oumi-ai/oumi

What it solves

Oumi is an open-source platform designed to remove the complexity and boilerplate associated with the entire lifecycle of foundation models. It provides a unified API and ready-to-use recipes to handle data preparation, training, evaluation, and deployment, allowing developers to move from research to production without writing custom training loops or data pipelines.

How it works

Oumi streamlines the model development process through a set of CLI tools and configurations. Users can use oumi train, oumi evaluate, and oumi infer commands combined with YAML configuration files (recipes) to execute specific workflows. It supports a wide range of training techniques (SFT, LoRA, QLoRA, GRPO) and integrates with popular inference engines like vLLM and SGLang. Additionally, it includes a oumi launch command to deploy training and inference jobs remotely on cloud platforms such as AWS, Azure, GCP, and Lambda.

Who it’s for

It is built for ML researchers and enterprise teams who need to build, fine-tune, and deploy state-of-the-art foundation models (ranging from 10M to 405B parameters) across various environments, from local laptops to large-scale cloud clusters.

Highlights

• End-to-End Lifecycle: Covers everything from data synthesis and curation using LLM-as-a-Judge to final deployment.
• Zero Boilerplate: Provides a library of pre-configured recipes for popular model families like Llama, Qwen, and DeepSeek.
• Multi-Cloud Support: Native ability to launch jobs on AWS, Azure, GCP, and Lambda.
• Broad Model Support: Compatible with both text-only and multimodal models.
• SOTA Training Techniques: Native support for distributed training (FSDP, DeepSpeed, DDP) and advanced fine-tuning methods like GRPO.