At WWDC25 we launched a new type of Lab event for the developer community - Group Labs. A Group Lab is a panel Q&A designed for a large audience of developers. Group Labs are a unique opportunity for the community to submit questions directly to a panel of Apple engineers and designers. Here are the highlights from the WWDC25 Group Lab for Machine Learning and AI Frameworks. What are you most excited about in the Foundation Models framework? The Foundation Models framework provides access to an on-device Large Language Model (LLM), enabling entirely on-device processing for intelligent features. This allows you to build features such as personalized search suggestions and dynamic NPC generation in games. The combination of guided generation and streaming capabilities is particularly exciting for creating delightful animations and features with reliable output. The seamless integration with SwiftUI and the new design material Liquid Glass is also a major advantage. When should I still bring my own LLM via CoreML? It's generally recommended to first explore Apple's built-in system models and APIs, including the Foundation Models framework, as they are highly optimized for Apple devices and cover a wide range of use cases. However, Core ML is still valuable if you need more control or choice over the specific model being deployed, such as customizing existing system models or augmenting prompts. Core ML provides the tools to get these models on-device, but you are responsible for model distribution and updates. Should I migrate PyTorch code to MLX? MLX is an open-source, general-purpose machine learning framework designed for Apple Silicon from the ground up. It offers a familiar API, similar to PyTorch, and supports C, C++, Python, and Swift. MLX emphasizes unified memory, a key feature of Apple Silicon hardware, which can improve performance. It's recommended to try MLX and see if its programming model and features better suit your application's needs. MLX shines when working with state-of-the-art, larger models. Can I test Foundation Models in Xcode simulator or device? Yes, you can use the Xcode simulator to test Foundation Models use cases. However, your Mac must be running macOS Tahoe. You can test on a physical iPhone running iOS 18 by connecting it to your Mac and running Playgrounds or live previews directly on the device. Which on-device models will be supported? any open source models? The Foundation Models framework currently supports Apple's first-party models only. This allows for platform-wide optimizations, improving battery life and reducing latency. While Core ML can be used to integrate open-source models, it's generally recommended to first explore the built-in system models and APIs provided by Apple, including those in the Vision, Natural Language, and Speech frameworks, as they are highly optimized for Apple devices. For frontier models, MLX can run very large models. How often will the Foundational Model be updated? How do we test for stability when the model is updated? The Foundation Model will be updated in sync with operating system updates. You can test your app against new model versions during the beta period by downloading the beta OS and running your app. It is highly recommended to create an "eval set" of golden prompts and responses to evaluate the performance of your features as the model changes or as you tweak your prompts. Report any unsatisfactory or satisfactory cases using Feedback Assistant. Which on-device model/API can I use to extract text data from images such as: nutrition labels, ingredient lists, cashier receipts, etc? Thank you. The Vision framework offers the RecognizeDocumentRequest which is specifically designed for these use cases. It not only recognizes text in images but also provides the structure of the document, such as rows in a receipt or the layout of a nutrition label. It can also identify data like phone numbers, addresses, and prices. What is the context window for the model? What are max tokens in and max tokens out? The context window for the Foundation Model is 4,096 tokens. The split between input and output tokens is flexible. For example, if you input 4,000 tokens, you'll have 96 tokens remaining for the output. The API takes in text, converting it to tokens under the hood. When estimating token count, a good rule of thumb is 3-4 characters per token for languages like English, and 1 character per token for languages like Japanese or Chinese. Handle potential errors gracefully by asking for shorter prompts or starting a new session if the token limit is exceeded. Is there a rate limit for Foundation Models API that is limited by power or temperature condition on the iPhone? Yes, there are rate limits, particularly when your app is in the background. A budget is allocated for background app usage, but exceeding it will result in rate-limiting errors. In the foreground, there is no rate limit unless the device is under heavy load (e.g., camera open, game mode). The system dynamically balances performance, battery life, and thermal conditions, which can affect the token throughput. Use appropriate quality of service settings for your tasks (e.g., background priority for background work) to help the system manage resources effectively. Do the foundation models support languages other than English? Yes, the on-device Foundation Model is multilingual and supports all languages supported by Apple Intelligence. To get the model to output in a specific language, prompt it with instructions indicating the user's preferred language using the locale API (e.g., "The user's preferred language is en-US"). Putting the instructions in English, but then putting the user prompt in the desired output language is a recommended practice. Are larger server-based models available through Foundation Models? No, the Foundation Models API currently only provides access to the on-device Large Language Model at the core of Apple Intelligence. It does not support server-side models. On-device models are preferred for privacy and for performance reasons. Is it possible to run Retrieval-Augmented Generation (RAG) using the Foundation Models framework? Yes, it is possible to run RAG on-device, but the Foundation Models framework does not include a built-in embedding model. You'll need to use a separate database to store vectors and implement nearest neighbor or cosine distance searches. The Natural Language framework offers simple word and sentence embeddings that can be used. Consider using a combination of Foundation Models and Core ML, using Core ML for your embedding model.

