# AI use cases for Oumi (oumi-ai/oumi)

Repo: [oumi-ai/oumi](https://github.com/oumi-ai/oumi) | Use cases: 3 | Page: https://dedupedai.com/use-cases/oumi-ai-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.

This file holds every use case of this repo. Each one starts with a "## " heading and ends with its URL.

- Train, evaluate and chat with a model from recipes: https://dedupedai.com/use-cases/oumi-ai-oumi/train-evaluate-and-chat-with-a-small-model/
- Run a fine-tuning job on a cloud GPU: https://dedupedai.com/use-cases/oumi-ai-oumi/run-a-training-job-on-a-cloud-gpu/
- Drive Oumi from Claude or Cursor: https://dedupedai.com/use-cases/oumi-ai-oumi/drive-oumi-from-claude-or-cursor/

## How to train, evaluate and chat with a model from recipes with Oumi

Repo: [oumi-ai/oumi](https://github.com/oumi-ai/oumi) | Difficulty: Beginner | Time: 45 minutes | Basis: From the README

Install Oumi and run the SmolLM quickstart recipes for training, evaluation and interactive inference.

**Who it is for:** Someone new to fine-tuning who wants a full train, eval and infer loop working before touching their own data.

**The problem:** Fine-tuning tutorials usually stop at training, leaving evaluation and inference as separate projects.

**The outcome:** A trained 135M model, an evaluation run and an interactive chat, all from YAML recipes.

### Steps

1. Install with `uv pip install oumi`, or `uv pip install 'oumi[gpu]'` for GPU support
2. Train with `oumi train -c configs/recipes/smollm/sft/135m/quickstart_train.yaml`
3. Evaluate with `oumi evaluate -c configs/recipes/smollm/evaluation/135m/quickstart_eval.yaml`
4. Chat with it using `oumi infer -c configs/recipes/smollm/inference/135m_infer.yaml --interactive`
5. Copy a recipe and point it at your own dataset when the quickstart works

### Prompt to paste into your agent

```text
Install oumi-ai/oumi and run the SmolLM 135M quickstart: oumi train, oumi evaluate and oumi infer --interactive with the recipe configs. Report the eval results and one sample answer, then show me which fields to change to use <my dataset>.
```

### Caveat

Oumi is in beta; the core is stable but some advanced features may change.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/oumi-ai-oumi/train-evaluate-and-chat-with-a-small-model/

## How to run a fine-tuning job on a cloud GPU with Oumi

Repo: [oumi-ai/oumi](https://github.com/oumi-ai/oumi) | Difficulty: Intermediate | Time: 1 hour | Basis: From the README

Use Oumi launch to send the same recipe to GCP, AWS, Azure or Lambda.

**Who it is for:** A researcher with a laptop who needs a real GPU for a few hours of training.

**The problem:** Moving a training job to the cloud usually means rewriting scripts for each provider.

**The outcome:** A remote training job started from one command, with the provider chosen by a flag.

### Steps

1. Install Oumi and set up credentials for your cloud provider
2. Launch on GCP with `oumi launch up -c configs/recipes/smollm/sft/135m/quickstart_gcp_job.yaml`
3. Target another cloud with a flag, for example `--resources.cloud lambda`
4. Or run locally in Docker with `docker pull ghcr.io/oumi-ai/oumi:latest`
5. Scale up to a larger model recipe once the small job works

### Prompt to paste into your agent

```text
Using oumi-ai/oumi, launch the SmolLM quickstart job on <cloud> with oumi launch up, monitor it until it finishes, and tell me how long it ran. Do not start any job larger than the quickstart without asking me.
```

### Caveat

You need working credentials for each cloud; the README links to the launch docs for setup.

Risk, cost: Cloud GPU jobs bill by the hour on your own cloud account; shut down resources when the job ends.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/oumi-ai-oumi/run-a-training-job-on-a-cloud-gpu/

## How to drive Oumi from Claude or Cursor with Oumi

Repo: [oumi-ai/oumi](https://github.com/oumi-ai/oumi) | Difficulty: Intermediate | Time: 30 minutes | Basis: From the README

Use the oumi-mcp MCP server so your coding agent can run Oumi training, evaluation and inference.

**Who it is for:** A developer who would rather describe a fine-tuning experiment to an agent than hand-edit YAML.

**The problem:** Training configs have many fields and the CLI has many commands, which slows down experimenting.

**The outcome:** An agent that can call Oumi through MCP to set up and run experiments for you.

### Steps

1. Install Oumi with `uv pip install oumi`
2. Set up the oumi-mcp MCP server for Claude or Cursor, following the Oumi docs
3. Ask the agent to pick a recipe and adapt it to your dataset
4. Review the config it writes before letting it start a training run
5. Use `oumi deploy` later if you want a dedicated inference endpoint on Fireworks or Parasail

### Prompt to paste into your agent

```text
Set up the oumi-ai/oumi oumi-mcp MCP server in <Claude or Cursor>. Then pick a recipe for fine-tuning <model> on <dataset>, write the config, and show it to me before running anything.
```

### Caveat

The README mentions oumi-mcp but leaves setup to the docs.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/oumi-ai-oumi/drive-oumi-from-claude-or-cursor/