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Fine-tuning adapts an open base model to your own data. Valkyrie runs the training for you on an on-demand GPU using a managed Unsloth pipeline, you provide the dataset and the base model, and Valkyrie handles the rest.

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Before you start

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Create a fine-tuning job

1

Open Fine-Tuning

Go to Fine-Tuning in the dashboard and start a new job.
2

Choose a base model and dataset

Select the open base model to fine-tune and the dataset to train on.
3

Launch

Start the job. Valkyrie schedules it, provisions a GPU, generates the training script, and runs it. If a preferred GPU type isn’t available, provisioning retries with fallbacks.

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Track progress

A job moves through a series of states as it runs:
You can watch progress from the Fine-Tuning screen, and stream logs from the job (also available through the MCP logging tools). Training progress is parsed from the run’s logs.

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Outputs

When a job completes, its outputs (the trained weights or adapters) are stored for you. From the completed job you can:
  • List outputs and download them with temporary signed URLs.
  • Deploy the fine-tuned model directly, see Deploy a model.

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If a job fails

Fine-tuning runs on ephemeral cloud GPUs, so transient infrastructure issues can occur. Valkyrie lets you retry or recover a failed job without recreating it from scratch. If a job stays stuck, retrying usually reschedules it onto fresh hardware.
Fine-tuning has a maximum run time. Very large datasets or long training configurations should be sized with that limit in mind.

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Deploy what you trained

Deploy a fine-tuned model

Turn a completed job into a live, OpenAI-compatible endpoint.