Hi everyone, I'm working with VNFeaturePrintObservation in Swift to compute the similarity between images. The computeDistance function allows me to calculate the distance between two images, and I want to cluster similar images based on these distances. Current Approach Right now, I'm using a brute-force approach where I compare every image against every other image in the dataset. This results in an O(n^2) complexity, which quickly becomes a bottleneck. With 5000 images, it takes around 10 seconds to complete, which is too slow for my use case. Question Are there any efficient algorithms or data structures I can use to improve performance? If anyone has experience with optimizing feature vector clustering or has suggestions on how to scale this efficiently, I'd really appreciate your insights. Thanks!

Hello everyone, I’m looking for guidance regarding my app review timeline, as things seem unusually delayed compared to previous submissions. My iOS app was rejected on November 19th due to AI-related policy questions. I immediately responded to the reviewer with detailed explanations covering: Model used (Gemini Flash 2.0 / 2.5 Lite) How the AI only generates neutral, non-directive reflective questions How the system prevents any diagnosis, therapy-like behavior or recommendations Crisis-handling limitations Safety safeguards at generation and UI level Internal red-team testing and results Data retention, privacy, and non-use of data for model training After sending the requested information, I resubmitted the build on November 19th at 14:40. Since then: November 20th (7:30) → Status changed to In Review. November 21st, 22nd, 23rd, 24th, 25th → No movement, still In Review. My open case on App Store Connect is still pending without updates. Because of the previous rejection, I expected a short delay, but this is now 5 days total and 3 business days with no progress, which feels longer than usual for my past submissions. I’m not sure whether: My app is in a secondary review queue due to the AI-related rejection, The reviewer is waiting for internal clarification, Or if something is stuck and needs to be escalated. I don’t want to resubmit a new build unless necessary, since that would restart the queue. Could someone from the community (or Apple, if possible) confirm whether this waiting time is normal after an AI-policy rejection? And is there anything I should do besides waiting — for example, contacting Developer Support again or requesting a follow-up? Thank you very much for your help. I appreciate any insight from others who have experienced similar delays.

I'm developing a tennis ball tracking feature using Vision Framework in Swift, specifically utilizing VNDetectedObjectObservation and VNTrackObjectRequest. Occasionally (but not always), I receive the following runtime error: Failed to perform SequenceRequest: Error Domain=com.apple.Vision Code=9 "Internal error: unexpected tracked object bounding box size" UserInfo={NSLocalizedDescription=Internal error: unexpected tracked object bounding box size} From my investigation, I suspect the issue arises when the bounding box from the initial observation (VNDetectedObjectObservation) is too small. However, Apple's documentation doesn't clearly define the minimum bounding box size that's considered valid by VNTrackObjectRequest. Could someone clarify: What is the minimum acceptable bounding box width and height (normalized) that Vision Framework's VNTrackObjectRequest expects? Is there any recommended practice or official guidance for bounding box size validation before creating a tracking request? This information would be extremely helpful to reliably avoid this internal error. Thank you!

