Hi, I'm looking for the best way to use MLX models, particularly those I've fine-tuned, within a React Native application on iOS devices. Is there a recommended integration path or specific API for bridging MLX's capabilities to React Native for deployment on iPhones and iPads?

I'm currently trying to add support for Image Playground to our apps. It seems that it's not working in an app that is "Designed for iPad" and runs on a Mac. The modal just shows a spinner and the following is logged to console: Private sandbox for com.apple.GenerativePlaygroundApp.remoteUIExtension : <none> Private sandbox for com.apple.GenerativePlaygroundApp.remoteUIExtension : <none> Private sandbox for com.apple.GenerativePlaygroundApp.remoteUIExtension : <none> Private sandbox for com.apple.GenerativePlaygroundApp.remoteUIExtension : <none> GP extension could not be loaded: Extension (platform: 2) could not be found (in update) dealloc Query controller [C32BA176-6A3E-465D-B3C5-0F8D91068B89] ImagePlaygroundViewController.isAvailable returns true, however. In a "real" Mac Catalyst app, it's working. Just not when the app is actually an iPad app. Is this a bug?

I have an app that stores lots of data that is of interest to the user. Analogies would be the Photos apps or the Health app. I'm trying to use the Foundation Models framework to allow users to surface information they find interesting using natural language, for example, "Tell me about the widgets from yesterday" or "Tell me about the widgets for the last 3 days". Specifically, I'm trying to get a date range passed down to the Tool so that I can pull the relevant widgets from the database in the call function. What is the right way to set up the Arguments to get at a date range?

Is there an API that allows iOS app developers to leverage Apple Foundation Models to authorize a user's Apple Intelligence extension, chatGPT login account? I'm trying to provide a real-time question feature for chatGPT, a logged-in extension account, while leveraging Apple Intelligence's LLM. Is there an API that also affects the extension login account?

Context I trained a LoRA adapter for Apple’s on-device language model using the Foundation Models Adapter Training Toolkit v0.2.0 on macOS 26 beta 4. Although training completes successfully, loading the resulting .fmadapter package fails with: Adapter is not compatible with the current system base model. What I’ve Observed, Hard-coded Signature: In export/constants.py, the toolkit sets, BASE_SIGNATURE = "9799725ff8e851184037110b422d891ad3b92ec1" Metadata Injection: The export_fmadapter.py script writes this value into the adapter’s metadata: self_dict[MetadataKeys.BASE_SIGNATURE] = BASE_SIGNATURE Compatibility Check: At runtime, the Foundation Models framework compares the adapter’s baseModelSignature against the OS’s system model signature, and reports compatibleAdapterNotFound if they don’t match—without revealing the expected signature. Questions Signature Generation - What exactly does the toolkit hash to derive BASE_SIGNATURE? Is it a straight SHA-1 of base-model.pt, or is there an additional transformation? Recomputing for Beta 4 - Is there a way to locally compute the correct signature for the macOS 26 beta 4 system model? Toolkit Updates - Will Apple release Adapter Training Toolkit v0.3.0 with an updated BASE_SIGNATURE for beta 4, or is there an alternative workaround to generate it myself? Any guidance on how the Foundation Models framework derives and verifies the base model signature—or how to regenerate it for beta 4—would be greatly appreciated.

I used Yolo5-11 and while performing great detecting balls lets say 5-10ft away in 1920 resolution and even in 640 it really is taking toll on my app performance. When I use Create ML it outputs all in 415x which is probably the reason why it does not detect objects from far. What can I do to preserve some energy ? My model is used with about 1K pictures 200 each test and validate, and from close up and far.

