I'm downloading a fine-tuned model from HuggingFace which is then cached on my Mac when the app first starts. However, I wanted to test adding a progress bar to show the download progress. To test this I need to delete the cached model. From what I've seen online this is cached at /Users/userName/.cache/huggingface/hub However, if I delete the files from here, using Terminal, the app still seems to be able to access the model. Is the model cached somewhere else? On my iPhone it seems deleting the app also deletes the cached model (app data) so that is useful.

I'm implementing an App Intent for my iOS app that helps users plan trip activities. It only works when run as a shortcut but not using voice through Siri. There are 2 issues: The ShortcutsTripEntity will only accept a voice input for a specific trip but not others. I'm stuck with a throwing error when trying to use requestDisambiguation() on the activity day @Parameter property. How do I rectify these issues. This is blocking me from completing a critical feature that lets users quickly plan activities through Siri and Shortcuts. Expected behavior for trip input: The intent should make Siri accept the spoken trip input from any of the options. Actual behavior for trip input: Siri only accepts the same trip when spoken but accepts any when selected by click/touch. Expected behavior for day input: Siri should accept the spoken selected option. Actual behavior for day input: Siri only accepts an input by click/touch but yet throws an error at runtime I'm happy to provide more code. But here's the relevant code: struct PlanActivityTestIntent: AppIntent { @Parameter(title: "Activity Day") var activityDay: ShortcutsItineraryDayEntity @Parameter( title: "Trip", description: "The trip to plan an activity for", default: ShortcutsTripEntity(id: UUID().uuidString, title: "Untitled trip"), requestValueDialog: "Which trip would you like to add an activity to?" ) var tripEntity: ShortcutsTripEntity @Parameter(title: "Activity Title", description: "The title of the activity", requestValueDialog: "What do you want to do or see?") var title: String @Parameter(title: "Activity Day", description: "Activity Day", default: ShortcutsItineraryDayEntity(itineraryDay: .init(itineraryId: UUID(), date: .now), timeZoneIdentifier: "UTC")) var activityDay: ShortcutsItineraryDayEntity func perform() async throws -> some ProvidesDialog { // ...other code... let tripsStore = TripsStore() // load trips and map them to entities try? await tripsStore.getTrips() let tripsAsEntities = tripsStore.trips.map { trip in let id = trip.id ?? UUID() let title = trip.title return ShortcutsTripEntity(id: id.uuidString, title: title, trip: trip) } // Ask user to select a trip. This line would doesn't accept a voice // answer. Why? let selectedTrip = try await $tripEntity.requestDisambiguation( among: tripsAsEntities, dialog: .init( full: "Which of the \(tripsAsEntities.count) trip would you like to add an activity to?", supporting: "Select a trip", systemImageName: "safari.fill" ) ) // This line throws an error let selectedDay = try await $activityDay.requestDisambiguation( among: daysAsEntities, dialog:"Which day would you like to plan an activity for?" ) } } Here are some related images that might help:

I'm experimenting with Foundation Models and I'm trying to understand how to define a Tool whose input argument is defined at runtime. Specifically, I want a Tool that takes a single String parameter that can only take certain values defined at runtime. I think my question is basically the same as this one: https://developer.apple.com/forums/thread/793471 However, the answer provided by the engineer doesn't actually demonstrate how to create the GenerationSchema. Trying to piece things together from the documentation that the engineer linked to, I came up with this: let citiesDefinedAtRuntime = ["London", "New York", "Paris"] let citySchema = DynamicGenerationSchema( name: "CityList", properties: [ DynamicGenerationSchema.Property( name: "city", schema: DynamicGenerationSchema( name: "city", anyOf: citiesDefinedAtRuntime ) ) ] ) let generationSchema = try GenerationSchema(root: citySchema, dependencies: []) let tools = [CityInfo(parameters: generationSchema)] let session = LanguageModelSession(tools: tools, instructions: "...") With the CityInfo Tool defined like this: struct CityInfo: Tool { let name: String = "getCityInfo" let description: String = "Get information about a city." let parameters: GenerationSchema func call(arguments: GeneratedContent) throws -> String { let cityName = try arguments.value(String.self, forProperty: "city") print("Requested info about \(cityName)") let cityInfo = getCityInfo(for: cityName) return cityInfo } func getCityInfo(for city: String) -> String { // some backend that provides the info } } This compiles and usually seems to work. However, sometimes the model will try to request info about a city that is not in citiesDefinedAtRuntime. For example, if I prompt the model with "I want to travel to Tokyo in Japan, can you tell me about this city?", the model will try to request info about Tokyo, even though this is not in the citiesDefinedAtRuntime array. My understanding is that this should not be possible – constrained generation should only allow the LLM to generate an input argument from the list of cities defined in the schema. Am I missing something here or overcomplicating things? What's the correct way to make sure the LLM can only call a Tool with an input parameter from a set of possible values defined at runtime? Many thanks!

I'm trying the new RecognizeDocumentsRequest supposed to detect paragraphs (among other things) in a document. I tried many source images, and I don't see the slightest difference compared to the old API (VN)RecognizedTextRequest Is it supposed to not work or is it in beta?

