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A custom model is a set of weights you own, stored on Veri and deployable from a single named entry. Today you build your library by saving a completed Veri training job: the checkpoint is already on Veri storage, so saving it is a metadata-only step that names it and makes it deployable. No weights are copied. Your custom models live in the model library in the dashboard. From there you deploy a model or copy its ID for the API.
Hugging Face import and direct upload are not available yet. For now, a custom model comes from a completed Veri training job. To serve a Hugging Face repo directly, deploy it with source="huggingface" (see Deployments).

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Save a training job as a model

When a training job completes, its checkpoint is already on Veri-managed storage. Save it to give it a name and make it reusable and deployable.
Saving is idempotent per job: saving the same completed job again returns the existing model rather than creating a duplicate. The saved model reuses the completed checkpoint already in Veri storage; it does not copy the weights. Only completed jobs that produced a Hugging Face checkpoint can be saved. Jobs that are still running, failed, or ran a custom script (which produces arbitrary output rather than a standard checkpoint) cannot be saved as a model. You can also keep working with the raw checkpoint directly:
See Training for the full training flow.

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Manage your library

Renaming changes only the library label. Removing a model from the library deletes only the registry entry: it never deletes the training checkpoint, and it does not interrupt a deployment that is already serving that checkpoint. A model with an active deployment cannot be removed until you stop the deployment.

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Model states

Saving a completed training job produces a ready model directly.

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Size limits

A custom model must fit on a single GPU node, the same ceiling as a deployment. Pick a GPU large enough to hold the weights when you deploy.

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Deploy a custom model

Once a model is ready, deploy it by ID with source="custom_model". Custom models always serve from Veri storage, so cold starts skip any external download step.
The deployment records both the saved model and its source checkpoint, so you can always trace what a running endpoint is serving. Because the checkpoint location is captured when the deployment is created, later renaming or removing the model from your library does not affect a running deployment. See Deployments for the serving lifecycle and the OpenAI-compatible chat surface.

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Getting started without a model

If your library is empty, you have not saved a trained model yet. Train a model, then save the resulting checkpoint with veri models save. To deploy an open-source model without training, deploy a Hugging Face repo directly from Deployments.

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Where to go next

Deploy a model

Spin up a deployment and chat with it in ~10 minutes.

Train your own model

Train a checkpoint, then save it to your library.

Hosting & GPU sizing

Pick the right GPU for your model size.

CLI deployment commands

Manage deployments from the terminal.