Hi, I'm trying to use the new RecognizeDocumentsRequest from the Vision Framework to read a receipt. It looks very promising by being able to read paragraphs, lines and detect data. So far it unfortunately seems to read every line on the receipt as a paragraph and when there is more space on one line it creates two paragraphs. Is there perhaps an Apple Engineer who knows if this is expected behaviour or if I should file a Feedback for this? Code setup: let request = RecognizeDocumentsRequest() let observations = try await request.perform(on: image) guard let document = observations.first?.document else { return } for paragraph in document.paragraphs { print(paragraph.transcript) for data in paragraph.detectedData { switch data.match.details { case .phoneNumber(let data): print("Phone: \(data)") case .postalAddress(let data): print("Postal: \(data)") case .calendarEvent(let data): print("Calendar: \(data)") case .moneyAmount(let data): print("Money: \(data)") case .measurement(let data): print("Measurement: \(data)") default: continue } } } See attached image as an example of a receipt I'd like to parse. The top 3 lines are the name, street, and postal code + city. These are all separate paragraphs. Checking on detectedData does see the street (2nd line) as PostalAddress, but not the complete address. Might that be a location thing since it's a Dutch address. And lower on the receipt it sees the block with "Pomp 1 95 Ongelood" and the things below also as separate paragraphs. First picking up the left side and after that the right side. So it's something like this: * Pomp 1 Volume Prijs € TOTAAL * BTW Netto 21.00 % 95 Ongelood 41,90 l 1.949/ 1 81.66 € 14.17 67.49

Hi everyone, I've been struggling for a few weeks to integrate my Core ML Image Classifier model into my .swiftpm project, and I’m hoping someone can help. Here’s what I’ve done so far: I converted my .mlmodel file to .mlmodelc manually via the terminal. In my Package.swift file, I tried both "copy" and "process" options for the resource. The issues I’m facing: When using "process", Xcode gives me the error: "multiple resources named 'coremldata.bin' in target 'AppModule'." When using "copy", the app runs, but the model doesn’t work, and the terminal shows: "A valid manifest does not exist at path: .../Manifest.json." I even tried creating a Manifest.json manually to test, but this led to more errors, such as: "File format version must be in the form of major.minor.patch." "Failed to look up root model." To check if the problem was specific to my model, I tested other Core ML models in the same setup, but none of them worked either. I feel stuck and unsure of how to resolve these issues. Any guidance or suggestions would be greatly appreciated. Thanks in advance! :)

I am using macOS Tahoe on Xcode 26.

Hi For certain tasks, such as qualitative analysis or tagging, it is advisable to provide the AI with the option to respond with a joker / wild card answer when it encounters difficulties in tagging or scoring. For instance, you can include this slot in the prompt as follows: output must be "not data to score" when there isn't information to score. In the absence of these types of slots, AI trends to provide a solution even when there is insufficient information. Foundations Models are told to be prompted with simple prompts. I wonder: Is recommended keep this slot though adds verbose complexity? Is the best place the comment of a guided attribute? other tips? Another use case is when you want the AI to be tied to the information provided in the prompt and not take information from its data set. What is the best approach to this purpose? Thanks in advance for any suggestion.

Problem I have set SWIFT_UPCOMING_FEATURE_EXISTENTIAL_ANY at Build Settings > Swift Compiler - Upcoming Features to true to support this existential any proposal. Then following errors appears in the MLModel class, but this is an auto-generated file, so I don't know how to deal with it. Use of protocol 'MLFeatureProvider' as a type must be written 'any MLFeatureProvider' Use of protocol 'Error' as a type must be written 'any Error' environment Xcode 16.0 Xcode 16.1 Beta 2 What I tried Delete cache of DerivedData and regenerate MLModel class files I also tried using DepthAnythingV2SmallF16P6.mlpackage to verify if there is a problem with my mlmodel I tried the above after setting up Swift6 in Xcode I also used coremlc to generate MLModel class files with Swift6 specified by command.

Hello, I was trying to test out Foundation Model however it says Model assets are unavailable. I got my MacBook M1 back in China when i was living there. is this due to region lock?

I am working on an app using FoundationModels to process web pages. I am looking to find ways to filter the input to fit within the token limits. I have unit tests, UI tests and the app running on an iPad in the simulator. It appears that the different configurations of the test environment seems to affect the token limits. That is, the same input in a unit test and UI test will hit different token limits. Is this correct? Or is this an artifact of my test tooling?

