ZeticMLangeModel

The ZeticMLangeModel class is the primary interface for running on-device AI inference on iOS. It handles model downloading, Neural Engine context initialization, and hardware-accelerated execution through a single unified Swift API.

Import

import ZeticMLange

Initializer

`ZeticMLangeModel(personalKey:name:)`

Creates a new model instance, downloading the optimized binary if needed and initializing the NPU execution context.

init(personalKey: String, name: String) throws

Parameter	Type	Description
`personalKey`	`String`	Your personal authentication key from the Melange Dashboard.
`name`	`String`	The unique identifier for your compiled model (e.g., `"Steve/YOLOv11_comparison"`).

Throws: An error if the model cannot be downloaded or the Neural Engine context fails to initialize.

let model = try ZeticMLangeModel(personalKey: "YOUR_PERSONAL_KEY", name: "YOUR_MODEL_KEY")

The initializer performs a network call on first use to download the model binary. The binary is cached locally after the first download, so subsequent initializations are fast.

`ZeticMLangeModel(personalKey:name:version:)`

Creates a new model instance targeting a specific model version.

init(personalKey: String, name: String, version: Int) throws

Parameter	Type	Description
`personalKey`	`String`	Your personal authentication key.
`name`	`String`	The unique identifier for your compiled model.
`version`	`Int`	The specific version of the model to load.

let model = try ZeticMLangeModel(personalKey: PERSONAL_KEY, name: MODEL_NAME, version: VERSION)

`ZeticMLangeModel(personalKey:name:modelMode:)`

Creates a new model instance with a specific inference mode.

init(personalKey: String, name: String, modelMode: ModelMode) throws

Parameter	Type	Description
`personalKey`	`String`	Your personal authentication key.
`name`	`String`	The unique identifier for your compiled model.
`modelMode`	`ModelMode`	Inference strategy: `RUN_AUTO`, `RUN_SPEED`, or `RUN_ACCURACY`. See Enums.

let model = try ZeticMLangeModel(personalKey: PERSONAL_KEY, name: MODEL_NAME, modelMode: RUN_SPEED)

Methods

`run(_:)`

Executes inference on the loaded model using the provided input tensors.

func run(_ inputs: [Tensor]) throws -> [Tensor]

Parameter	Type	Description
`inputs`	`[Tensor]`	An array of input tensors matching the model's expected input shapes and data types.

Returns: [Tensor]: The model's output tensors.

Throws: An error if input shapes do not match the model's expected inputs, or if inference execution fails.

let outputs = try model.run(inputs)

Full Working Example

import ZeticMLange

class ViewController: UIViewController {
    override func viewDidLoad() {
        super.viewDidLoad()

        do {
            // (1) Load model
            // Downloads the optimized binary on first run, then caches locally
            let model = try ZeticMLangeModel(personalKey: PERSONAL_KEY, name: "Steve/YOLOv11_comparison")

            // (2) Prepare model inputs
            // Ensure input shapes match your model's requirement (e.g., Float32 arrays)
            let inputs: [Tensor] = [] // Prepare your inputs

            // (3) Run Inference
            // Executes the fully automated hardware graph.
            // No manual delegate configuration or memory syncing required.
            let outputs = try model.run(inputs)

            // (4) Process outputs
            // outputs is a [Tensor] containing the model's results
            for output in outputs {
                // Process each output tensor
            }
        } catch {
            print("Melange error: \(error)")
        }
    }
}

Always ensure your input tensor shapes exactly match what the model expects. A shape mismatch will throw an error. Check the model's input specification on the Melange Dashboard.

Swift Package Manager Setup

Add the Melange package to your Xcode project:

Open your project in Xcode
Go to File then Add Package Dependencies
Enter the repository URL: https://github.com/zetic-ai/ZeticMLangeiOS
Click Add Package
Select ZeticMLange and link it to your app target