Hello Apple Developer Community, I'm investigating Core ML model loading behavior and noticed that even when the compiled model path remains unchanged after an APP update, the first run still triggers an "uncached load" process. This seems to impact user experience with unnecessary delays. Question: Does Core ML provide any public API to check whether a compiled model (from a specific .mlmodelc path) is already cached in the system? If such API exists, we'd like to use it for pre-loading decision logic - only perform background pre-load when the model isn't cached. Has anyone encountered similar scenarios or found official solutions? Any insights would be greatly appreciated!

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.

I'm experimenting with the new SpeechTranscriber in macOS/iOS 26, transcribing speech from a prerecorded mp4 file. Speed and quality are amazing! I've told the transcriber to include time indexes. Each run is always exactly one word, which can be very useful. When I look at the indexes the end of one run is always identical to the start of the next run, even if there's a pause. I'd like to identify pauses, perhaps to generate something like phrases for subtitling. With each run of text going into the next I can't do this, other than using punctuation - which might be rather rough. Any suggestions on detecting pauses, or getting that kind of metadata from the transcriber? Here's a short sample, showing each run with the start, end, and characters in the run: 105.9 --> 107.04 I 107.04 --> 107.16 think 107.16 --> 108.0 more 108.0 --> 108.42 lighting 108.42 --> 108.6 is 108.6 --> 108.72 definitely 108.72 --> 109.2 needed, 109.2 --> 109.92 downtown. 109.98 --> 110.4 My 110.4 --> 110.52 only 110.52 --> 110.7 question 110.7 --> 111.06 is, 111.06 --> 111.48 poll 111.48 --> 111.78 five, 111.78 --> 111.84 that 111.84 --> 112.08 you're 112.08 --> 112.38 increasing 112.38 --> 112.5 the 112.5 --> 113.34 50,000? 113.4 --> 113.58 Where 113.58 --> 113.88 exactly

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

Here's the result: Very weird.

JAX Metal shows 55x slower random number generation compared to NVIDIA CUDA on equivalent workloads. This makes Monte Carlo simulations and scientific computing impractical on Apple Silicon. Performance Comparison NVIDIA GPU: 0.475s for 12.6M random elements M1 Max Metal: 26.3s for same workload Performance gap: 55x slower Environment Apple M1 Max, 64GB RAM, macOS Sequoia Version 15.6.1 JAX 0.4.34, jax-metal latest Backend: Metal Reproduction Code import time import jax import jax.numpy as jnp from jax import random key = random.PRNGKey(42) start_time = time.time() random_array = random.normal(key, (50000, 252)) duration = time.time() - start_time print(f"Duration: {duration:.3f}s")

@Generable enum Breakfast { case waffles case pancakes case bagels case eggs } do { let session = LanguageModelSession() let userInput = "I want something sweet." let prompt = "Pick the ideal breakfast for request: (userInput)" let response = try await session.respond(to: prompt,generating: Breakfast.self) print(response.content) } catch let error { print(error) } i want to test the @Generable demo but get error with below:decodingFailure(FoundationModels.LanguageModelSession.GenerationError.Context(debugDescription: "Failed to convert text into into GeneratedContent\nText: waffles", underlyingErrors: [Swift.DecodingError.dataCorrupted(Swift.DecodingError.Context(codingPath: [], debugDescription: "The given data was not valid JSON.", underlyingError: Optional(Error Domain=NSCocoaErrorDomain Code=3840 "Unexpected character 'w' around line 1, column 1." UserInfo={NSJSONSerializationErrorIndex=0, NSDebugDescription=Unexpected character 'w' around line 1, column 1.})))]))

Hi all, I spent the last few months developing an MLX/Ollama local AI Benchmarking suite for Apple Silicon, written in pure Swift and signed with an Apple Developer Certificate, open source, GPL, and free. I would love some feedback to continue development. It is the only benchmarking suite I know of that supports live power metrics and MLX natively, as well as quick exports for benchmark results, and an arena mode, Model A vs B with history. I really want this project to succeed, and have widespread use, so getting 75 stars on the github repo makes it eligible for Homebrew/Cask distribution. Github Repo

Hello fellow developers, I'm the founder of a FinTech startup, Cent Capital (https://cent.capital), where we are building an AI-powered financial co-pilot. We're deeply exploring the Apple ecosystem to create a more proactive and ambient user experience. A core part of our vision is to use App Intents and the Shortcuts app to surface personalized financial insights without the user always needing to open our app. For example, suggesting a Shortcut like, "What's my spending in the 'Dining Out' category this month?" or having an App Intent proactively surface an insight like, "Your 'Subscriptions' budget is almost full." My question for the community is about the architectural and user experience best practices for this. How are you thinking about the balance between providing rich, actionable insights via Intents without being overly intrusive or "spammy" to the user? What are the best practices for designing the data model that backs these App Intents for a complex domain like personal finance? Are there specific performance or privacy considerations we should be aware of when surfacing potentially sensitive financial data through these system-level integrations? We believe this is the future of FinTech apps on iOS and would love to hear how other developers are thinking about this challenge. Thanks for your insights!

