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

🔥 News

• [2026/06] Added support for the Gemma 4 model family
• [2026/05] Oumi v0.8 released with oumi deploy CLI for dedicated inference endpoints, an oumi-mcp MCP server for Claude/Cursor integration, batch API support across Anthropic/Fireworks/Together, and Transformers v5 / TRL / vLLM dependency upgrades
• [2026/03] Upgraded to Transformers v5, TRL v0.30, vLLM v0.19, and veRL v0.7 compatibility
• [2026/03] MCP Integration Phase 1: package scaffold and dependencies for MCP server support
• [2026/03] New: oumi deploy command for deploying oumi models dedicated inference endpoints on fireworks.ai and parasail
• [2026/03] Added support for Qwen3.5 model family
• [2026/03] Inference engines received multiple improvements: list_models api, improved error reporting
• [2026/02] Preview of using the Oumi Platform and Lambda to fine-tune and deploy a 4B model for user intent classification
• [2026/02] Lambda and Oumi partner for end-to-end custom model development
• [2025/12] Oumi v0.6.0 released with Python 3.13 support, oumi analyze CLI command, TRL 0.26+ support, and more
• [2025/12] WeMakeDevs AI Agents Assemble Hackathon: Oumi webinar on Finetuning for Text-to-SQL
• [2025/12] Oumi co-sponsors WeMakeDevs AI Agents Assemble Hackathon with over 2000 project submissions
• [2025/11] Oumi v0.5.0 released with advanced data synthesis, hyperparameter tuning automation, support for OpenEnv, and more

🔎 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, GRPO, 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, ...)

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

Choose the installation method that works best for you:

# Basic installation
uv pip install oumi
With GPU support
uv pip install 'oumi[gpu]'
Latest development version
uv pip install git+https://github.com/oumi-ai/oumi.git

# Pull the latest image
docker pull ghcr.io/oumi-ai/oumi:latest
Run oumi commands
docker run --gpus all -it ghcr.io/oumi-ai/oumi:latest oumi --help
Train with a mounted config
docker run --gpus all -v $(pwd):/workspace -it ghcr.io/oumi-ai/oumi:latest \
    oumi train --config /workspace/my_config.yaml

curl -LsSf https://oumi.ai/install.sh | bash

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:

# 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:

# 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, DeepSpeed, 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-Next 80B A3B | LoRA • Inference • Inference (Instruct) • Evaluation | | Qwen3 30B A3B | LoRA • Inference • Evaluation | | Qwen3 32B | LoRA • Inference • Evaluation | | Qwen3 14B | LoRA • Inference • Evaluation | | Qwen3 8B | FFT • Inference • Evaluation | | Qwen3 4B | FFT • Inference • Evaluation | | Qwen3 1.7B | FFT • Inference • Evaluation | | Qwen3 0.6B | FFT • 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 |

💎 Gemma 3 Family

| Model | Example Configurations | |-------|------------------------| | Gemma 3 4B Instruct | FFT • Inference • Evaluation | | Gemma 3 12B Instruct | LoRA • Inference • Evaluation | | Gemma 3 27B Instruct | LoRA • Inference • Evaluation |

🦉 OLMo 3 Family

| Model | Example Configurations | |-------|------------------------| | OLMo 3 7B Instruct | FFT • Inference • Evaluation | | OLMo 3 32B Instruct | LoRA • Inference • 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 | | Qwen3-VL 2B | Inference | | Qwen3-VL 4B | Inference | | Qwen3-VL 8B | Inference | | 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:

@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.