ModelPackage

Created: 2026-03-18 · Last updated: 2026-03-18

A dict-like collection of named ir.Model objects forming a complete model.

from mobius import ModelPackage

Class Signature

class ModelPackage(UserDict[str, ir.Model]):
    config: object | None

    def __init__(
        self,
        models: dict[str, ir.Model] | None = None,
        config: object | None = None,
    ) -> None: ...

Methods

save()

Save all component models to a directory.

def save(
    self,
    directory: str,
    *,
    external_data: str = "onnx",
    max_shard_size_bytes: int | None = None,
    components: Callable[[str], bool] | None = None,
    progress_bar: bool = True,
    check_weights: bool = True,
) -> None:

Parameter	Type	Default	Description
directory	str	(required)	Output directory path.
external_data	str	"onnx"	"onnx" or "safetensors" format.
max_shard_size_bytes	int | None	None	Max shard size (safetensors only).
components	Callable | None	None	Predicate to select components to save.
check_weights	bool	True	Verify all initializers have weight data.

load()

Load models from a directory.

@classmethod
def load(cls, directory: str) -> ModelPackage:

apply_weights()

Apply weights from a state dict to all component models.

def apply_weights(
    self,
    state_dict: dict[str, torch.Tensor],
    prefix_map: dict[str, str] | None = None,
) -> None:

Examples

from mobius import build

# Build and save
pkg = build("meta-llama/Llama-3.2-1B")
pkg.save("output/llama/")

# Access individual models
model = pkg["model"]
print(model.graph.name)

# Check components
print(list(pkg.keys()))  # ["model"] for single-model
# ["model", "vision", "embedding"] for VLM

# Load from disk
pkg = ModelPackage.load("output/llama/")

# Save as safetensors
pkg.save("output/llama/", external_data="safetensors")

Output Layout

• Single model: directory/model.onnx + directory/model.onnx.data

• Multi model: directory/{name}/model.onnx for each component