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 cover the same platform through the TypeScript SDK and CLI.
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 job detail page shows:
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.Failures surface the trainer's error message in a banner.
During the run the trainer uploads mid-run checkpoints; on success it uploads the final adapter. Both become usable in two places:
*.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.