> ## Documentation Index
> Fetch the complete documentation index at: https://arkor-92aeef0e-eng-615.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# createTrainer

> Define a fine-tuning run.

# `createTrainer`

`createTrainer` is where you describe a fine-tuning run: which base model, which dataset, what knobs. The result is a `Trainer` that `arkor start` (and Studio's **Run training** button) drives.

```ts theme={null}
import { createTrainer } from "arkor";

export const trainer = createTrainer({
  name: "support-bot-v1",
  model: "unsloth/gemma-4-E4B-it",
  dataset: { type: "huggingface", name: "arkorlab/triage-demo" },
  lora: { r: 16, alpha: 16 },
  maxSteps: 100,
});
```

## The fields you reach for first

* `name`: shows up in Studio and in cloud-side logs. Pick something specific.
* `model`: the base open-weight model. Templates use `gemma-4-E4B-it`. See [Supported models](/models).
* `dataset`: where the training data lives. See [`DatasetSource`](/guides/sdk/dataset).
* `lora`: LoRA / QLoRA knobs. `r: 16, alpha: 16` is a fine default; omit to take the backend default.
* `maxSteps` or `numTrainEpochs`: cap how long the run goes.
* `callbacks`: see [Callbacks](/guides/sdk/callbacks).

## Try it without a real run

`dryRun: true` tells the backend to truncate the dataset and cap steps so a run finishes in a couple of minutes while still exercising every stage of the pipeline. Useful when wiring up callbacks for the first time.

```ts theme={null}
createTrainer({
  name: "smoke",
  model: "unsloth/gemma-4-E4B-it",
  dataset: { type: "huggingface", name: "arkorlab/triage-demo" },
  dryRun: true,
});
```

## Reference

For the full `TrainerInput` shape, every typed optional field, `LoraConfig`, the unstable forwarded fields (`warmupSteps`, `loggingSteps`, `saveSteps`, `evalSteps`, etc.), and the multi-trainer roadmap note, see the [`createTrainer` reference](/sdk/create-trainer).