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Version: v0.19.1

Deployments

Creating a New Deployment​

You can deploy a model from two entry points:

  • From the Deployments page by clicking New Deployment
  • From a completed Experiment by clicking the Deploy button

From the Deployments Page​

When you click New Deployment, you’ll be prompted to provide a deployment name. If you don’t choose a name, one will be generated for you.

You can deploy:

  • A model from a completed Experiment
  • A public model from Hugging Face

Deploying from a Hugging Face Model​

To deploy a Hugging Face model, select the Hugging Face Model option and enter the model ID in the format organization/model-name. You can either enter the ID manually or use the dropdown to browse available models.

Deploying from an Experiment​

To deploy a fine-tuned model from an experiment:

  1. Select the Experiment option
  2. Use the search box or scroll to find and select your desired experiment
  3. Click Create Deployment

No further configuration is required. The deployment process will begin automatically.

Deployment Status and Chat​

Once deployment starts, the Status Overview panel will show the current state. While it’s initializing, the status will read Starting.

Once the deployment is live:

  • The status will read as Running
  • You’ll see an endpoint URL you can use in downstream applications
  • You can interact with the model directly in the Chat with the Endpoint section

If the model was fine-tuned for classification, you can send inputs and get classification responses directly from the chat window.

Deployment Limitations

Not all problem types support deployment. Currently, multimodal generation, image classification, and object detection models cannot be deployed through the standard deployment pipeline (e.g. DETR and other model architectures are not compatible with the vLLM inference engine used for deployments).

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