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

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

Short answer: 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. All steps.

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

The problemFine-tuning tutorials usually stop at training, leaving evaluation and inference as separate projects.

The outcomeA trained 135M model, an evaluation run and an interactive chat, all from YAML recipes.

What you need first

  • 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

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 for your agent

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.

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Using the Deduped MCP, get the setup steps for oumi-ai/oumi and "Train, evaluate and chat with a model from recipes"

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