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

🔥 News

• [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

• [2025/11] Example notebook to perform RLVF fine-tuning with OpenEnv, an open source library from the Meta PyTorch team for creating, deploying, and distributing agentic RL environments

• [2025/10] Oumi v0.4.1 and v0.4.2 released] with support for Qwen3-VL and Transformers v4.56, data synthesis documentation and examples, and many bug fixes

Older updates

• [2025/09] Oumi v0.4.0 released with DeepSpeed support, a Hugging Face Hub cache management tool, KTO/Vision DPO trainer support

• [2025/08] Training and 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, 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, ...)

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

Choose the installation method that works best for you:

Using pip (Recommended)

# Basic installation uv pip install oumi

# 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`

Don't have uv? Install it or use pip instead.

Using Docker

# Pull the latest image docker pull ghcr.io/oumi-ai/oumi:latest

# 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`

Quick Install Script (Experimental)

Try Oumi without setting up a Python environment. This installs Oumi in an isolated environment:

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

# 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/quickstart_gcp_job.yaml

# 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_gcp_job.yaml --resources.cloud aws

Azure

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

Lambda

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

📋 Click to see more supported models

Instruct Models

Model Size Paper HF Hub License Open 1

✅ SmolLM-Instruct 135M/360M/1.7B Blog Hub Apache 2.0 ✅

✅ DeepSeek R1 Family 1.5B/8B/32B/70B/671B Blog Hub MIT ❌

✅ Llama 3.1 Instruct 8B/70B/405B Paper Hub License ❌

✅ Llama 3.2 Instruct 1B/3B Paper Hub License ❌

✅ Llama 3.3 Instruct 70B Paper Hub License ❌

✅ Phi-3.5-Instruct 4B/14B Paper Hub License ❌

✅ Qwen3 0.6B-32B Paper Hub License ❌

Qwen2.5-Instruct 0.5B-70B Paper Hub License ❌

OLMo 2 Instruct 7B Paper Hub Apache 2.0 ✅

✅ OLMo 3 Instruct 7B/32B Paper Hub Apache 2.0 ✅

MPT-Instruct 7B Blog Hub Apache 2.0 ✅

Command R 35B/104B Blog Hub License ❌

Granite-3.1-Instruct 2B/8B Paper Hub Apache 2.0 ❌

Gemma 2 Instruct 2B/9B Blog Hub License ❌

✅ Gemma 3 Instruct 4B/12B/27B Blog Hub License ❌

DBRX-Instruct 130B MoE Blog Hub Apache 2.0 ❌

Falcon-Instruct 7B/40B Paper Hub Apache 2.0 ❌

✅ Llama 4 Scout Instruct 17B (Activated) 109B (Total) Paper Hub License ❌

✅ Llama 4 Maverick Instruct 17B (Activated) 400B (Total) Paper Hub License ❌

Vision-Language Models

Model Size Paper HF Hub License Open

✅ Llama 3.2 Vision 11B Paper Hub License ❌

✅ LLaVA-1.5 7B Paper Hub License ❌

✅ Phi-3 Vision 4.2B Paper Hub License ❌

✅ BLIP-2 3.6B Paper Hub MIT ❌

✅ Qwen2-VL 2B Blog Hub License ❌

✅ Qwen3-VL 2B/4B/8B Blog Hub License ❌

✅ SmolVLM-Instruct 2B Blog Hub Apache 2.0 ✅

Base Models

Model Size Paper HF Hub License Open

✅ SmolLM2 135M/360M/1.7B Blog Hub Apache 2.0 ✅

✅ Llama 3.2 1B/3B Paper Hub License ❌

✅ Llama 3.1 8B/70B/405B Paper Hub License ❌

✅ GPT-2 124M-1.5B Paper Hub MIT ✅

DeepSeek V2 7B/13B Blog Hub License ❌

Gemma2 2B/9B Blog Hub License ❌

GPT-J 6B Blog Hub Apache 2.0 ✅

GPT-NeoX 20B Paper Hub Apache 2.0 ✅

Mistral 7B Paper Hub Apache 2.0 ❌

Mixtral 8x7B/8x22B Blog Hub Apache 2.0 ❌

MPT 7B Blog Hub Apache 2.0 ✅

OLMo 1B/7B Paper Hub Apache 2.0 ✅

✅ Llama 4 Scout 17B (Activated) 109B (Total) Paper Hub License ❌

Reasoning Models

Model Size Paper HF Hub License Open

✅ gpt-oss 20B/120B Paper Hub Apache 2.0 ❌

✅ Qwen3 0.6B-32B Paper Hub License ❌

✅ Qwen3-Next 80B-A3B Blog Hub License ❌

Qwen QwQ 32B Blog Hub License ❌

Code Models

Model Size Paper HF Hub License Open

✅ Qwen2.5 Coder 0.5B-32B Blog Hub License ❌

DeepSeek Coder 1.3B-33B Paper Hub License ❌

StarCoder 2 3B/7B/15B Paper Hub License ✅

Math Models

Model Size Paper HF Hub License Open

DeepSeek Math 7B Paper Hub License ❌

📖 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} }

@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.

Footnotes

• 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. ↩