatible API by specifying base model, dataset, reward function, and other optional settings such as hyperparameters. For more information, see Create fine-tuning job.
-
Monitor Training Progress – Track job status, events, and training metrics using the fine-tuning jobs APIs. For more information, see List fine-tuning events. Access intermediate model checkpoints to evaluate performance at different training stages, see List fine-tuning checkpoints.
-
Run Inference – Use the fine-tuned model ID directly for inference through Amazon Bedrock's OpenAI-compatible Responses or Chat Completions APIs. For more information, see Run inference with fine-tuned model.
Supported regions and endpoints
The following table shows the foundation models and regions that support OpenAI compatible fine-tuning APIs:
| Provider | Model | Model ID | Region name | Region | Endpoint |
|---|---|---|---|---|---|
| OpenAI | Gpt-oss-20B | openai.gpt-oss-20b | US West (Oregon) | us-west-2 | bedrock-mantle.us-west-2.api.aws |
| Qwen | Qwen3 32B | qwen.qwen3-32b | US West (Oregon) | us-west-2 | bedrock-mantle.us-west-2.api.aws |
