TRAIN A MODEL

Requires an authenticated session with an active project. See the quickstart guide for more info. This example uses the banking77 example dataset.

Upload the training data

oumi-cli datasets upload ./banking77_train_basic.jsonl --display-name "banking77-train"

Data must be in JSONL format. Add --dry-run to verify compatibility and view metadata without uploading.

Note the dataset ID it returns.

Write a recipe json

Set DATASET_ID to the ID from the previous step, then paste the whole block (lora finetunes Qwen3-8b on the banking 77 dataset):

DATASET_ID=1

cat > recipe.json <<EOF
{
  "displayName": "banking77-intent-classifier",
  "recipeConfig": {
    "type": "train",
    "trainingConfig": {
      "model": { "modelName": "Qwen/Qwen3-8B" },
      "data": { "train": { "datasets": [{ "datasetId": $DATASET_ID }] } },
      "peft": { "peftMethod": "lora" }
    }
  }
}
EOF

Create the recipe

oumi-cli recipes create --type training --input-json-file recipe.json

Returns the recipe ID you pass to train.

Launch the run

Training is a long running operation which can take 1hr+ depending on the datatset and model. Billing charges apply. For more info see our pricing page.

oumi-cli models train --name banking77-intent-classifier --recipe <RECIPE_ID>

Blocks until training finishes, then prints the model ID, model version ID, and operation ID.

The model weights can be exported with the following command. (Note that model exports require a pro plan and are limited to 1 per month):

oumi-cli models download <MODEL_ID> --out ./weights

NEXT STEPS

Evaluate a model

Score it and find where it fails.

Deploy a model

Serve it behind an inference endpoint.

⌘I

Getting started

Basics

Workflows

Command reference

TRAIN A MODEL

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Evaluate a model

Deploy a model

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Evaluate a model

Deploy a model

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