Manage Model Entities for Customization

Before running a customization job, you need to set up a Model Entity that points to your base model checkpoint. This section covers creating the required FileSet and Model Entity.

Task Guides

Create a Model FileSet

Create a FileSet containing your base model checkpoint from HuggingFace, NGC, or local storage.

Create a Model FileSet

Create a Model Entity

Create a Model Entity that references your FileSet and enables customization.

Create a Model Entity

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Key Concepts

What is a FileSet?

A FileSet is a collection of files managed by the platform. For customization, you create a FileSet containing:

• Model weights (.safetensors, .bin, or .nemo files)

• Model configuration (config.json)

• Tokenizer files (tokenizer.json, tokenizer_config.json, and so on)

FileSets can be populated from:

• HuggingFace Hub - Download directly from HF repositories

• NGC - Download from NVIDIA NGC catalogs

• Local upload - Upload files from your local machine

What is a Model Entity?

A Model Entity is the platform’s representation of a model. It contains:

• FileSet reference - Points to where the model files are stored

• Model Spec - Auto-populated metadata about the model architecture

• Adapters - LoRA or other adapters attached to this model (populated after training)

• Base model link - For fine-tuned models, links back to the parent

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Quick Start Example

Complete example of setting up a model for customization:

Tip

HuggingFace Token: If downloading from a gated HuggingFace repository (like Llama models), you will need to create a secret containing your HuggingFace API token first. Refer to Manage Secrets for instructions.

import os
import time
from nemo_platform import NeMoPlatform
from nemo_platform._exceptions import ConflictError
from nemo_platform.types.files import HuggingfaceStorageConfigParam

client = NeMoPlatform(
    base_url=os.environ.get("NMP_BASE_URL", "http://localhost:8080"),
    workspace="default",
)

# Step 1: Create FileSet from HuggingFace
try:
    fileset = client.files.filesets.create(
        workspace="default",
        name="llama-3-2-1b",
        description="Llama 3.2 1B base model from HuggingFace",
        storage=HuggingfaceStorageConfigParam(
            type="huggingface",
            repo_id="meta-llama/Llama-3.2-1B-Instruct",
            repo_type="model",
            token_secret="my-hf-token"  # Refer to Manage Secrets documentation
        )
    )
    print(f"Created FileSet: {fileset.name}")
except ConflictError:
    print("FileSet already exists, retrieving...")
    fileset = client.files.filesets.retrieve(workspace="default", name="llama-3-2-1b")

# Step 2: Create Model Entity
try:
    model = client.models.create(
        workspace="default",
        name="llama-3-2-1b",
        fileset="default/llama-3-2-1b",  # Reference to the FileSet
        description="Llama 3.2 1B base model for customization"
    )
    print(f"Created Model Entity: {model.name}")
except ConflictError:
    print("Model Entity already exists, retrieving...")
    model = client.models.retrieve(workspace="default", name="llama-3-2-1b")

# Step 3: Wait for ModelSpec to be auto-populated
print("Waiting for model spec to be populated...")
while not model.spec:
    time.sleep(5)
    model = client.models.retrieve(workspace="default", name="llama-3-2-1b")

print(f"Model ready!")
print(f"  Architecture: {model.spec.family}")
print(f"  Parameters: {model.spec.base_num_parameters:,}")
print(f"  Layers: {model.spec.num_layers}")

After the Model Entity is ready (has a spec), you can use it in customization jobs with model: "default/llama-3-2-1b".

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