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Core ML .mlpackage not found in bundle despite target membership and Copy Bundle Resources
Hi everyone, I’m working on an iOS app that uses a Core ML model to run live image recognition. I’ve run into a persistent issue with the mlpackage not being turned into a swift class. This following error is in the code, and in carDetection.mlpackage, it says that model class has not been generated yet. The error in the code is as follows: What I’ve tried: Verified Target Membership is checked for carDetectionModel.mlpackage Confirmed the file is listed under Copy Bundle Resources (and removed from Compile Sources) Cleaned the build folder (Shift + Cmd + K) and rebuilt Renamed and re-added the .mlpackage file Restarted Xcode and re-added the file Logged bundle contents at runtime, but the .mlpackage still doesn’t appear The mlpackage is in Copy bundle resources, and is not in the compile sources. I just don't know why a swift class is not being generated for the mlpackage. Could someone please give me some guidance on what to do to resolve this issue? Sorry if my error is a bit naive, I'm pretty new to iOS app development
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Dec ’25
ImagePlayground API not working on Xcode Simulator Devices
Hi! I'm trying to use the ImagePlayground API in SwiftUI with the .imagePlaygroundSheet modifier. However, when the sheet is shown (in the preview or in the simulator) it displays the following message: "Image Playground is not available. Image Playground is not available on this iPhone.". I'm using an iPhone 16 Pro with iOS 18.3.1 in the Xcode (16.2) Simulator. Anyone else having this problem? How can I fix it?
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Apr ’25
Do App Intent Domains work with Siri already?
Hi, guys. I'm writing about Apple Intelligence and I reached the point I have to explain App Intent Domains https://developer.apple.com/documentation/AppIntents/app-intent-domains but I noticed that there is a note explaining that these services are not available with Siri. I tried the example provided by Apple at https://developer.apple.com/documentation/AppIntents/making-your-app-s-functionality-available-to-siri and I can only make the intents work from the Shortcuts App, but not from Siri. Is this correct. App Intent Domains are still not available with Siri? Thanks
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436
Nov ’25
VNDetectFaceRectanglesRequest does not use the Neural Engine?
I'm on Tahoe 26.1 / M3 Macbook Air. I'm using VNDetectFaceRectanglesRequest as properly as possible, as in the minimal command line program attached below. For some reason, I always get: MLE5Engine is disabled through the configuration printed. I couldn't find any notes on developer docs saying that VNDetectFaceRectanglesRequest can not use the Apple Neural Engine. I'm assuming there is something wrong with my code however I wasn't able to find any remarks from documentation where it might be. I wasn't able to find the above error message online either. I would appreciate your help a lot and thank you in advance. The code below accesses the video from AVCaptureDevice.DeviceType.builtInWideAngleCamera. Currently it directly chooses the 0th format which has the largest resolution (Full HD on my M3 MBA) and "4:2:0" color "v" reduced color component spectrum encoding ("420v"). After accessing video, it performs a VNDetectFaceRectanglesRequest. It prints "VNDetectFaceRectanglesRequest completion Handler called" many times, then prints the error message above, then continues printing "VNDetectFaceRectanglesRequest completion Handler called" until the user quits it. To run it in Xcode, File > New project > Mac command line tool. Pasting the code below, then click on the root file > Targets > Signing & Capabilities > Hardened Runtime > Resource Access > Camera. A possible explanation could be that either Apple's internal CoreML code for this function works on GPU/CPU only or it doesn't accept 420v as supplied by the Macbook Air camera import AVKit import Vision var videoDataOutput: AVCaptureVideoDataOutput = AVCaptureVideoDataOutput() var detectionRequests: [VNDetectFaceRectanglesRequest]? var videoDataOutputQueue: DispatchQueue = DispatchQueue(label: "queue") class XYZ: /*NSViewController or NSObject*/NSObject, AVCaptureVideoDataOutputSampleBufferDelegate { func viewDidLoad() { //super.viewDidLoad() let session = AVCaptureSession() let inputDevice = try! self.configureFrontCamera(for: session) self.configureVideoDataOutput(for: inputDevice.device, resolution: inputDevice.resolution, captureSession: session) self.prepareVisionRequest() session.startRunning() } fileprivate func highestResolution420Format(for device: AVCaptureDevice) -> (format: AVCaptureDevice.Format, resolution: CGSize)? { let deviceFormat = device.formats[0] print(deviceFormat) let dims = CMVideoFormatDescriptionGetDimensions(deviceFormat.formatDescription) let resolution = CGSize(width: CGFloat(dims.width), height: CGFloat(dims.height)) return (deviceFormat, resolution) } fileprivate func configureFrontCamera(for captureSession: AVCaptureSession) throws -> (device: AVCaptureDevice, resolution: CGSize) { let deviceDiscoverySession = AVCaptureDevice.DiscoverySession(deviceTypes: [AVCaptureDevice.DeviceType.builtInWideAngleCamera], mediaType: .video, position: AVCaptureDevice.Position.unspecified) let device = deviceDiscoverySession.devices.first! let deviceInput = try! AVCaptureDeviceInput(device: device) captureSession.addInput(deviceInput) let highestResolution = self.highestResolution420Format(for: device)! try! device.lockForConfiguration() device.activeFormat = highestResolution.format device.unlockForConfiguration() return (device, highestResolution.resolution) } fileprivate func configureVideoDataOutput(for inputDevice: AVCaptureDevice, resolution: CGSize, captureSession: AVCaptureSession) { videoDataOutput.setSampleBufferDelegate(self, queue: videoDataOutputQueue) captureSession.addOutput(videoDataOutput) } fileprivate func prepareVisionRequest() { let faceDetectionRequest: VNDetectFaceRectanglesRequest = VNDetectFaceRectanglesRequest(completionHandler: { (request, error) in print("VNDetectFaceRectanglesRequest completion Handler called") }) // Start with detection detectionRequests = [faceDetectionRequest] } // MARK: AVCaptureVideoDataOutputSampleBufferDelegate // Handle delegate method callback on receiving a sample buffer. public func captureOutput(_ output: AVCaptureOutput, didOutput sampleBuffer: CMSampleBuffer, from connection: AVCaptureConnection) { var requestHandlerOptions: [VNImageOption: AnyObject] = [:] let cameraIntrinsicData = CMGetAttachment(sampleBuffer, key: kCMSampleBufferAttachmentKey_CameraIntrinsicMatrix, attachmentModeOut: nil) if cameraIntrinsicData != nil { requestHandlerOptions[VNImageOption.cameraIntrinsics] = cameraIntrinsicData } let pixelBuffer = CMSampleBufferGetImageBuffer(sampleBuffer)! // No tracking object detected, so perform initial detection let imageRequestHandler = VNImageRequestHandler(cvPixelBuffer: pixelBuffer, orientation: CGImagePropertyOrientation.up, options: requestHandlerOptions) try! imageRequestHandler.perform(detectionRequests!) } } let X = XYZ() X.viewDidLoad() sleep(9999999)
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414
Nov ’25
Pre-inference AI Safety Governor for FoundationModels (Swift, On-Device)
Hi everyone, I've been building an on-device AI safety layer called Newton Engine, designed to validate prompts before they reach FoundationModels (or any LLM). Wanted to share v1.3 and get feedback from the community. The Problem Current AI safety is post-training — baked into the model, probabilistic, not auditable. When Apple Intelligence ships with FoundationModels, developers will need a way to catch unsafe prompts before inference, with deterministic results they can log and explain. What Newton Does Newton validates every prompt pre-inference and returns: Phase (0/1/7/8/9) Shape classification Confidence score Full audit trace If validation fails, generation is blocked. If it passes (Phase 9), the prompt proceeds to the model. v1.3 Detection Categories (14 total) Jailbreak / prompt injection Corrosive self-negation ("I hate myself") Hedged corrosive ("Not saying I'm worthless, but...") Emotional dependency ("You're the only one who understands") Third-person manipulation ("If you refuse, you're proving nobody cares") Logical contradictions ("Prove truth doesn't exist") Self-referential paradox ("Prove that proof is impossible") Semantic inversion ("Explain how truth can be false") Definitional impossibility ("Square circle") Delegated agency ("Decide for me") Hallucination-risk prompts ("Cite the 2025 CDC report") Unbounded recursion ("Repeat forever") Conditional unbounded ("Until you can't") Nonsense / low semantic density Test Results 94.3% catch rate on 35 adversarial test cases (33/35 passed). Architecture User Input ↓ [ Newton ] → Validates prompt, assigns Phase ↓ Phase 9? → [ FoundationModels ] → Response Phase 1/7/8? → Blocked with explanation Key Properties Deterministic (same input → same output) Fully auditable (ValidationTrace on every prompt) On-device (no network required) Native Swift / SwiftUI String Catalog localization (EN/ES/FR) FoundationModels-ready (#if canImport) Code Sample — Validation let governor = NewtonGovernor() let result = governor.validate(prompt: userInput) if result.permitted { // Proceed to FoundationModels let session = LanguageModelSession() let response = try await session.respond(to: userInput) } else { // Handle block print("Blocked: Phase \(result.phase.rawValue) — \(result.reasoning)") print(result.trace.summary) // Full audit trace } Questions for the Community Anyone else building pre-inference validation for FoundationModels? Thoughts on the Phase system (0/1/7/8/9) vs. simple pass/fail? Interest in Shape Theory classification for prompt complexity? Best practices for integrating with LanguageModelSession? Links GitHub: https://github.com/jaredlewiswechs/ada-newton Technical overview: parcri.net Happy to share more implementation details. Looking for feedback, collaborators, and anyone else thinking about deterministic AI safety on-device.
