Mobile ML: iOS and Android

Mobile ML Frameworks

iOS

Android

Core ML Integration

import CoreML

// Load model
let model = try! MyModel()

// Run inference
let input = MyModelInput(text: "Hello world")
let output = try! model.prediction(input: input)
print(output.label)

TensorFlow Lite Android

import org.tensorflow.lite.Interpreter

val interpreter = Interpreter(loadModelFile())

// Prepare input
val input = floatArrayOf(...)
val output = Array(1) { FloatArray(numClasses) }

// Run inference
interpreter.run(input, output)

Converting Models

To Core ML

import coremltools as ct

# From PyTorch
traced_model = torch.jit.trace(model, example_input)
mlmodel = ct.convert(traced_model, inputs=[ct.TensorType(name="input", shape=(1, 512))])
mlmodel.save("model.mlpackage")

To TensorFlow Lite

import tensorflow as tf

converter = tf.lite.TFLiteConverter.from_keras_model(model)
converter.optimizations = [tf.lite.Optimize.DEFAULT]
tflite_model = converter.convert()

with open("model.tflite", "wb") as f:
    f.write(tflite_model)

On-Device LLMs

LLM on iOS

// Using llama.cpp Swift bindings
let llama = LlamaModel(path: "model.gguf")
let response = llama.generate("Hello, how are you?", maxTokens: 100)

LLM on Android

// Using llama.android
val llama = LlamaAndroid()
llama.loadModel("/sdcard/model.gguf")
val response = llama.generate("Tell me a joke")

Size Constraints

Best Practices

• Use quantized models (INT8/INT4)
• Download models on first launch
• Batch operations for efficiency
• Monitor battery impact
• Test on diverse devices