ZeticMLangeModel

This page reflects ZeticMLange Android 1.11.0.

ZeticMLangeModel loads a general on-device model from the Melange registry and runs tensor inference on Android.

Import

import com.zeticai.mlange.core.model.ZeticMLangeModel

Constructor

ZeticMLangeModel(
    context: Context,
    personalKey: String,
    name: String,
    version: Int? = null,
    modelMode: ModelMode = ModelMode.RUN_AUTO,
    onDownload: ((Float) -> Unit)? = null,
    cacheHandlingPolicy: ModelCacheHandlingPolicy = ModelCacheHandlingPolicy.REMOVE_OVERLAPPING,
)

Parameter	Type	Default	Description
`context`	`Context`	-	Android context used for cache and file access.
`personalKey`	`String`	-	Personal key for accessing the model.
`name`	`String`	-	Model name in `account_name/project_name` format.
`version`	`Int?`	`null`	Model version. `null` loads the latest version.
`modelMode`	`ModelMode`	`RUN_AUTO`	Backend selection strategy.
`onDownload`	`((Float) -> Unit)?`	`null`	Download progress callback from `0.0` to `1.0`.
`cacheHandlingPolicy`	`ModelCacheHandlingPolicy`	`REMOVE_OVERLAPPING`	Managed artifact cache cleanup policy.

val model = ZeticMLangeModel(
    context = context,
    personalKey = PERSONAL_KEY,
    name = "account_name/project_name",
    modelMode = ModelMode.RUN_AUTO,
)

`run(inputs)`

Runs inference with tensors matching the model input order.

fun run(inputs: Array<Tensor> = emptyArray()): Array<Tensor>

Parameter	Type	Description
`inputs`	`Array<Tensor>`	Input tensors matching the model's expected shapes and data types.

Returns: output tensors in model output order.

val outputs = model.run(arrayOf(inputTensor))
val firstOutput = outputs[0]

Background Downloads

Schedule a durable model download without constructing a runtime instance. Keep the returned handle to observe or stop the job after the app moves to the background.

fun downloadInBackground(
    context: Context,
    personalKey: String,
    name: String,
    version: Int? = null,
    modelMode: ModelMode = ModelMode.RUN_AUTO,
    cacheHandlingPolicy: ModelCacheHandlingPolicy = ModelCacheHandlingPolicy.KEEP_EXISTING,
): BackgroundDownloadHandle

fun getBackgroundDownloadStatus(
    context: Context,
    handle: BackgroundDownloadHandle,
): BackgroundDownloadStatus

fun stopBackgroundDownload(
    context: Context,
    handle: BackgroundDownloadHandle,
): BackgroundDownloadStopResult

fun removeDownloadedModel(
    context: Context,
    name: String,
    version: Int? = null,
): ModelRemovalResult

val handle = ZeticMLangeModel.downloadInBackground(
    context = context,
    personalKey = PERSONAL_KEY,
    name = "account_name/project_name",
)

val status = ZeticMLangeModel.getBackgroundDownloadStatus(context, handle)
val stopResult = ZeticMLangeModel.stopBackgroundDownload(context, handle)

val removal = ZeticMLangeModel.removeDownloadedModel(
    context = context,
    name = "account_name/project_name",
)
if (removal.isInUse) {
    // Close active instances, then retry removal.
}

Stopping preserves an already installed model. removeDownloadedModel stops active downloads for the selected model before removing its managed artifacts. See Cache Management for states and result values.

Lifecycle

val isClosed: Boolean
fun close()

Call close() when the model is no longer needed.

model.close()

ZeticMLangeModel

Import

Constructor

run(inputs)

Background Downloads

Lifecycle

Related

On this page

`run(inputs)`