The developer tutorial for visual intelligence indicates that the method to detect and handle taps on a displayed entity from the Search section is via an "OpenIntent" associated with your entity. However, running this intent executes code from within my app. If I have the perform() method display UI, it always displays UI from within my app. I noticed that the Google app's integration to visual intelligence has a different behavior-- tapping on an entity does not take you to the Google app -- instead, a Webview is presented sheet-style WITHIN the Visual Intelligence environment (see below) How is that accomplished?

Hi, I'm currently using Metal Performance Shaders Graph (MPSGraphExecutable) to run neural network inference operations as part of a metal rendering pipeline. I also tried to profile the usage of neural engine when running inference using MPSGraphExecutable but the graph shows no sign of neural engine usage. However, when I used the coreML model inspection tool in xcode and run performance report, it was able to use ANE. Does MPSGraphExecutable automatically utilize the Apple Neural Engine (ANE) when running inference operations, or does it only execute on GPU? My model (Core ML Package) was converted from a pytouch model using coremltools with ML program type and support iOS17.0+. Any insights or documentation references would be greatly appreciated!

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

Download the Foundation Models Adaptor Training Toolkit Hi, after I clicked on the download button, I was redirected to this page https://developer.apple.com and did not download the toolkit.

Hello, I’m experiencing a severe performance degradation when running CoreML models on a live AVFoundation video feed compared to offline or synthetic inference. This happens across multiple models I've converted (including SCI, RTMPose, and RTMW) and affects multiple devices. The Environment OS: macOS 26.3, iOS 26.3, iPadOS 26.3 Hardware: Mac14,6 (M2 Max), iPad Pro 11 M1, iPhone 13 mini Compute Units: cpuAndNeuralEngine The Numbers When testing my SCI_output_image_int8.mlpackage model, the inference timings are drastically different: Synthetic/Offline Inference: ~1.34 ms Live Camera Inference: ~15.96 ms Preprocessing is completely ruled out as the bottleneck. My profiling shows total preprocessing (nearest-neighbor resize + feature provider creation) takes only ~0.4 ms in camera mode. Furthermore, no frames are being dropped. What I've Tried I am building a latency-critical app and have implemented almost every recommended optimization to try and fix this, but the camera-feed penalty remains: Matched the AVFoundation camera output format exactly to the model input (640x480 at 30/60fps). Used IOSurface-backed pixel buffers for everything (camera output, synthetic buffer, and resize buffer). Enabled outputBackings. Loaded the model once and reused it for all predictions. Configured MLModelConfiguration with reshapeFrequency = .frequent and specializationStrategy = .fastPrediction. Wrapped inference in ProcessInfo.processInfo.beginActivity(options: .latencyCritical, reason: "CoreML_Inference"). Set DispatchQueue to qos: .userInteractive. Disabled the idle timer and enabled iOS Game Mode. Exported models using coremltools 9.0 (deployment target iOS 26) with ImageType inputs/outputs and INT8 quantization. Reproduction To completely rule out UI or rendering overhead, I wrote a standalone Swift CLI script that isolates the AVFoundation and CoreML pipeline. The script clearly demonstrates the ~15ms latency on live camera frames versus the ~1ms latency on synthetic buffers. (I have attached camera_coreml_benchmark.swift and coreml model (very light low light enghancement model) to this repo on github https://github.com/pzoltowski/apple-coreml-camera-latency-repro). My Question: Is this massive overhead expected behavior for AVFoundation + Core ML on live feeds, or is this a framework/runtime bug? If expected, what is the Apple-recommended pattern to bypass this camera-only inference slowdown? One think found interesting when running in debug model was faster (not as fast as in performance benchmark but faster than 16ms. Also somehow if I did some dummy calculation on on different DispatchQueue also seems like model got slightly faster. So maybe its related to ANE Power State issues (Jitter/SoC Wake) and going to fast to sleep and taking a long time to wakeup? Doing dummy calculation in background thought is probably not a solution. Thanks in advance for any insights!

I've created a "Transfer Learning BERT Embeddings" model with the default "Latin" language family and "Automatic" Language setting. This model performs exceptionally well against the test data set and functions as expected when I preview it in Create ML. However, when I add it to the Xcode project of the application to which I am deploying it, I am getting runtime errors that suggest it can't find the embedding resources: Failed to locate assets for 'mul_Latn' - '5C45D94E-BAB4-4927-94B6-8B5745C46289' embedding model Note, I am adding the model to the app project the same way that I added an earlier "Maximum Entropy" model. That model had no runtime issues. So it seems there is an issue getting hold of the embeddings at runtime. For now, "runtime" means in the Simulator. I intend to deploy my application to iOS devices once GM 26 is released (the app also uses AFM). I'm developing on Tahoe 26 beta, running on iOS 26 beta, using Xcode 26 beta. Is this a known/expected issue? Are the embeddings expected to be a resource in the model? Is there a workaround? I did try opening the model in Xcode and saving it as an mlpackage, then adding that to my app project, but that also didn't resolve the issue.

Can't able to run the Create ML for training and I upgraded to MacOS 26.3 beta and I have tried older and newer