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How to use Oumi: what you can do with it

What is Oumi? Oumi is an open-source platform for the whole foundation model lifecycle: data preparation, training, evaluation and deployment. It fine-tunes models from 10M to 400B+ parameters with SFT, LoRA, QLoRA, DPO and GRPO, trains agentic tool-using models, synthesizes data with LLM judges, evaluates on benchmarks, serves with vLLM or SGLang, and runs from laptops to AWS, Azure, GCP, Lambda and Slurm through one CLI and ready-made recipes.

How do I use it? Install with uv pip install oumi, or uv pip install 'oumi[gpu]' for GPU support; then Train with oumi train -c configs/recipes/smollm/sft/135m/quickstart_train.yaml; Evaluate with oumi evaluate -c configs/recipes/smollm/evaluation/135m/quickstart_eval.yaml. Full steps below.

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What do you want to do?

3 use cases

Quick start with Oumi

  1. Check you have what it needs: Python with uv or pip, or Docker; A GPU for real training runs (install the gpu extra); Optional: cloud accounts for remote jobs with oumi launch.
  2. Open oumi-ai/oumi on GitHub and read the README and license.
  3. Install with uv pip install oumi, or uv pip install 'oumi[gpu]' for GPU support
  4. Train with oumi train -c configs/recipes/smollm/sft/135m/quickstart_train.yaml
  5. Evaluate with oumi evaluate -c configs/recipes/smollm/evaluation/135m/quickstart_eval.yaml
  6. Chat with it using oumi infer -c configs/recipes/smollm/inference/135m_infer.yaml --interactive

These steps come from our top use case, Train, evaluate and chat with a model from recipes, which also has a copy-paste agent prompt.

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About this repo

  • Who it is forML engineers and researchers who want reproducible training, evaluation and inference recipes without writing training loops.
  • Why it is hot9.4k stars, talked about in 2 AI sources we track
  • LanguagePython
  • Sourcegithub.com/oumi-ai/oumi ยท Back to Hot repos

Requirements

  • Python with uv or pip, or Docker
  • A GPU for real training runs (install the gpu extra)
  • Optional: cloud accounts for remote jobs with oumi launch

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Questions about Oumi

What is Oumi?

Oumi is an open-source platform for the whole foundation model lifecycle: data preparation, training, evaluation and deployment. It fine-tunes models from 10M to 400B+ parameters with SFT, LoRA, QLoRA, DPO and GRPO, trains agentic tool-using models, synthesizes data with LLM judges, evaluates on benchmarks, serves with vLLM or SGLang, and runs from laptops to AWS, Azure, GCP, Lambda and Slurm through one CLI and ready-made recipes. It is an open-source project on GitHub: https://github.com/oumi-ai/oumi.

How do I use Oumi?

Install with uv pip install oumi, or uv pip install 'oumi[gpu]' for GPU support; then Train with oumi train -c configs/recipes/smollm/sft/135m/quickstart_train.yaml; Evaluate with oumi evaluate -c configs/recipes/smollm/evaluation/135m/quickstart_eval.yaml. The use cases on this page give the full steps and a copy-paste agent prompt for each job.

Who is Oumi for?

ML engineers and researchers who want reproducible training, evaluation and inference recipes without writing training loops.

What can I do with Oumi?

3 use cases so far, for example: Train, evaluate and chat with a model from recipes; Run a fine-tuning job on a cloud GPU; Drive Oumi from Claude or Cursor. Each has steps and a copy-paste agent prompt.

What do I need to run Oumi?

Python with uv or pip, or Docker; A GPU for real training runs (install the gpu extra); Optional: cloud accounts for remote jobs with oumi launch.

What are alternatives to Oumi?

Other hot AI repos in Software and DevOps: ECC, Thinkrail, Superpowers, Deep Infer, Instructor.

Is Oumi safe to run?

Deduped does not own, audit or vouch for it. Read the README and license, run it on your own systems first and review every command before you run it.