My app used app intents. And when user said "Prüfung der Bluetooth Funktion", screen can show the whole words. But in my app, it only can get "Bluetooth Funktion". This behaviour only happened in German version. In English version, everything worked well. Is anyone can support me? Why German version siri cut my words?

Hello, I am currently developing an application that requires barcode scanning using Apple’s Vision framework (VNBarcodeSymbology). I noticed that the framework supports several GS1 DataBar symbologies, such as: VNBarcodeSymbology.gs1DataBar VNBarcodeSymbology.gs1DataBarExpanded VNBarcodeSymbology.gs1DataBarLimited However, I could not find any explicit reference to support for GS1 DataBar Stacked (both regular and expanded variants). Could you confirm whether GS1 DataBar Stacked is currently supported in VisionKit's DataScannerViewController or VNBarcodeObservation? If not, are there any plans to include support for this symbology in a future iOS update? This functionality is critical for my use case, as GS1 DataBar Stacked barcodes are widely used in retail, pharmaceuticals, and logistics, where space constraints prevent the use of standard GS1 DataBar formats. I appreciate any clarification on this matter and would be happy to provide additional details if needed.

Hello, I am interested in using jax-metal to train ML models using Apple Silicon. I understand this is experimental. After installing jax-metal according to https://developer.apple.com/metal/jax/, my python code fails with the following error JaxRuntimeError: UNKNOWN: -:0:0: error: unknown attribute code: 22 -:0:0: note: in bytecode version 6 produced by: StableHLO_v1.12.1 My issue is identical to the one reported here https://github.com/jax-ml/jax/issues/26968#issuecomment-2733120325, and is fixed by pinning to jax-metal 0.1.1., jax 0.5.0 and jaxlib 0.5.0. Thank you!

Hello, I have to create an app in Swift that it scan NFC Identity card. It extract data and convert it to human readable data. I do it with below code import CoreNFC class NFCIdentityCardReader: NSObject , NFCTagReaderSessionDelegate { func tagReaderSessionDidBecomeActive(_ session: NFCTagReaderSession) { print("\(session.description)") } func tagReaderSession(_ session: NFCTagReaderSession, didInvalidateWithError error: any Error) { print("NFC Error: \(error.localizedDescription)") } var session: NFCTagReaderSession? func beginScanning() { guard NFCTagReaderSession.readingAvailable else { print("NFC is not supported on this device") return } session = NFCTagReaderSession(pollingOption: .iso14443, delegate: self, queue: nil) session?.alertMessage = "Hold your NFC identity card near the device." session?.begin() } func tagReaderSession(_ session: NFCTagReaderSession, didDetect tags: [NFCTag]) { guard let tag = tags.first else { session.invalidate(errorMessage: "No tag detected") return } session.connect(to: tag) { (error) in if let error = error { session.invalidate(errorMessage: "Connection error: \(error.localizedDescription)") return } switch tag { case .miFare(let miFareTag): self.readMiFareTag(miFareTag, session: session) case .iso7816(let iso7816Tag): self.readISO7816Tag(iso7816Tag, session: session) case .iso15693, .feliCa: session.invalidate(errorMessage: "Unsupported tag type") @unknown default: session.invalidate(errorMessage: "Unknown tag type") } } } private func readMiFareTag(_ tag: NFCMiFareTag, session: NFCTagReaderSession) { // Read from MiFare card, assuming it's formatted as an identity card let command: [UInt8] = [0x30, 0x04] // Example: Read command for block 4 let requestData = Data(command) tag.sendMiFareCommand(commandPacket: requestData) { (response, error) in if let error = error { session.invalidate(errorMessage: "Error reading MiFare: \(error.localizedDescription)") return } let readableData = String(data: response, encoding: .utf8) ?? response.map { String(format: "%02X", $0) }.joined() session.alertMessage = "ID Card Data: \(readableData)" session.invalidate() } } private func readISO7816Tag(_ tag: NFCISO7816Tag, session: NFCTagReaderSession) { let selectAppCommand = NFCISO7816APDU(instructionClass: 0x00, instructionCode: 0xA4, p1Parameter: 0x04, p2Parameter: 0x00, data: Data([0xA0, 0x00, 0x00, 0x02, 0x47, 0x10, 0x01]), expectedResponseLength: -1) tag.sendCommand(apdu: selectAppCommand) { (response, sw1, sw2, error) in if let error = error { session.invalidate(errorMessage: "Error reading ISO7816: \(error.localizedDescription)") return } let readableData = response.map { String(format: "%02X", $0) }.joined() session.alertMessage = "ID Card Data: \(readableData)" session.invalidate() } } } But I got null. I think that these data are encrypted. How can I convert them to readable data without MRZ, is it possible ? I need to get personal informations from Identity card via Core NFC. Thanks in advance. Best regards