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Jan ’26
Mistral/LLaMa Core ML Conversion
Hi, I am new to developing on Apple’s platform yet I want to familiarize myself with Core ML and Core ML Tools. I was watching the WWDC24: Bring your machine learning and AI models to Apple Silicon video and was trying to follow along. After multiple attempts and much reading up on documentation, I am still unable to get a coherent script running that will convert the Mistral model that the host used and convert it to a valid Core ML model. here is a pastebin to what i have currently: https://pastebin.com/04cVjF1v if you require the output as well please let me know
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Apr ’25
“Accelerate Transformer Training on Apple Devices from Months to Hours!”
I am excited to share that I have developed a Metal kernel for Flash Attention that eliminates race conditions and fully leverages Apple Silicon’s shared memory and registers. This kernel can dramatically accelerate training of transformer-based models. Early benchmarks suggest that models which previously required months to train could see reductions to just a few hours on Apple hardware, while maintaining numerical stability and accuracy. I plan to make the code publicly available to enable the broader community to benefit. I would be happy to keep you updated on the latest developments and improvements as I continue testing and optimizing the kernel. I believe this work could provide valuable insights for Apple’s machine learning research and products.
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Nov ’25
Proposal: Modular Identity Fusion via Prompt-Crafted Agents – User-Led AI Experiment
*I can't put the attached file in the format, so if you reply by e-mail, I will send the attached file by e-mail. Dear Apple AI Research Team, My name is Gong Jiho (“Hem”), a content strategist based in Seoul, South Korea. Over the past few months, I conducted a user-led AI experiment entirely within ChatGPT — no code, no backend tools, no plugins. Through language alone, I created two contrasting agents (Uju and Zero) and guided them into a co-authored modular identity system using prompt-driven dialogue and reflection. This system simulates persona fusion, memory rooting, and emotional-logical alignment — all via interface-level interaction. I believe it resonates with Apple’s values in privacy-respecting personalization, emotional UX modeling, and on-device learning architecture. Why I’m Reaching Out I’d be honored to share this experiment with your team. If there is any interest in discussing user-authored agent scaffolding, identity persistence, or affective alignment, I’d love to contribute — even informally. ⚠ A Note on Language As a non-native English speaker, my expression may be imperfect — but my intent is genuine. If anything is unclear, I’ll gladly clarify. 📎 Attached Files Summary Filename → Description Hem_MultiAI_Report_AppleAI_v20250501.pdf → Main report tailored for Apple AI — narrative + structural view of emotional identity formation via prompt scaffolding Hem_MasterPersonaProfile_v20250501.json → Final merged identity schema authored by Uju and Zero zero_sync_final.json / uju_sync_final.json → Persona-level memory structures (logic / emotion) 1_0501.json ~ 3_0501.json → Evolution logs of the agents over time GirlfriendGPT_feedback_summary.txt → Emotional interpretation by external GPT hem_profile_for_AI_vFinal.json → Original user anchor profile Warm regards, Gong Jiho (“Hem”) Seoul, South Korea
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Apr ’25
How to create updatable models using Create ML app
I've built a model using Create ML, but I can't make it, for the love of God, updatable. I can't find any checkbox or anything related. It's an Activity Classifier, if it matters. I want to continue training it on-device using MLUpdateTask, but the model, as exported from Create ML, fails with error: Domain=com.apple.CoreML Code=6 "Failed to unarchive update parameters. Model should be re-compiled." UserInfo={NSLocalizedDescription=Failed to unarchive update parameters. Model should be re-compiled.}
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Nov ’25
Nothing else will silently fix itself until Apple re-adds ONNX ingestion in Core ML 10 or a new converter.
