---
title: "Training jobs"
description: "Fine-tune an open-weight model from the dashboard: creating jobs, monitoring runs, and using the results."
---

A training job fine-tunes an open-weight base model with LoRA on Arkor's managed GPUs. Jobs live under a project's **Jobs** tab. If you prefer code over forms, the [framework docs](/docs/framework) cover the same platform through the TypeScript SDK and CLI.

## Creating a job

**Jobs → New Job** takes:

| Field | Notes |
| ----- | ----- |
| Job name | Free text, for the job list |
| Model | Hugging Face repo id of the base model |
| Dataset | Hugging Face dataset id |
| Dataset format | How the dataset's columns map to training text: Alpaca (`instruction` / `input` / `output`), ChatML (a `messages` list), ShareGPT, prompt-completion, plain text, or pre-tokenized |
| Max samples | Optional cap on dataset rows; empty trains on all rows |
| Max steps | Optional cap on optimizer steps; empty trains by epochs |
| Learning rate | Defaults to `0.0002` |

Submitting queues the job. Arkor picks a GPU from its provider pool and dispatches the run; you do not choose or manage GPUs.

![The new job form](/images/cloud/job-new-light.png "www.arkor.ai/oddphin-org/support-bot/jobs/new")
![The new job form](/images/cloud/job-new-dark.png "www.arkor.ai/oddphin-org/support-bot/jobs/new")

## Monitoring a run

The job detail page shows:

- **Status** — `queued`, `running`, `preempted`, `completed`, `failed`, or `cancelled`, with a Cancel button while queued, running, or preempted. `preempted` means the GPU the job was training on was reclaimed mid-run; the job is not lost — it waits to be picked up again and resumes from its last checkpoint automatically.
- **Latest metrics** — the step, loss, and epoch from the newest trainer event at load time; reload the page for fresh numbers.
- **Training events** — the event log as a table: per-step log lines with step, loss, and time, plus lifecycle events.
- **Configuration** — the job id, provider, timestamps, and the full submitted config as JSON.

Failures surface the trainer's error message in a banner.

![A completed job's detail page with metrics and the event log](/images/cloud/job-detail-light.png "www.arkor.ai/oddphin-org/support-bot/jobs/job_2WZDQDJuqkND1p6pbZH7h")
![A completed job's detail page with metrics and the event log](/images/cloud/job-detail-dark.png "www.arkor.ai/oddphin-org/support-bot/jobs/job_2WZDQDJuqkND1p6pbZH7h")

## Checkpoints and adapters

During the run the trainer uploads mid-run **checkpoints**; on success it uploads the **final adapter**. Both become usable in two places:

- The [project Playground](/docs/cloud/playground) can chat with the final adapter or any checkpoint of a completed job.
- An [endpoint](/docs/cloud/endpoints) can serve the final adapter or a specific checkpoint at a stable `*.arkor.app` URL. The endpoint's target can be swapped in place through the SDK's deployments API, so you can promote a better checkpoint without changing the URL your app calls.

A completed job's detail page links straight to the Playground preloaded with that job.