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A sampler checkpoint deploys to a dedicated Baseten inference deployment that serves the base model with your LoRA adapter loaded. Deployment works whether or not the training session is still running: checkpoints outlive the session, so you can shut the trainer and sampler down first and deploy later. Deploying needs an hf_access_token in workspace secrets, because the deployment downloads the base weights from Hugging Face.

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Deploy from the CLI

Run truss loops checkpoints deploy with the checkpoint’s globally unique id (the id field from listing checkpoints, not the checkpoint name): 
uvx truss loops checkpoints deploy --checkpoint-ids <id>
The --checkpoint-ids flag skips the checkpoint picker, but the command still prompts for a model name, GPU type, GPU count, and the Hugging Face secret name (default hf_access_token). A successful deploy prints the IDs you need to call the model: 
Successfully created deployment: deployment-1
Model ID: wnpkzp03
Deployment ID: qrpg2r0
Deployment succeeded.
Set the model parameter on each request to the Loops checkpoint name (e.g. step-100).
The deployment downloads base weights and starts the inference server, so several minutes pass before it reaches ACTIVE. To inspect the generated Truss config without deploying, pass --dry-run.

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Deploy from the dashboard

Every Loop has a page in the dashboard at app.baseten.co/training/loop/<run_id>, reachable from the Training tab. Its Checkpoints table lists each saved checkpoint with its bt:// path, type, and size, and is where you deploy one; the flow matches deploying job checkpoints.

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Call the deployed model

Call the deployment’s OpenAI-compatible chat completions route. The URL includes the deployment ID, and model is the checkpoint name (checkpoint_id), not its globally unique id: 
curl -X POST "https://model-<model_id>.api.baseten.co/deployment/<deployment_id>/sync/v1/chat/completions" \
  -H "Authorization: Bearer $BASETEN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "step-1",
    "messages": [{"role": "user", "content": "What is the capital of France?"}]
  }'
For request options, streaming, and client libraries, see calling your model.

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Delete the deployment

The deployment bills for its GPU while it’s live. Delete it with DELETE /v1/models/{model_id} when you’re done: 
curl -X DELETE "https://api.baseten.co/v1/models/<model_id>" \
  -H "Authorization: Bearer $BASETEN_API_KEY"
Deleting the deployment doesn’t touch the checkpoint. You can redeploy it any time.

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Next steps

  • Train on a dataset: Move from the quickstart’s single example to a real training loop.
  • Loops concepts: How checkpoints relate to sessions, trainers, and samplers.