Skip to main content
This feature is in Private Preview. For access or more information, contact us or reach out to your account representative.
Upload fine-tuned models from your S3 bucket, track multiple versions, and create deployments behind your dedicated endpoint. For detailed endpoint documentation, see the Management API Reference.

​
Key Concepts

  • Model Architecture: The serving compatibility class for a model, such as gpt-oss-120b or zai-glm-4.7. Each endpoint supports a specific architecture, and custom weights must be compatible with that architecture.
  • Model Version: An immutable snapshot of model weights. Each upload creates a new version identified by an auto-incrementing integer, such as 1, 2, or 3. Versions can also have aliases like production or v1-stable.
  • Deployment: A running model version and its serving configuration. A deployment belongs to an endpoint and can be replaced without changing the endpoint ID used by applications.
  • Endpoint: A stable inference target identified by a unique endpoint ID, such as my-org-gpt-oss-120b. The endpoint routes requests to one or more deployments.
  • Replica: One running copy of a deployment. Replica count determines the capacity available to the deployment.
The inference API uses the OpenAI-compatible model field as a target selector. For Dedicated Inference, pass the endpoint ID in this field. In the Management API, model refers to a model version resource name where noted.

​
Typical Workflow

  1. Upload model weights — Push your fine-tuned weights from S3 to Cerebras. The upload is asynchronous; you’ll receive a version ID to track progress.
  2. Check upload status — Poll the version status until the sync completes.
  3. Create a deployment: Once the upload is complete, deploy the version behind your endpoint.
  4. Run inference: Make requests to your endpoint using the endpoint ID as the model field.
  5. Iterate: Upload new versions as you fine-tune, assign aliases like production to track releases, and replace deployments without changing the endpoint ID.

​
Authentication

The Management API uses a separate API key from the standard inference API. You can find your Management API key under Management API keys on the API keys page in the Cerebras Cloud console.

​
S3 Bucket Setup

Before uploading model weights, you need an S3 bucket configured with cross-account access to Cerebras. Your Cerebras representative will provide specific instructions, but the bucket policy must grant cross-account access:
Replace <your-bucket-name> with your S3 bucket name. The IAM role ARN (<cerebras-provided-iam-role-arn>) will be provided by Cerebras and enables secure cross-account access to your model weights.