Create Customization Target#

Prerequisites#

Before you can create a customization target, make sure that you have:

  • Access to the NeMo Customizer service

  • The address of the base model you want to customize (model_uri)

    • If using a HuggingFace-style (hf://) model_uri, you must provide a HuggingFace endpoint (such as https://huggingface.co); if not specified, the Data Store’s HF-compatible endpoint is used (http://<datastore-host>/v1/hf)

  • Review the customization target value reference guide to obtain the required values for the model you want to use.

Options#

You can create a customization target in the following ways.

API#

  1. Submit a POST request to /v1/customization/targets.

    curl -X POST \
  "${CUSTOMIZER_SERVICE_URL}/v1/customization/targets" \
  -H 'accept: application/json' \
  -H 'Content-Type: application/json' \
  -d '{
      "name": "llama-3.1-8b-instruct@2.0",
      "namespace": "meta",
      "description": "Customization target for Meta Llama 3.1 8B",
      "enabled": true,
      "base_model": "meta/llama-3.1-8b",
      "model_path": "llama-3_1-8b-instruct_2_0",
      "model_uri": "ngc://nvidia/nemo/llama-3_1-8b:2.0",
      "hf_endpoint": "<your HF endpoint>",
      "tokenizer": {},
      "num_parameters": 123456789,
      "precision": "bf16-mixed",
      "project": "urn:your-project-id"
      }' | jq

  2. Review the response.

    Example Response
    {
 "id": "cust-target-abc123",
 "name": "llama-3.1-8b-instruct@2.0",
 "namespace": "meta",
 "description": "Customization target for Meta Llama 3.1 8B",
 "enabled": true,
 "base_model": "meta/llama-3.1-8b",
 "model_path": "llama-3_1-8b-instruct_2_0",
 "model_uri": "ngc://nvidia/nemo/llama-3_1-8b:2.0",
 "hf_endpoint": "<your HF endpoint>",
 "tokenizer": {},
 "num_parameters": 123456789,
 "precision": "bf16-mixed",
 "status": "created",
 "ownership": {},
 "custom_fields": {},
 "created_at": "2024-05-08T12:00:00Z",
 "updated_at": "2024-05-08T12:00:00Z"
 }

    Tip

    Model files are large and can take time to download from the model_uri. You can check the status of the download by getting target details and reviewing the status.

    Note

    If you’re re-creating a target with the same name after deleting it, ensure that the model download job for the deleted target doesn’t already exist. The model download job has a TTL of 10 minutes and is automatically removed after this period expires.

On this page