Everything you need to build state-of-the-art foundation models, end-to-end.

Oumi is a fully open-source platform that streamlines the entire lifecycle of foundation models - from data preparation and training to evaluation and deployment. Whether you're developing on a laptop, launching large scale experiments on a cluster, or deploying models in production, Oumi provides the tools and workflows you need.

With Oumi, you can:

• 🚀 Train and fine-tune models from 10M to 405B parameters using state-of-the-art techniques (SFT, LoRA, QLoRA, DPO, and more)
• 🤖 Work with both text and multimodal models (Llama, Qwen, Phi, and others)
• 🔄 Synthesize and curate training data with LLM judges
• ⚡️ Deploy models efficiently with popular inference engines (vLLM, SGLang)
• 📊 Evaluate models comprehensively across standard benchmarks
• 🌎 Run anywhere - from laptops to clusters to clouds (AWS, Azure, GCP, Lambda, and more)
• 🔌 Integrate with both open models and commercial APIs (OpenAI, Anthropic, Vertex AI, Together, Parasail, ...)

All with one consistent API, production-grade reliability, and all the flexibility you need for research.

Learn more at oumi.ai, or jump right in with the quickstart guide.

Notebook	Try in Colab	Goal
🎯 Getting Started: A Tour		Quick tour of core features: training, evaluation, inference, and job management
🔧 Model Finetuning Guide		End-to-end guide to LoRA tuning with data prep, training, and evaluation
📚 Model Distillation		Guide to distilling large models into smaller, efficient ones
📋 Model Evaluation		Comprehensive model evaluation using Oumi's evaluation framework
☁️ Remote Training		Launch and monitor training jobs on cloud (AWS, Azure, GCP, Lambda, etc.) platforms
📈 LLM-as-a-Judge		Filter and curate training data with built-in judges
🔄 vLLM Inference Engine		Fast inference at scale with the vLLM engine

Installing oumi in your environment is straightforward:

# Install the package (CPU & NPU only)
pip install oumi  # For local development & testing

# OR, with GPU support (Requires Nvidia or AMD GPU)
pip install oumi[gpu]  # For GPU training

# To get the latest version, install from the source
pip install git+https://github.com/oumi-ai/oumi.git

For more advanced installation options, see the installation guide.

You can quickly use the oumi command to train, evaluate, and infer models using one of the existing recipes:

# Training
oumi train -c configs/recipes/smollm/sft/135m/quickstart_train.yaml

# Evaluation
oumi evaluate -c configs/recipes/smollm/evaluation/135m/quickstart_eval.yaml

# Inference
oumi infer -c configs/recipes/smollm/inference/135m_infer.yaml --interactive

For more advanced options, see the training, evaluation, inference, and llm-as-a-judge guides.

You can run jobs remotely on cloud platforms (AWS, Azure, GCP, Lambda, etc.) using the oumi launch command:

# GCP
oumi launch up -c configs/recipes/smollm/sft/135m/quickstart_gcp_job.yaml

# AWS
oumi launch up -c configs/recipes/smollm/sft/135m/quickstart_aws_job.yaml

# Azure
oumi launch up -c configs/recipes/smollm/sft/135m/quickstart_azure_job.yaml

# Lambda
oumi launch up -c configs/recipes/smollm/sft/135m/quickstart_lambda_job.yaml

Note: Oumi is in beta and under active development. The core features are stable, but some advanced features might change as the platform improves.

If you need a comprehensive platform for training, evaluating, or deploying models, Oumi is a great choice.

Here are some of the key features that make Oumi stand out:

• 🔧 Zero Boilerplate: Get started in minutes with ready-to-use recipes for popular models and workflows. No need to write training loops or data pipelines.
• 🏢 Enterprise-Grade: Built and validated by teams training models at scale
• 🎯 Research Ready: Perfect for ML research with easily reproducible experiments, and flexible interfaces for customizing each component.
• 🌐 Broad Model Support: Works with most popular model architectures - from tiny models to the largest ones, text-only to multimodal.
• 🚀 SOTA Performance: Native support for distributed training techniques (FSDP, DDP) and optimized inference engines (vLLM, SGLang).
• 🤝 Community First: 100% open source with an active community. No vendor lock-in, no strings attached.

Explore the growing collection of ready-to-use configurations for state-of-the-art models and training workflows:

Note: These configurations are not an exhaustive list of what's supported, simply examples to get you started. You can find a more exhaustive list of supported models, and datasets (supervised fine-tuning, pre-training, preference tuning, and vision-language finetuning) in the oumi documentation.

This section lists all the language models that can be used with Oumi. Thanks to the integration with the 🤗 Transformers library, you can easily use any of these models for training, evaluation, or inference.

Models prefixed with a checkmark (✅) have been thoroughly tested and validated by the Oumi community, with ready-to-use recipes available in the configs/recipes directory.

To learn more about all the platform's capabilities, see the Oumi documentation.

Oumi is a community-first effort. Whether you are a developer, a researcher, or a non-technical user, all contributions are very welcome!

• To contribute to the oumi repository, please check the CONTRIBUTING.md for guidance on how to contribute to send your first Pull Request.
• Make sure to join our Discord community to get help, share your experiences, and contribute to the project!
• If you are interested in joining one of the community's open-science efforts, check out our open collaboration page.

Oumi makes use of several libraries and tools from the open-source community. We would like to acknowledge and deeply thank the contributors of these projects! ✨ 🌟 💫

If you find Oumi useful in your research, please consider citing it:

@software{oumi2025,
  author = {Oumi Community},
  title = {Oumi: an Open, End-to-end Platform for Building Large Foundation Models},
  month = {January},
  year = {2025},
  url = {https://github.com/oumi-ai/oumi}
}

This project is licensed under the Apache License 2.0. See the LICENSE file for details.