I am writing a custom package wrapping Foundation Models which provides a chain-of-thought with intermittent self-evaluation among other things. At first I was designing this package with the command line in mind, but after seeing how well it augments the models and makes them more intelligent I wanted to try and build a SwiftUI wrapper around the package. When I started I was using synchronous generation rather than streaming, but to give the best user experience (as I've seen in the WWDC sessions) it is necessary to provide constant feedback to the user that something is happening. I have created a super simplified example of my setup so it's easier to understand. First, there is the Reasoning conversation item, which can be converted to an XML representation which is then fed back into the model (I've found XML works best for structured input) public typealias ConversationContext = XMLDocument extension ConversationContext { public func toPlainText() -> String { return xmlString(options: [.nodePrettyPrint]) } } /// Represents a reasoning item in a conversation, which includes a title and reasoning content. /// Reasoning items are used to provide detailed explanations or justifications for certain decisions or responses within a conversation. @Generable(description: "A reasoning item in a conversation, containing content and a title.") struct ConversationReasoningItem: ConversationItem { @Guide(description: "The content of the reasoning item, which is your thinking process or explanation") public var reasoningContent: String @Guide(description: "A short summary of the reasoning content, digestible in an interface.") public var title: String @Guide(description: "Indicates whether reasoning is complete") public var done: Bool } extension ConversationReasoningItem: ConversationContextProvider { public func toContext() -> ConversationContext { // <ReasoningItem title="${title}"> // ${reasoningContent} // </ReasoningItem> let root = XMLElement(name: "ReasoningItem") root.addAttribute(XMLNode.attribute(withName: "title", stringValue: title) as! XMLNode) root.stringValue = reasoningContent return ConversationContext(rootElement: root) } } Then there is the generator, which creates a reasoning item from a user query and previously generated items: struct ReasoningItemGenerator { var instructions: String { """ <omitted for brevity> """ } func generate(from input: (String, [ConversationReasoningItem])) async throws -> sending LanguageModelSession.ResponseStream<ConversationReasoningItem> { let session = LanguageModelSession(instructions: instructions) // build the context for the reasoning item out of the user's query and the previous reasoning items let userQuery = "User's query: \(input.0)" let reasoningItemsText = input.1.map { $0.toContext().toPlainText() }.joined(separator: "\n") let context = userQuery + "\n" + reasoningItemsText let reasoningItemResponse = try await session.streamResponse( to: context, generating: ConversationReasoningItem.self) return reasoningItemResponse } } I'm not sure if returning LanguageModelSession.ResponseStream<ConversationReasoningItem> is the right move, I am just trying to imitate what session.streamResponse returns. Then there is the orchestrator, which I can't figure out. It receives the streamed ConversationReasoningItems from the Generator and is responsible for streaming those to SwiftUI later and also for evaluating each reasoning item after it is complete to see if it needs to be regenerated (to keep the model on-track). I want the users of the orchestrator to receive partially generated reasoning items as they are being generated by the generator. Later, when they finish, if the evaluation passes, the item is kept, but if it fails, the reasoning item should be removed from the stream before a new one is generated. So in-flight reasoning items should be outputted aggresively. I really am having trouble figuring this out so if someone with more knowledge about asynchronous stuff in Swift, or- even better- someone who has worked on the Foundation Models framework could point me in the right direction, that would be awesome!

