github.com/oumi-ai/oumi @v0.8 sqlite

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

# 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

# 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

# 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

# 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