oumi: an end-to-end platform for training, evaluating, and deploying foundation models with zero boilerplate

What it solves

Oumi is an open-source platform designed to simplify the complex end-to-end lifecycle of building foundation models. It removes the need for developers to write repetitive boilerplate code for training loops, data pipelines, and deployment workflows, allowing them to move from data preparation to production more quickly.

How it works

Oumi provides a unified API and a command-line interface (CLI) that allows users to execute training, evaluation, and inference tasks using predefined "recipes" (configuration files). It integrates with popular inference engines like vLLM and SGLang, and supports distributed training techniques such as FSDP, DeepSpeed, and DDP. The platform can be run locally on laptops or launched remotely on cloud providers like AWS, Azure, GCP, and Lambda.

Who it’s for

It is built for ML researchers and enterprise teams who need to develop, fine-tune, and deploy foundation models (ranging from 10M to 405B parameters) across various modalities (text and multimodal) and environments.

Highlights

• End-to-End Lifecycle: Covers data synthesis, curation, training, evaluation, and deployment.
• Broad Model Support: Compatible with a wide range of architectures including Llama, DeepSeek, Qwen, and Phi.
• Advanced Training Techniques: Native support for SFT, LoRA, QLoRA, and GRPO.
• Cloud Integration: Ability to launch and monitor jobs remotely across multiple major cloud platforms.
• LLM-as-a-Judge: Built-in tools for filtering and curating training data using LLM judges.