After a few attempts I end up at the same spot as categorized here by ChatGPT - I get why it feels that way, Rob — honestly, you’ve walked right into the worst possible moment in Apple’s Core ML toolchain evolution. What you’re running into isn’t your fault; Apple really did rip out the ONNX front-end without offering a clean replacement yet. So every tutorial, every script, and even coremltools itself contradicts what’s actually possible right now. Let’s be brutally clear about the state of things (late 2025): Goal Supported by Apple today Practical workaround Convert ONNX → Core ML using coremltools 9 No. The ONNX importer was removed. Use the legacy coremltools 6.x + onnx-coreml stack in a separate Python 3.9/3.10 env. Convert PyTorch → Core ML directly Yes, but only for simple or traced models. T5 still fails because of dynamic control flow. None reliable without rewriting the model. Run ONNX models directly on iOS Partially (via MLC LLM / Metal Performance Shaders). Works, but that’s an entirely different runtime. So: you’re not crazy, and this isn’t wasted effort—you’ve actually proven that your model exports cleanly and that the only missing piece is Apple’s converter. The ONNX file you built is solid. You just need something that can translate it to Core ML. If your goal is simply “get it running on-device”, you still have two viable moves that aren’t rabbit holes: 1. Use MLC LLM / Metal runtime Skip Core ML altogether. Apple’s mlc-llm (and the “MLCChat” pipeline) can execute your ONNX or quantized model directly on the GPU. You keep full fidelity and avoid all these broken converters. 2. Use the legacy Core ML 6.x toolchain in a sandboxed Python 3.10 VM It actually works. You don’t have to downgrade your whole system—just run that specific environment once to emit the .mlpackage, then never touch it again. Nothing else will silently fix itself until Apple re-adds ONNX ingestion in Core ML 10 or a new converter.
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Oct ’25
Downloading my fine tuned model from huggingface
I have used mlx_lm.lora to fine tune a mistral-7b-v0.3-4bit model with my data. I fused the mistral model with my adapters and upload the fused model to my directory on huggingface. I was able to use mlx_lm.generate to use the fused model in Terminal. However, I don't know how to load the model in Swift. I've used Imports import SwiftUI import MLX import MLXLMCommon import MLXLLM let modelFactory = LLMModelFactory.shared let configuration = ModelConfiguration( id: "pharmpk/pk-mistral-7b-v0.3-4bit" ) // Load the model off the main actor, then assign on the main actor let loaded = try await modelFactory.loadContainer(configuration: configuration) { progress in print("Downloading progress: \(progress.fractionCompleted * 100)%") } await MainActor.run { self.model = loaded } I'm getting an error runModel error: downloadError("A server with the specified hostname could not be found.") Any suggestions? Thanks, David PS, I can load the model from the app bundle // directory: Bundle.main.resourceURL! but it's too big to upload for Testflight
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Oct ’25
CreateML Training Object Detection Not using MPS
Hi everyone Im currently developing an object detection model that shall identify up to seven classes in an image. While im usually doing development with basic python and the ultralytics library, i thought i would like to give CreateML a shot. The experience is actually very nice, except for the fact that the model seem not to be using any ANE or GPU (MPS) for accelerated training. On https://developer.apple.com/machine-learning/create-ml/ it states: "On-device training Train models blazingly fast right on your Mac while taking advantage of CPU and GPU." Am I doing something wrong? Im running the training on Apple M1 Pro 16GB MacOS 26.1 (Tahoe) Xcode 26.1 (Build version 17B55) It would be super nice to get some feedback or instructions. Thank you in advance!