@property (assign,nonatomic) long long experimentalMLE5EngineUsage; //@synthesize experimentalMLE5EngineUsage=_experimentalMLE5EngineUsage - In the implementation block What is it, and why would disabling it fix NMS for a MLProgram? Is there anyway to signal this flag from model metadata? Is there anyway to signal or disable from a global, system-level scope? It's extremely easy to reproduce, but do not know how to investigate the drastic regression between toggling this flag let config = MLModelConfiguration() config.setValue(1, forKey: "experimentalMLE5EngineUsage")

I’m trying to group my EntityPropertyQuery selection into sections as well as making it searchable. I know that the EntityStringQuery is used to perform the text search via entities(matching string: String). That works well enough and results in this modal: Though, when I’m using a DynamicOptionsProvider to section my EntityPropertyQuery, it doesn’t allow for searching anymore and simply opens the sectioned list in a menu like so: How can I combine both? I’ve seen it in other apps, but can’t figure out why my code doesn’t allow to section the results and make it searchable? Any ideas? My code (simplified) struct MyIntent: AppIntent { @Parameter(title: "Meter"), optionsProvider: MyOptionsProvider()) var meter: MyIntentEntity? // … struct MyOptionsProvider: DynamicOptionsProvider { func results() async throws -> ItemCollection<MyIntentEntity> { // Get All Data let allData = try IntentsDataHandler.shared.getEntities() // Create Arrays for Sections let fooEntities = allData.filter { $0.type == .foo } let barEntities = allData.filter { $0.type == .bar } return ItemCollection(sections: [ ItemSection("Foo", items: fooEntities), ItemSection("Bar", items: barEntities) ]) } } struct MeterIntentQuery: EntityStringQuery { // entities(for identifiers: [UUID]) and suggestedEntities() functions func entities(matching string: String) async throws -> [MyIntentEntity] { // Fetch All Data let allData = try IntentsDataHandler.shared.getEntities() // Filter Data by String let matchingData = allData.filter { data in return data.title.localizedCaseInsensitiveContains(string)) } return matchingData } }

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!

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")

We are really excited to have introduced the Foundation Models framework in WWDC25. When using the framework, you might have feedback about how it can better fit your use cases. Starting in macOS/iOS 26 Beta 4, the best way to provide feedback is to use #Playground in Xcode. To do so: In Xcode, create a playground using #Playground. Fore more information, see Running code snippets using the playground macro. Reproduce the issue by setting up a session and generating a response with your prompt. In the canvas on the right, click the thumbs-up icon to the right of the response. Follow the instructions on the pop-up window and submit your feedback by clicking Share with Apple. Another way to provide your feedback is to file a feedback report with relevant details. Specific to the Foundation Models framework, it’s super important to add the following information in your report: Language model feedback This feedback contains the session transcript, including the instructions, the prompts, the responses, etc. Without that, we can’t reason the model’s behavior, and hence can hardly take any action. Use logFeedbackAttachment(sentiment:issues:desiredOutput: ) to retrieve the feedback data of your current model session, as shown in the usage example, write the data into a file, and then attach the file to your feedback report. If you believe what you’d report is related to the system configuration, please capture a sysdiagnose and attach it to your feedback report as well. The framework is still new. Your actionable feedback helps us evolve the framework quickly, and we appreciate that. Thanks, The Foundation Models framework team

