We have suddenly encountered a serious issue: our local ML models are no longer being decrypted. Everything was set up according to the guide at https://developer.apple.com/documentation/coreml/generating-a-model-encryption-key and had been working in production, but yesterday we started receiving the following error: Error Domain=com.apple.CoreML Code=8 "Fetching decryption key from server failed: noEntryFound("No records found"). Make sure the encryption key was generated with correct team ID." UserInfo={NSLocalizedDescription=Fetching decryption key from server failed: noEntryFound("No records found"). Make sure the encryption key was generated with correct team ID.} We haven’t changed anything in our code. This started spontaneously affecting users of the release version as of yesterday. It also no longer works locally — we receive the same error at the moment the autogenerated function is called: class func load(configuration: MLModelConfiguration = MLModelConfiguration(), completionHandler handler: @escaping (Swift.Result<ZingPDModel, Error>) -> Void) I assume that I can generate a new key through Xcode, integrate it in place of the old one, and it might start working again. However, this won’t affect existing users until they update the app. Could the issue be on Apple’s infrastructure side?

I couldn't find information about this in the documentation. Could someone clarify if this API is available and how to access it?

Hi friends, I have just found that the inference speed dropped to only 1/10 of the original model. Had anyone encountered this? Thank you.

Our app is downloading a zip of an .mlpackage file, which is then compiled into an .mlmodelc file using MLModel.compileModel(at:). This model is then run using a VNCoreMLRequest. Two users – and this after a very small rollout - are reporting issues running the VNCoreMLRequest. The error message from their logs: Error Domain=com.apple.CoreML Code=0 "Failed to build the model execution plan using a model architecture file '/private/var/mobile/Containers/Data/Application/F93077A5-5508-4970-92A6-03A835E3291D/Documents/SKDownload/Identify-image-iOS/mobile_img_eu_v210.mlmodelc/model.mil' with error code: -5." The URL there is to a file inside the compiled model. The error is happening when the perform function of VNImageRequestHandler is run. (i.e. the model compiled without an error.) Anyone else seen this issue? Its only picked up in a few web results and none of them are directly relevant or have a fix. I know that a CoreML error Code=0 is a generic error, but does anyone know what error code -5 is? Not even sure which framework its coming from.

How do I test the new RecognizeDocumentRequest API. Reference: https://www.youtube.com/watch?v=H-GCNsXdKzM I am running Xcode Beta, however I only have one primary device that I cannot install beta software on. Please provide a strategy for testing. Will simulator work? The new capability is critical to my application, just what I need for structuring document scans and extraction. Thank you.

v3 was released 2 years ago but developers are unable to convert models created with Keras v3 to CoreML

Hi, DataScannerViewController does't recognize currencies less than 1.00 (e.g. 0.59 USD, 0.99 EUR, etc.). Why? How to solve the problem? This feature is not described in Apple documentation, is there a solution? This is my code: func makeUIViewController(context: Context) -&gt; DataScannerViewController { let dataScanner = DataScannerViewController(recognizedDataTypes: [ .text(textContentType: .currency)]) return dataScanner }

Hi, I have an app that uses Core Data to store user information and display it in various views. I want to know if it's possible to easily integrate this setup with FoundationModels to make it easier for the user to query and manipulate the information, and if so, how would I go about it? Can the model be pointed to the database schema file and the SQLite file sitting in the user's app group container to parse out the information needed? And/or should the NSManagedObjects be made @Generable for better output? Any guidance about this would be useful.

Hi, I'm using LanguageModelSession and giving it two different tools to query data from a local database. I'm wondering how I can have the session generate structured content as the response that includes data one or both tools (or no tool at all). Here is an example of what I'm trying to do: Let's say the app has access to a database that contains information about exercise and sleep data (this is just an analogy). There are two tools, GetExerciseData() and GetSleepData(). The user may then prompt something like, "how well did I sleep in November". I have this working so that it calls through to the right tool, which would return a SleepSummary. However, I can't figure out how to have the session return the right structured data. I can do this and get back good text data: let response = session.respond(to: userInput), but I believe I want to do something like: let response = session.respond(to: trimmed, generating: <SomeStructure?>) Sometimes the model I run one tool or the other, or both tools, or no tool at all. Any help of what the right way to go about this would be much appreciated. Most of the example I found have to do with 1 tool.