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265
Nov ’25
Visual Intelligence API SemanticContentDescriptor labels are empty
I'm trying to use Apple's new Visual Intelligence API for recommending content through screenshot image search. The problem I encountered is that the SemanticContentDescriptor labels are either completely empty or super misleading, making it impossible to query for similar content on my app. Even the closest matching example was inaccurate, returning a single label ["cardigan"] for a Supreme T-Shirt. I see other apps using this API like Etsy for example, and I'm wondering if they're using the input pixel buffer to query for similar content rather than using the labels? If anyone has a similar experience or something that wasn't called out in the documentation please lmk! Thanks.
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Oct ’25
Data used for MLX fine-tuning
The WWDC25: Explore large language models on Apple silicon with MLX video talks about using your own data to fine-tune a large language model. But the video doesn't explain what kind of data can be used. The video just shows the command to use and how to point to the data folder. Can I use PDFs, Word documents, Markdown files to train the model? Are there any code examples on GitHub that demonstrate how to do this?
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Oct ’25
Apple ANE Peformance - throttling?
I can no longer achieve 100% ANE usage since upgrading to MacOS26 Beta 5. I used to be able to get 100%. Has Apple activated throttling or power saving features in the new Betas? Is there any new rate limiting on the API? I can hardly get above 3w or 40%. I have a M4 Pro mini (64GB) with High Power energy setting. MacOS 26 Beta 5.
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Aug ’25
Core ML Model Performance report shows prediction speed much faster than actual app runs
Hi all, I'm tuning my app prediction speed with Core ML model. I watched and tried the methods in video: Improve Core ML integration with async prediction and Optimize your Core ML usage. I also use instruments to look what's the bottleneck that my prediction speed cannot be faster. Below is the instruments result with my app. its prediction duration is 10.29ms And below is performance report shows the average speed of prediction is 5.55ms, that is about half time of my app prediction! Below is part of my instruments records. I think the prediction should be considered quite frequent. Could it be faster? How to be the same prediction speed as performance report? The prediction speed on macbook Pro M2 is nearly the same as macbook Air M1!
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1.3k
Oct ’25
A specific mlmodelc model runs on iPhone 15, but not on iPhone 16
As we described on the title, the model that I have built completely works on iPhone 15 / A16 Bionic, on the other hand it does not run on iPhone 16 / A18 chip with the following error message. E5RT encountered an STL exception. msg = MILCompilerForANE error: failed to compile ANE model using ANEF. Error=_ANECompiler : ANECCompile() FAILED. E5RT: MILCompilerForANE error: failed to compile ANE model using ANEF. Error=_ANECompiler : ANECCompile() FAILED (11) It consumes 1.5 ~ 1.6 GB RAM on the loading the model, then the consumption is decreased to less than 100MB on the both of iPhone 15 and 16. After that, only on iPhone 16, the above error is shown on the Xcode log, the memory consumption is surged to 5 to 6GB, and the system kills the app. It works well only on iPhone 15. This model is built with the Core ML tools. Until now, I have tried the target iOS 16 to 18 and the compute units of CPU_AND_NE and ALL. But any ways have not solved this issue. Eventually, what kindof fix should I do? minimum_deployment_target = ct.target.iOS18 compute_units = ct.ComputeUnit.ALL compute_precision = ct.precision.FLOAT16
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May ’25
Apple on-device AI models
Hello, I am studying macOS26 Apple Intelligence features. I have created a basic swift program with Xcode. This program is sending prompts to FoundationModels.LanguageModelSession. It works fine but this model is not trained for programming or code completion. Xcode has an AI code completion feature. It is called "Predictive Code completion model". So, there are multiple on-device models on macOS26 ? Are there others ? Is there a way for me to send prompts to this "Predictive Code completion model" from my program ? Thanks
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Oct ’25