GraphCanon updated 2d · GitHub synced 2d
Decision brief
Oumi is a tool for fine-tuning, evaluating, and deploying open-source large language models (LLMs) such as Gemma 4, Qwen3.5, Qwen3.6, gpt-oss, DeepSeek-R1, among others.
Good fit when
- - You are working specifically with one of the supported open-source LLMs including Gemma 4 or Qwen variants.
- - Your project requires a dedicated tool that offers specific fine-tuning and evaluation functionalities for a wide range of language models.
Avoid when
- - If your focus is on proprietary models rather than open-source ones, Oumi may not offer the necessary support or integrations.
- - You require deployment flexibility beyond what Oumi provides for less commonly supported open-source LLMs outside its primary focus (e.g., Gemma 4, Qwen series).
- Requirements:
- Requires Docker; Docker is used for standardized and portable environment deployments.
Observed Jul 12, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Very active (1d since push)
- As of 2d
- Provenance
- Not a fork · Organization account
- As of 2d
- Security (OSV)
- No lockfile
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install oumiSimilar tools
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Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
A comprehensive tool for the management and deployment of various open-source language models (LLMs) including Gemma 4, Qwen3.5, Qwen3.6, gpt-oss, DeepSeek-R1, among others.
Capability facts
- Deploy
- Self-host
Source: dockerfile:Dockerfile · Aug 23, 2026
- Docker
- Dockerfile present
Source: dockerfile:Dockerfile · Aug 23, 2026
- CLI
- CLI entrypoint
Source: pyproject.toml:[project.scripts] · Aug 23, 2026
- Languages
- python
Source: github.language+pyproject.toml · Aug 23, 2026
Categories
Tags
README
🚀 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 |
Installation
Choose the installation method that works best for you:
Using pip (Recommended)
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## 📜 License
This project is licensed under the Apache License 2.0. See the [LICENSE](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](https://opensource.org/ai) for more information.
For agents
This page has a .md twin and JSON over the API.