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

🔥 News

• [2025/08] Inference support for OpenAI's gpt-oss-20b and gpt-oss-120b: recipes here
• [2025/08] Aug 14 Webinar - OpenAI's gpt-oss: Separating the Substance from the Hype.
• [2025/08] Oumi v0.3.0 released with model quantization (AWQ), an improved LLM-as-a-Judge API, and Adaptive Inference
• [2025/07] Recipe for Qwen3 235B
• [2025/07] July 24 webinar: "Training a State-of-the-art Agent LLM with Oumi + Lambda"
• [2025/06] Oumi v0.2.0 released with support for GRPO fine-tuning, a plethora of new model support, and much more
• [2025/06] Announcement of Data Curation for Vision Language Models (DCVLR) competition at NeurIPS2025
• [2025/06] Recipes for training, inference, and eval with the newly released Falcon-H1 and Falcon-E models
• [2025/05] Support and recipes for InternVL3 1B
• [2025/04] Added support for training and inference with Llama 4 models: Scout (17B activated, 109B total) and Maverick (17B activated, 400B total) variants, including full fine-tuning, LoRA, and QLoRA configurations
• [2025/04] Recipes for Qwen3 model family
• [2025/04] Introducing HallOumi: a State-of-the-Art Claim-Verification Model (technical overview)
• [2025/04] Oumi now supports two new Vision-Language models: Phi4 and Qwen 2.5

🔎 About

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, DeepSeek, 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.

🚀 Getting Started

| 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 |

🔧 Usage

Installation

Installing oumi in your environment is straightforward:

```shell

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.

Oumi CLI

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

```shell

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.

Running Jobs Remotely

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

```shell

GCP

oumi launch up -c configs/recipes/smollm/sft/135m/quickstartgcpjob.yaml

AWS

oumi launch up -c configs/recipes/smollm/sft/135m/quickstartgcpjob.yaml --resources.cloud aws

Azure

oumi launch up -c configs/recipes/smollm/sft/135m/quickstartgcpjob.yaml --resources.cloud azure

Lambda

oumi launch up -c configs/recipes/smollm/sft/135m/quickstartgcpjob.yaml --resources.cloud lambda ```

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

💻 Why use Oumi?

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.

📚 Examples & Recipes

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.

Qwen Family

| Model | Example Configurations | |-------|------------------------| | Qwen3 30B A3B | LoRA • Inference • Evaluation | | Qwen3 32B | LoRA • Inference • Evaluation | | QwQ 32B | FFT • LoRA • QLoRA • Inference • Evaluation | | Qwen2.5-VL 3B | SFT • LoRA• Inference (vLLM) • Inference | | Qwen2-VL 2B | SFT • LoRA • Inference (vLLM) • Inference (SGLang) • Inference • Evaluation |

🐋 DeepSeek R1 Family

| Model | Example Configurations | |-------|------------------------| | DeepSeek R1 671B | Inference (Together AI) | | Distilled Llama 8B | FFT • LoRA • QLoRA • Inference • Evaluation | | Distilled Llama 70B | FFT • LoRA • QLoRA • Inference • Evaluation | | Distilled Qwen 1.5B | FFT • LoRA • Inference • Evaluation | | Distilled Qwen 32B | LoRA • Inference • Evaluation |

🦙 Llama Family

| Model | Example Configurations | |-------|------------------------| | Llama 4 Scout Instruct 17B | FFT • LoRA • QLoRA • Inference (vLLM) • Inference • Inference (Together.ai) | | Llama 4 Scout 17B | FFT | | Llama 3.1 8B | FFT • LoRA • QLoRA • Pre-training • Inference (vLLM) • Inference • Evaluation | | Llama 3.1 70B | FFT • LoRA • QLoRA • Inference • Evaluation | | Llama 3.1 405B | FFT • LoRA • QLoRA | | Llama 3.2 1B | FFT • LoRA • QLoRA • Inference (vLLM) • Inference (SGLang) • Inference • Evaluation | | Llama 3.2 3B | FFT • LoRA • QLoRA • Inference (vLLM) • Inference (SGLang) • Inference • Evaluation | | Llama 3.3 70B | FFT • LoRA • QLoRA • Inference (vLLM) • Inference • Evaluation | | Llama 3.2 Vision 11B | SFT • Inference (vLLM) • Inference (SGLang) • Evaluation |

🦅 Falcon family

| Model | Example Configurations | |-------|------------------------| | Falcon-H1 | FFT • Inference • Evaluation | | Falcon-E (BitNet) | FFT • DPO • Evaluation |

🎨 Vision Models

| Model | Example Configurations | |-------|------------------------| | Llama 3.2 Vision 11B | SFT • LoRA • Inference (vLLM) • Inference (SGLang) • Evaluation | | LLaVA 7B | SFT • Inference (vLLM) • Inference | | Phi3 Vision 4.2B | SFT • LoRA • Inference (vLLM) | | Phi4 Vision 5.6B | SFT • LoRA • Inference (vLLM) • Inference | | Qwen2-VL 2B | SFT • LoRA • Inference (vLLM) • Inference (SGLang) • Inference • Evaluation | | Qwen2.5-VL 3B | SFT • LoRA• Inference (vLLM) • Inference | | SmolVLM-Instruct 2B | SFT • LoRA |

🔍 Even more options

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.

📖 Documentation

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

🤝 Join the Community!

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.

🙏 Acknowledgements

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! ✨ 🌟 💫

📝 Citation

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

bibtex @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} }

📜 License

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

[^1]: Open models are defined as models with fully open weights, training code, and data, and a permissive license. See Open Source Definitions for more information.