When I ran the following code on a physical iPhone device that supports Apple Intelligence, I encountered the following error log. What does this internal error code mean? Image generation failed with NSError in a different domain: Error Domain=ImagePlaygroundInternal.ImageGeneration.GenerationError Code=11 “(null)”, returning a generic error instead let imageCreator = try await ImageCreator() let style = imageCreator.availableStyles.first ?? .animation let stream = imageCreator.images(for: [.text("cat")], style: style, limit: 1) for try await result in stream { // error: ImagePlayground.ImageCreator.Error.creationFailed _ = result.cgImage }

Hi, I am modifying the sample camera app that is here: https://developer.apple.com/tutorials/sample-apps/capturingphotos-camerapreview ... In the processPreviewImages, I am using the Vision APIs to generate a segmentation mask for a person/object, then compositing that person onto a different background (with some other filtering). The filtering and compositing is done via CoreImage. At the end, I convert the CIImage to a CGImage then to a SwiftUI Image. When I run it on my iPhone, it works fine, and has not crashed. When I run it on the iPhone with the debugger, it crashes within a few seconds with: EXC_BAD_ACCESS in libRPAC.dylib`std::__1::__hash_table<std::__1::__hash_value_type<long, qos_info_t>, std::__1::__unordered_map_hasher<long, std::__1::__hash_value_type<long, qos_info_t>, std::__1::hash, std::__1::equal_to, true>, std::__1::__unordered_map_equal<long, std::__1::__hash_value_type<long, qos_info_t>, std::__1::equal_to, std::__1::hash, true>, std::__1::allocator<std::__1::__hash_value_type<long, qos_info_t>>>::__emplace_unique_key_args<long, std::__1::piecewise_construct_t const&, std::__1::tuple<long const&>, std::__1::tuple<>>: It had previously been working fine with the debugger, so I'm not sure what has changed. Is there a difference in how the Vision APIs are executed if the debugger is attached vs. not?

Hi all, I'm trying to find out if/when we can expect mxfp8/mxfp4 support on Apple Silicon. I've noticed that mlx now has casting data types, but all computation is still done in bf16. Would be great to reduce power consumption with support for these lower precision data types since edge inference is already typically done at a lower precision! Thanks in advance.

I'm adding Visual Intelligence support to my app, and now want to add a Tip using TipKit to guide users to this feature from within my app. I want to add a Rule to my Tip which will only show this Tip on devices where Visual Intelligence is supported (ex. not iPhone 14 Pro Max). What is the best way for me to determine availability to set this TipKit rule? Here's the documentation I'm following for Visual Intelligence: https://developer.apple.com/documentation/visualintelligence/integrating-your-app-with-visual-intelligence

I have seen inconsistent results for my Colab machine learning notebooks running locally on a Mac M4, compared to running the same notebook code on either T4 (in Colab) or a RTX3090 locally. To illustrate the problems I have set up a notebook that implements two simple CNN models that solves the Fashion-MNIST problem. https://colab.research.google.com/drive/11BhtHhN079-BWqv9QvvcSD9U4mlVSocB?usp=sharing For the good model with 2M parameters I get the following results: T4 (Colab, JAX): Test accuracy: 0.925 3090 (Local PC via ssh tunnel, Jax): Test accuracy: 0.925 Mac M4 (Local, JAX): Test accuracy: 0.893 Mac M4 (Local, Tensorflow): Test accuracy: 0.893 That is, I see a significant drop in performance when I run on the Mac M4 compared to the NVIDIA machines, and it seems to be independent of backend. I however do not know how to pinpoint this to either Keras or Apple’s METAL implementation. I have reported this to Keras: https://colab.research.google.com/drive/11BhtHhN079-BWqv9QvvcSD9U4mlVSocB?usp=sharing but as this can be (likely is?) an Apple Metal issue, I wanted to report this here as well. On the mac I am running the following Python libraries: keras 3.9.1 tensorflow 2.19.0 tensorflow-metal 1.2.0 jax 0.5.3 jax-metal 0.1.1 jaxlib 0.5.3

