Create a Model FileSet | NVIDIA NeMo Platform

Create a FileSet containing your base model checkpoint before creating a Model Entity.

Prerequisites

Obtained the base URL of your NeMo Platform.
For gated or private Hugging Face models: Created a secret with your HF token. Refer to Manage Secrets.
Set the NMP_BASE_URL environment variable.

export NMP_BASE_URL="https://your-nemo-platform-url"

From Hugging Face Hub

The most common method is downloading directly from Hugging Face. The example below uses Qwen/Qwen3-1.7B, a public model that requires no token:

import os
from nemo_platform import ConflictError, NeMoPlatform
from nemo_platform.types.files import HuggingfaceStorageConfigParam
client = NeMoPlatform(
    base_url=os.environ.get("NMP_BASE_URL", "http://localhost:8080"),
    workspace="default",
)
HF_REPO_ID = "Qwen/Qwen3-1.7B"
MODEL_NAME = "qwen3-1.7b"
# Create FileSet from Hugging Face
try:
    fileset = client.files.filesets.create(
        workspace="default",
        name=MODEL_NAME,
        description="Qwen3 1.7B from Hugging Face",
        purpose="model",
        storage=HuggingfaceStorageConfigParam(
            type="huggingface",
            repo_id=HF_REPO_ID,
            repo_type="model",
        ),
    )
    print(f"Created FileSet: {fileset.name}")
except ConflictError:
    print(f"FileSet '{MODEL_NAME}' already exists, retrieving...")
    fileset = client.files.filesets.retrieve(workspace="default", name=MODEL_NAME)
print(f"FileSet ready: {fileset.name}")

Gated models (such as Llama) require a Hugging Face token. To use one:

Accept the model license on the Hugging Face model page.
Create a Hugging Face token with read access.
Store the token as a secret in the platform (see Manage Secrets), then pass it as token_secret in the storage config:

storage=HuggingfaceStorageConfigParam(
    type="huggingface",
    repo_id="meta-llama/Llama-3.2-1B-Instruct",
    repo_type="model",
    token_secret="my-hf-token",
)

From NGC

For models from NVIDIA NGC:

import os
from nemo_platform import ConflictError, NeMoPlatform
from nemo_platform.types.files import NGCStorageConfigParam
client = NeMoPlatform(
    base_url=os.environ.get("NMP_BASE_URL", "http://localhost:8080"),
    workspace="default",
)
MODEL_NAME = "nemotron-mini-4b"
NGC_TARGET = "nemotron-mini-4b-instruct"
NGC_ORG = "nvidia"
NGC_TEAM = "nemo"
NGC_VERSION = "1.0"
NGC_API_KEY_SECRET = "my-ngc-key"
NGC_API_KEY = os.environ.get("NGC_API_KEY")
if not NGC_API_KEY:
    raise RuntimeError("Set NGC_API_KEY before creating the NGC secret.")
# First, create a secret for your NGC API key (if not already created)
try:
    ngc_secret = client.secrets.create(
        name=NGC_API_KEY_SECRET,
        workspace="default",
        value=NGC_API_KEY,
    )
    print(f"Created secret: {NGC_API_KEY_SECRET}")
except ConflictError:
    print(f"Secret '{NGC_API_KEY_SECRET}' already exists, continuing...")
    ngc_secret = client.secrets.retrieve(name=NGC_API_KEY_SECRET, workspace="default")
# Create FileSet from NGC
try:
    fileset = client.files.filesets.create(
        workspace="default",
        name=MODEL_NAME,
        description="Nemotron Mini 4B from NGC",
        purpose="model",
        storage=NGCStorageConfigParam(
            type="ngc",
            org=NGC_ORG,
            team=NGC_TEAM,
            target=NGC_TARGET,  # NGC asset name
            target_type="resource",
            version=NGC_VERSION,
            api_key_secret=ngc_secret.name,
        ),
    )
    print(f"Created FileSet: {fileset.name}")
except ConflictError:
    print("FileSet already exists, retrieving...")
    fileset = client.files.filesets.retrieve(workspace="default", name=MODEL_NAME)

Check FileSet Status

Files are downloaded in the background after you create a FileSet. Check the status:

# List files in the FileSet
response = client.files.list(
    workspace="default",
    fileset="qwen3-1.7b",
)
files = response.data
print(f"Files in FileSet ({len(files)} total):")
for f in files[:10]:  # Show first 10
    print(f" - {f.path} ({f.size:,} bytes)")

Example Response

{
  "data": [
    {
      "file_ref": "/v2/workspaces/default/filesets/qwen3-1.7b/-/config.json",
      "path": "config.json",
      "size": 1234,
      "cache_status": null
    },
    {
      "file_ref": "/v2/workspaces/default/filesets/qwen3-1.7b/-/model.safetensors",
      "path": "model.safetensors",
      "size": 2400000000,
      "cache_status": null
    },
    {
      "file_ref": "/v2/workspaces/default/filesets/qwen3-1.7b/-/tokenizer.json",
      "path": "tokenizer.json",
      "size": 9085657,
      "cache_status": null
    },
    {
      "file_ref": "/v2/workspaces/default/filesets/qwen3-1.7b/-/tokenizer_config.json",
      "path": "tokenizer_config.json",
      "size": 901,
      "cache_status": null
    }
  ]
}

Next Steps

Proceed to create a Model Entity.