I didn't run benchmarks before update, but it seems at least 5x slower. Of course all the LLM work is on remote servers, so is non-intuitive to me this should be happening. Had updated MacOS and Xcode to 26.1RC at the same time, so can't even say I think it is MacOS or I think it is Xcode. Before the update the progress indicator for each piece of code might seem to get stuck at the very end (and toggling between Navigators and Coding Assistant) in Xcode UI seemed to refresh the UI and confirm coding complete... but now it seems progress races to 50%, then often is stuck at 75%... well earlier than used to get stuck. And it like something is legitimately processing not just a UI glitch. I'm wondering if this is somehow tied to visual rendering of the code in the little white window? CMD-TAB into Xcode seems laggy. Xcode is pinning a CPU. Why, this is all remote LLM work? MacBook Pro 2021 M1 64GB RAM. Went from 26.01 to 26.1RC. Didn't touch any of the betas until RC1.

Does the Foundation Model provide Objective-C compatible APIs?

After updating to macOS15.2beta, the Yolo11 object detection model exported to coreml outputs incorrect and abnormal bounding boxes. It also doesn't work in iOS apps built on a 15.2 mac. The same model worked fine on macOS14.1. When training a Yolo11 custom model in Python, exporting it to coreml, and testing it in the preview tab of mlpackage on macOS15.2 and Xcode16.0, the above result is obtained.

Hello, I am developing an app for the Swift Student challenge; however, I keep encountering an error when using ClassifyImageRequest from the Vision framework in Xcode: VTEST: error: perform(_:): inside 'for await result in resultStream' error: internalError("Error Domain=NSOSStatusErrorDomain Code=-1 \"Failed to create espresso context.\" UserInfo={NSLocalizedDescription=Failed to create espresso context.}") It works perfectly when testing it on a physical device, and I saw on another thread that ClassifyImageRequest doesn't work on simulators. Will this cause problems with my submission to the challenge? Thanks

I have a fairly basic prompt I've created that parses a list of locations out of a string. I've then created a tool, which for these locations, finds their latitude/longitude on a map and populates that in the response. However, I cannot get the language model session to see/use my tool. I have code like this passing the tool to my prompt: class Parser { func populate(locations: String, latitude: Double, longitude: Double) async { let findLatLonTool = FindLatLonTool(latitude: latitude, longitude: longitude) let session = LanguageModelSession(tools: [findLatLonTool]) { """ A prompt that populates a model with a list of locations. """ """ Use the findLatLon tool to populate the latitude and longitude for the name of each location. """ } let stream = session.streamResponse(to: "Parse these locations: \(locations)", generating: ParsedLocations.self) let locationsModel = LocationsModels(); do { for try await partialParsedLocations in stream { locationsModel.parsedLocations = partialParsedLocations.content } } catch { print("Error parsing") } } } And then the tool that looks something like this: import Foundation import FoundationModels import MapKit struct FindLatLonTool: Tool { typealias Output = GeneratedContent let name = "findLatLon" let description = "Find the latitude / longitude of a location for a place name." let latitude: Double let longitude: Double @Generable struct Arguments { @Guide(description: "This is the location name to look up.") let locationName: String } func call(arguments: Arguments) async throws -> GeneratedContent { let request = MKLocalSearch.Request() request.naturalLanguageQuery = arguments.locationName request.region = MKCoordinateRegion( center: CLLocationCoordinate2D(latitude: latitude, longitude: longitude), latitudinalMeters: 1_000_000, longitudinalMeters: 1_000_000 ) let search = MKLocalSearch(request: request) let coordinate = try await search.start().mapItems.first?.location.coordinate if let coordinate = coordinate { return GeneratedContent( LatLonModel(latitude: coordinate.latitude, longitude: coordinate.longitude) ) } return GeneratedContent("Location was not found - no latitude / longitude is available.") } } But trying a bunch of different prompts has not triggered the tool - instead, what appear to be totally random locations are filled in my resulting model and at no point does a breakpoint hit my tool code. Has anybody successfully gotten a tool to be called?

Attempted to download the Adapter Toolkit linked to from https://developer.apple.com/apple-intelligence/foundation-models-adapter/. Failed on all attempts, with a "403 Forbidden" error. I had accepted the agreement on the first attempt. How would we get access please?

Hi all, I'm working on an app to classify dog breeds via CoreML, but when I try training a model using Image Feature Print v2, I get the following error: Failed to create CVPixelBufferPool. Width = 0, Height = 0, Format = 0x00000000 Strangely, when I switch back to Image Feature Print v1, the model trains perfectly fine. I've verified that there aren't any invalid or broken images in my dataset. Is there a fix for this? Thanks!