I'm implementing an LLM with Metal Performance Shader Graph, but encountered a very strange behavior, occasionally, the model will report an error message as this: LLVM ERROR: SmallVector unable to grow. Requested capacity (9223372036854775808) is larger than maximum value for size type (4294967295) and crash, the stack backtrace screenshot is attached. Note that 5th frame is mlir::getIntValues<long long> and 6th frame is llvm::SmallVectorBase<unsigned int>::grow_pod It looks like mlir mistakenly took a 64 bit value for a 32 bit type. Unfortunately, I could not found the source code of mlir::getIntValues, maybe it's Apple's closed source fork of llvm for MPS implementation? Anyway, any opinion or suggestion on that?

I have been working on a small CV program, which uses fine-tuned U2Netp model converted by coremltools 8.3.0 from PyTorch. It works well on my iPhone (with iOS version 18.5) and my Macbook (with MacOS version 15.3.1). But it fails to load after I upgraded Macbook to MacOS version 15.5. I have attached console log when loading this model. Unable to load MPSGraphExecutable from path /Users/yongzhang/Library/Caches/swiftmetal/com.apple.e5rt.e5bundlecache/24F74/E051B28C6957815C140A86134D673B5C015E79A1460E9B54B8764F659FDCE645/16FA8CF2CDE66C0C427F4B51BBA82C38ACC44A514CCA396FD7B281AAC087AB2F.bundle/H14C.bundle/main/main_mps_graph/main_mps_graph.mpsgraphpackage @ GetMPSGraphExecutable E5RT: Unable to load MPSGraphExecutable from path /Users/yongzhang/Library/Caches/swiftmetal/com.apple.e5rt.e5bundlecache/24F74/E051B28C6957815C140A86134D673B5C015E79A1460E9B54B8764F659FDCE645/16FA8CF2CDE66C0C427F4B51BBA82C38ACC44A514CCA396FD7B281AAC087AB2F.bundle/H14C.bundle/main/main_mps_graph/main_mps_graph.mpsgraphpackage (13) Unable to load MPSGraphExecutable from path /Users/yongzhang/Library/Caches/swiftmetal/com.apple.e5rt.e5bundlecache/24F74/E051B28C6957815C140A86134D673B5C015E79A1460E9B54B8764F659FDCE645/16FA8CF2CDE66C0C427F4B51BBA82C38ACC44A514CCA396FD7B281AAC087AB2F.bundle/H14C.bundle/main/main_mps_graph/main_mps_graph.mpsgraphpackage @ GetMPSGraphExecutable E5RT: Unable to load MPSGraphExecutable from path /Users/yongzhang/Library/Caches/swiftmetal/com.apple.e5rt.e5bundlecache/24F74/E051B28C6957815C140A86134D673B5C015E79A1460E9B54B8764F659FDCE645/16FA8CF2CDE66C0C427F4B51BBA82C38ACC44A514CCA396FD7B281AAC087AB2F.bundle/H14C.bundle/main/main_mps_graph/main_mps_graph.mpsgraphpackage (13) Failure translating MIL->EIR network: Espresso exception: "Network translation error": MIL->EIR translation error at /Users/yongzhang/CLionProjects/ImageSimilarity/models/compiled/u2netp.mlmodelc/model.mil:1557:12: Parameter binding for axes does not exist. [Espresso::handle_ex_plan] exception=Espresso exception: "Network translation error": MIL->EIR translation error at /Users/yongzhang/CLionProjects/ImageSimilarity/models/compiled/u2netp.mlmodelc/model.mil:1557:12: Parameter binding for axes does not exist. status=-14 Failed to build the model execution plan using a model architecture file '/Users/yongzhang/CLionProjects/ImageSimilarity/models/compiled/u2netp.mlmodelc/model.mil' with error code: -14.