iOS Developer Tools & Code Snippets

import coremltools as ct
import torch
import torchvision.models as models

# 1. Load and export to TorchScript
model = models.resnet50(pretrained=True).eval()
example_input = torch.randn(1, 3, 224, 224)
traced_model = torch.jit.trace(model, example_input)

# 2. Convert to Core ML
mlmodel = ct.convert(
    traced_model,
    inputs=[ct.TensorType(name="input", shape=example_input.shape)],
    # FP16 reduces model size by ~50% with <1% accuracy loss on most models
    minimum_deployment_target=ct.target.iOS16,
    compute_precision=ct.precision.FLOAT16,
)

# 3. Add metadata (shown in Xcode model viewer)
mlmodel.author = "Your Name"
mlmodel.license = "MIT"
mlmodel.short_description = "ResNet50 for image classification"
mlmodel.version = "1.0"

# Save as .mlpackage (preferred over .mlmodel for iOS 16+)
mlmodel.save("ResNet50.mlpackage")
print("Saved. Model size:", mlmodel.get_spec().ByteSize() // 1024, "KB")

MIT — free to use and modify

import coremltools as ct
from coremltools.optimize.coreml import (
    OpLinearQuantizerConfig,
    OptimizationConfig,
    linear_quantize_weights,
)

# Load an existing .mlpackage
model = ct.models.MLModel("MyTransformerModel.mlpackage")

# Configure 4-bit linear quantization
op_config = OpLinearQuantizerConfig(
    mode="linear_symmetric",
    dtype="int4",                    # 4-bit weights
    granularity="per_block",         # better accuracy than per_tensor
    block_size=32,
    weight_threshold=1024            # only quantize ops with weight >= 1024 elements
)

config = OptimizationConfig(global_config=op_config)

# Apply quantization
quantized_model = linear_quantize_weights(model, config=config)

# Verify size reduction
original_size = sum(
    len(val.SerializeToString())
    for val in model.get_spec().neuralNetwork.layers
)
quantized_model.save("MyTransformerModel_int4.mlpackage")

MIT — free to use and modify

import CoreML

/// Generic actor wrapper for any Core ML model.
/// Replace MyModel, MyModelInput, MyModelOutput with your generated types.
actor MLInferenceService<Model, Input, Output> where
    Model: MLModel,
    Input: MLFeatureProvider,
    Output: MLFeatureProvider
{
    private let model: Model
    private let config: MLModelConfiguration

    init(configuration: MLModelConfiguration = .init()) throws {
        self.config = configuration
        self.model = try Model(configuration: configuration)
    }

    /// Thread-safe async prediction.
    /// Caller never needs to dispatch — the actor serializes access.
    func predict(_ input: Input) async throws -> Output {
        // async prediction() available iOS 16+
        // does not block the cooperative thread pool
        return try await model.prediction(from: input) as! Output
    }
}

// Usage: one shared instance per model type
let classifier = try MLInferenceService<SentimentClassifier, SentimentClassifierInput, SentimentClassifierOutput>()

// Call from any context — even multiple concurrent callers
let result = try await classifier.predict(SentimentClassifierInput(text: "Hello world"))

MIT — free to use and modify

import Foundation

/// Bridges a callback-based producer to an AsyncThrowingStream.
/// Use when your token source is not natively async (C libraries, Combine, legacy callbacks).
func makeStream<T>(
    producer: @escaping (@escaping (T) -> Void, @escaping (Error?) -> Void) -> Void
) -> AsyncThrowingStream<T, Error> {
    AsyncThrowingStream { continuation in
        producer(
            { value in continuation.yield(value) },
            { error in
                if let error { continuation.finish(throwing: error) }
                else { continuation.finish() }
            }
        )
    }
}

// Example: bridge llama.cpp C callback to AsyncStream
// (assuming a Swift wrapper that calls your C sampling loop)
func streamLlamaInference(prompt: String) -> AsyncThrowingStream<String, Error> {
    makeStream { yieldToken, finish in
        llamaCppWrapper.generate(
            prompt: prompt,
            onToken: { token in yieldToken(token) },
            onComplete: { error in finish(error) }
        )
    }
}

// Consume in SwiftUI
.task(id: prompt) {
    for try await token in streamLlamaInference(prompt: prompt) {
        output += token
    }
}

MIT — free to use and modify

#!/bin/bash
# Verify entitlements in a signed .app bundle
# Usage: ./check-entitlements.sh MyApp.app MyApp.entitlements

APP="$1"
EXPECTED_ENTITLEMENTS="$2"

if [[ -z "$APP" || -z "$EXPECTED_ENTITLEMENTS" ]]; then
    echo "Usage: $0 <MyApp.app> <MyApp.entitlements>"
    exit 1
fi

echo "=== Embedded entitlements in $APP ==="
codesign -d --entitlements :- "$APP" 2>/dev/null | plutil -convert xml1 -o - - | xmllint --format -

echo ""
echo "=== Expected entitlements (from $EXPECTED_ENTITLEMENTS) ==="
plutil -convert xml1 -o - "$EXPECTED_ENTITLEMENTS" | xmllint --format -

echo ""
echo "=== Diff (empty = no mismatches) ==="
diff <(codesign -d --entitlements :- "$APP" 2>/dev/null | plutil -convert xml1 -o - -)      <(plutil -convert xml1 -o - "$EXPECTED_ENTITLEMENTS")

MIT — free to use and modify