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, please contact your admin to verify that the model download job does not exist for the deleted target. If it does, admin needs to update ttlSecondsAfterFinished for modelDownloader to 30 seconds or less (default TTL is set to 7200 seconds) and delete the job before proceeding.

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