---
title: "createTrainer"
description: "Define a fine-tuning run."
---

`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
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](/docs/framework/models).
- `dataset`: where the training data lives. See [`DatasetSource`](/docs/framework/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](/docs/framework/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
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](/docs/framework/sdk/create-trainer).