Fine-tune open-weight models using OpenAI-compatible APIs - Amazon Bedrock
Key capabilitiesReinforcement fine-tuning workflow for open-weight modelsSupported regions and endpoints
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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:

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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:

Supported models and regions for 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