Accelerate custom LLM deployment: Fine-tune with Oumi and deploy to Amazon Bedrock

This post is cowritten by David Stewart and Matthew Persons from Oumi.

Fine-tuning open source large language models (LLMs) often stalls between experimentation and production. Training configurations, artifact management, and scalable deployment each require different tools, creating friction when moving from rapid experimentation to secure, enterprise-grade environments.

In this post, we show how to fine-tune a Llama model using Oumi on Amazon EC2 (with the option to create synthetic data using Oumi), store artifacts in Amazon S3, and deploy to Amazon Bedrock using Custom Model Import for managed inference. While we use EC2 in this walkthrough, fine-tuning can be completed on other compute services such as Amazon SageMaker or Amazon Elastic Kubernetes Service, depending on your needs.

Benefits of Oumi and Amazon Bedrock

Oumi is an open source system that streamlines the foundation model lifecycle, from data preparation and training to evaluation. Instead of assembling separate tools for each stage ...