Hi everyone, I'm a Mac enthusiast experimenting with tensorflow-metal on my Mac Pro (2013). My question is about GPU selection in tensorflow-metal (v0.8.0), which still supports Intel-based Macs, including my machine. I've noticed that when running TensorFlow with Metal, it automatically selects a GPU, regardless of what I specify using device indices like "gpu:0", "gpu:1", or "gpu:2". I'm wondering if there's a way to manually specify which GPU should be used via an environment variable or another method. For reference, I’ve tried the example from TensorFlow’s guide on multi-GPU selection: https://www.tensorflow.org/guide/gpu#using_a_single_gpu_on_a_multi-gpu_system My goal is to explore performance optimizations by using MirroredStrategy in TensorFlow to leverage multiple GPUs: https://www.tensorflow.org/guide/distributed_training#mirroredstrategy Interestingly, I discovered that the metalcompute Python library (https://pypi.org/project/metalcompute/) allows to utilize manually selected GPUs on my system, allowing for proper multi-GPU computations. This makes me wonder: Is there a hidden environment variable or setting that allows manual GPU selection in tensorflow-metal? Has anyone successfully used MirroredStrategy on multiple GPUs with tensorflow-metal? Would a bridge between metalcompute and tensorflow-metal be necessary for this use case, or is there a more direct approach? I’d love to hear if anyone else has experimented with this or has insights on getting finer control over GPU selection. Any thoughts or suggestions would be greatly appreciated! Thanks!

Hi all, I’m encountering an issue when trying to run Apple Foundation Models in a blank project targeting iOS 26. Below are the details: Xcode: Latest version with iOS 26 SDK macOS: macOS 26 Tahoe (installed on main disk) Mac: 16” MacBook Pro with M2 Pro chip Apple Intelligence: Available and functional on this machine Problem: I created a new blank iOS project, set the deployment target to iOS 26, and ran the following minimal code using Foundation Models. However, I get no response at all in the output - not even an error. The app runs, but the model does not produce any output. #Playground { let session = LanguageModelSession() let response = try await session.respond(to: "Tell me a story") } Then, I tried to catch an error with this code: #Playground { let session = LanguageModelSession() do { let response = try await session.respond(to: "Tell me a story") print(response) } catch { print("Failed to get response:", error) } print("This line, never gets executed") } And got these results: I’ve done further testing and discovered something important: I tried running the Code Along sample project, and there the #Playground macro worked without issues. The only significant difference I noticed was the Canvas run destination: In my original project, I was using iPhone 16 Pro (iOS 26) as the run target in Canvas. Apple Intelligence was enabled on the simulator, but no response was returned when executing the prompt. In the sample project, the Canvas was running on My Mac. I attempted to match that setup, but at first, my destination was My Mac (Designed for iPad), which still didn’t work. The macro finally executed properly once I switched to My Mac (AppKit). So the question is ... it seems that for now, Foundation Models and the #Playground macro only run correctly when the canvas or destination is set to “My Mac (AppKit)”?

Hello, I am developing an iOS app that uses machine learning models. To improve accuracy and user experience, I would like to download .mlmodel files (compiled and compressed as zip files) from our own server after the app is installed, and use them for inference within the app. No executable code, scripts, or dynamic libraries will be downloaded—only model data files are used. According to App Store Review Guideline 2.5.2, I understand that apps may not download or execute code which introduces or changes features or functionality. In this case, are compiled and zip-compressed .mlmodel files considered "data" rather than "code", and is it allowed to download and use them in the app? If there are any restrictions or best practices related to this, please let me know. Thank you.

Hi, One can configure the languages of a (VN)RecognizeTextRequest with either: .automatic: language to be detected a specific language, say Spanish If the request is configured with .automatic and successfully detects Spanish, will the results be exactly equivalent compared to a request made with Spanish set as language? I could not find any information about this, and this is very important for the core architecture of my app. Thanks!

