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.

hello, Do you have any information on the handling of sparse matrix with MPS and PyTorch? release date? ...

I'm running MacOs 26 Beta 5. I noticed that I can no longer achieve 100% usage on the ANE as I could before with Apple Foundations on-device model. Has Apple activated some kind of throttling or power limiting of the ANE? I cannot get above 3w or 40% usage now since upgrading. I'm on the high power energy mode. I there an API rate limit being applied? I kave a M4 Pro mini with 64 GB of memory.

I am follwing this tutorial: https://apple.github.io/coremltools/docs-guides/source/convert-a-torchvision-model-from-pytorch.html I have obtained simialr result using the python code. However when I view it in Xcode, the preview prediction percentage confidence is way off I suspect it is due the the output of the model, which is in percentage already and in Xcode it multiply 100 again leading to this result. Please give me any feedback to fix this, thank you.

Hi everyone, I’m currently exploring the use of Foundation models on Apple platforms to build a chatbot-style assistant within an app. While the integration part is straightforward using the new FoundationModel APIs, I’m trying to figure out how to control the assistant’s responses more tightly — particularly: Ensuring the assistant adheres to a specific tone, context, or domain (e.g. hospitality, healthcare, etc.) Preventing hallucinations or unrelated outputs Constraining responses based on app-specific rules, structured data, or recent interactions I’ve experimented with prompt, systemMessage, and few-shot examples to steer outputs, but even with carefully generated prompts, the model occasionally produces incorrect or out-of-scope responses. Additionally, when using multiple tools, I'm unsure how best to structure the setup so the model can select the correct pathway/tool and respond appropriately. Is there a recommended approach to guiding the model's decision-making when several tools or structured contexts are involved? Looking forward to hearing your thoughts or being pointed toward related WWDC sessions, Apple docs, or sample projects.

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!

The documentation for the Create ML tool ("Building an object detector data source") mentions that there are options for using normalized values instead of pixels and also different anchor point origins ("MLBoundingBoxCoordinatesOrigin") instead of always using "center". However, the JSON format for these does not appear in any examples. Does anyone know the format for these options?

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!

I have an app that uses a couple of mlmodels (word tagger and gazetteer) and I’m trying to encrypt them before publishing. The models are part of a package. I understand that Xcode can’t automatically handle the encryption for a model in a package the way it can within a traditional app structure. Given that, I’ve generated the Apple MLModel encryption key from Xcode and am encrypting via the command line with: xcrun coremlcompiler compile Gazetteer.mlmodel GazetteerENC.mlmodelc --encrypt Gazetteerkey.mlmodelkey In the package manifest, I’ve listed the encrypted models as .copy resources for my target and have verified the URL to that file is good. When I try to load the encrypted .mlmodelc file (on a physical device) with the line:
 gazetteer = try NLGazetteer(contentsOf: gazetteerURL!) I get the error: Failed to open file: /…/Scanner.bundle/GazetteerENC.mlmodelc/coremldata.bin. It is not a valid .mlmodelc file. So my questions are: Does the NLGazetteer class support encrypted MLModel files? Given that my models are in a package, do I have the right general approach? Thanks for any help or thoughts.

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?

With the release of the newest version of tahoe and MLX supporting RDMA. Is there a documentation link to how to utilizes the libdrma dylib as well as what functions are available? I am currently assuming it mostly follows the standard linux infiniband library but I would like the apple specific details.

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.

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.

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

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

It seems to be that Swift has more APIs implemented than the C++ interface (especially APIs found in the MLXNN and MLXOptimize folders). Is there any intention to implement more APIs for neural networks and training them in the future?

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!

In this WWDC25 session, it is explictely mentioned that apps should support AttributedString for text parameters to their App Intents. However, I have not gotten this to work. Whenever I pass rich text (either generated by the new "Use Model" intent or generated manually for example using "Make Rich Text from Markdown"), my Intent gets an AttributedString with the correct characters, but with all attributes stripped (so in effect just plain text). struct TestIntent: AppIntent { static var title = LocalizedStringResource(stringLiteral: "Test Intent") static var description = IntentDescription("Tests Attributed Strings in Intent Parameters.") @Parameter var text: AttributedString func perform() async throws -> some IntentResult & ReturnsValue<AttributedString> { return .result(value: text) } } Is there anything else I am missing?

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 }

Does anyone know if ExecuTorch is officially supported or has been successfully used on visionOS? If so, are there any specific build instructions, example projects, or potential issues (like sandboxing or memory limitations) to be aware of when integrating it into an Xcode project for the Vision Pro? While ExecuTorch has support for iOS, I can't find any official documentation or community examples specifically mentioning visionOS. Thanks.

Hi team, We have implemented a writing tool inside a WebView that allows users to type content in a textarea. When the "Show Writing Tools" button is clicked, an AI-powered editor opens. After clicking the "Rewrite" button, the AI modifies the text. However, when clicking the "Replace" button, the rewritten text does not update the original textarea. Kindly check and help me showButton.addTarget(self, action: #selector(showWritingTools(_:)), for: .touchUpInside) @available(iOS 18.2, *) optional func showWritingTools(_ sender: Any) Note: same cases working in TextView pfa