Just tried to write a very simple test of using foundation models, but it gave me the error like this "ModelManager received unentitled request. Expected entitlement com.apple.modelmanager.inference establishment of session failed with Missing entitlement: com.apple.modelmanager.inference" The simple code is listed below: let session: LanguageModelSession = LanguageModelSession() let response = try? await session.respond(to: "What is the capital of France?") print("Response: (response)") So what's the problem of this one?

Hi Apple team, When using AppShortcutsProvider, I hit the hard limit: Each app may have at most 10 App Shortcuts. This feels limiting for apps that offer multiple workflows and would benefit from deeper Siri integration. Could this cap be raised — ideally to 30 — to support broader use of AppIntents, enhance Siri automation, and unlock more system-level capabilities? AppShortcuts are a fantastic tool. Increasing the limit would make them even more powerful. Thanks!

According to the Tool documentation, the arguments to the tool are specified as a static struct type T, which is given to tool.call(argument: T) However, if the arguments are not known until runtime, is it possible to still create a Tool object with the proper parameters? Let's say a JSON-style dictionary is passed into the Tool init function to specify T, is this achievable?

This is my code: witch SystemLanguageModel.default.availability { case .available: ContentView() .popover(isPresented: $showSettings) { SettingsView().presentationCompactAdaptation(.popover) } case .unavailable(.modelNotReady): ContentUnavailableView("Apple Intelligence is unavailable", systemImage: "apple.intelligence.badge.xmark", description: Text("Please come back later.")) case .unavailable(.appleIntelligenceNotEnabled): ContentUnavailableView("Apple Intelligence is unavailable", systemImage: "apple.intelligence.badge.xmark", description: Text("Please turn on Apple Intelligence.")) case .unavailable(.deviceNotEligible): ContentUnavailableView("Apple Intelligence is unavailable", systemImage: "apple.intelligence.badge.xmark", description: Text("This device is not eligible for Apple Intelligence.")) case .unavailable: ContentUnavailableView("Apple Intelligence is unavailable", systemImage: "apple.intelligence.badge.xmark") } When I switch off Apple Intelligence, I expected "Please turn on Apple Intelligence.", but instead I get "Please come back later." This seems to be wrong error?

I'm interested in using Foundation Models to act as an AI support agent for our extensive in-app documentation. We have many pages of in-app documents, which the user can currently search, but it would be great to use Foundation Models to let the user get answers to arbitrary questions. Is this possible with the current version of Foundation Models? It seems like the way to add new context to the model is with the instructions parameter on LanguageModelSession. As I understand it, the combined instructions and prompt need to consume less than 4096 tokens. That definitely wouldn't be enough for the amount of documentation I want the agent to be able to refer to. Is there another way of doing this, maybe as a series of recursive queries? If there is a solution based on multiple queries, should I expect this to be fast enough for interactive use?

Testing Foundation Models framework with a health-focused recipe generation app. The on-device approach is appealing but performance is rough. Taking 20+ seconds just to get recipe name and description. Same content from Claude API: 4 seconds. I know it's beta and on-device has different tradeoffs, but this is approaching unusable territory for real-time user experience. The streaming helps psychologically but doesn't mask the underlying latency.The privacy/cost benefits are compelling but not if users abandon the feature before it completes. Anyone else seeing similar performance? Is this expected for beta, or are there optimization techniques I'm missing?

I generate an array of random floats using the code shown below. However, I would like to do this with Double instead of Float. Are there any BNNS random number generators for double values, something like BNNSRandomFillUniformDouble? If not, is there a way I can convert BNNSNDArrayDescriptor from float to double? import Accelerate let n = 100_000_000 let result = Array<Float>(unsafeUninitializedCapacity: n) { buffer, initCount in var descriptor = BNNSNDArrayDescriptor(data: buffer, shape: .vector(n))! let randomGenerator = BNNSCreateRandomGenerator(BNNSRandomGeneratorMethodAES_CTR, nil) BNNSRandomFillUniformFloat(randomGenerator, &descriptor, 0, 1) initCount = n }