Hello, I am testing the sample project provided here: Bringing advanced speech-to-text capabilities to your app. On both macOS 26.0 beta and iOS 26.0 beta, the app crashes immediately on launch with a dyld "Symbol not found" error related to FoundationModels.framework. It feels like this may be related to testing primarily on newer Apple Silicon devices, as I am seeing consistent crashes on an Intel MacBook and on an older iPhone device. I would appreciate any insight, confirmation, or guidance on whether this is a known limitation or if there is a workaround. Is it planned to be resolved soon? Environment macOS: Device: MacBook Pro (Intel) Processor: 2 GHz Quad-Core Intel Core i5 Graphics: Intel Iris Plus Graphics 1536 MB Memory: 16 GB 3733 MHz LPDDR4X OS: macOS Tahoe Version 26.0 Beta (25A5338b) iOS: Device: iPhone 11 Model Number: MHDD3HN/A OS: iOS 26.0 Xcode: Version: 26.0 beta 3 (17A5276g) Crash (macOS) Abort signal received. Excerpt from crash dump: dyld`__abort_with_payload: 0x7ff80e3ad4a0 <+0>: movl $0x2000209, %eax 0x7ff80e3ad4a5 <+5>: movq %rcx, %r10 0x7ff80e3ad4a8 <+8>: syscall -> 0x7ff80e3ad4aa <+10>: jae 0x7ff80e3ad4b4 Console: dyld[9819]: Symbol not found: _$s16FoundationModels20LanguageModelSessionC5model10guardrails5tools12instructionsAcA06SystemcD0C_AC10GuardrailsVSayAA4Tool_pGAA12InstructionsVSgtcfC Referenced from: /Users/userx/Library/Developer/Xcode/DerivedData/SwiftTranscriptionSampleApp-*/Build/Products/Debug/SwiftTranscriptionSampleApp.app/Contents/MacOS/SwiftTranscriptionSampleApp.debug.dylib Expected in: /System/Library/Frameworks/FoundationModels.framework/Versions/A/FoundationModels Crash (iOS) Abort signal received. Excerpt from crash dump: dyld`__abort_with_payload: 0x18f22b4b0 <+0>: mov x16, #0x209 0x18f22b4b4 <+4>: svc #0x80 -> 0x18f22b4b8 <+8>: b.lo 0x18f22b4d8 Console dyld[2080]: Symbol not found: _$s16FoundationModels20LanguageModelSessionC5model10guardrails5tools12instructionsAcA06SystemcD0C_AC10GuardrailsVSayAA4Tool_pGAA12InstructionsVSgtcfC Referenced from: /private/var/containers/Bundle/Application/.../SwiftTranscriptionSampleApp.app/SwiftTranscriptionSampleApp.debug.dylib Expected in: /System/Library/Frameworks/FoundationModels.framework/FoundationModels Question Is this crash expected on Intel Macs and older iPhone models with the beta SDKs? Is there an official statement on whether macOS 26.x releases support Intel, or it exists only until macOS 26.1? Any suggested workarounds for testing this sample project on current hardware? Is this a known limitation for the 26.0 beta, and if so, should we expect a fix in 26.0 or only in subsequent releases? Attaching screenshots for reference. Thank you in advance.

Was just wondering why the foundation model documentation is no longer available, thanks! https://developer.apple.com/documentation/FoundationModels

Do we know what a safe max token limit is? After some iterating, I have come to believe 4096 might be the limit on device. Could you help me out by answering any of these questions: Is 4096 the correct limit? Do all devices have the same limit? Will the limit change over time or by device? The errors I get when going over the limit do not seem to say, hey you are over, so it's just by trial and error that I figure these issues out. Thanks for the fun new toys. Regards, Rob

Reading all the docs(1) I was under the impression that handling this error is well managed... Until I hit it and found out that the recommended handling options hide a crucial fact: in the catch block you can not do anything?! It's too late - everything is lost, no way to recover... All the docs mislead me that I can apply the Transcript trick in the catch block until I realised, that there is nothing there !!! This article here(2) enlightened me on the handling of this problem, but I must say (and the author as well) - this is a hack! So my questions: is there really no way to handle this exception properly? if not, can we have the most important information - the count of the context exposed through the official API (at least the known ones)? https://developer.apple.com/documentation/Technotes/tn3193-managing-the-on-device-foundation-model-s-context-window#Handle-the-exceeding-context-window-size-error-elegantly https://zats.io/blog/making-the-most-of-apple-foundation-models-context-window/

Environment: Hardware: Mac M4 OS: macOS Sequoia 15.7.4 TensorFlow-macOS Version: 2.16.2 TensorFlow-metal Version: 1.2.0 Description: When using the tensorflow-metal plug-in for GPU acceleration on M4, the ReLU activation function (both as a layer and as an activation argument) fails to correctly clip negative values to zero. The same code works correctly when forced to run on the CPU. Reproduction Script: import os import numpy as np import tensorflow as tf # weights and biases = -1 weights = [np.ones((10, 5)) * -1, np.ones(5) * -1] # input = 1 data = np.ones((1, 10)) # comment this line => GPU => get negative values # uncomment this line => CPU => no negative values # tf.config.set_visible_devices([], 'GPU') # create model model = tf.keras.Sequential([ tf.keras.layers.Input(shape=(10,)), tf.keras.layers.Dense(5, activation='relu') ]) # set weights model.layers[0].set_weights(weights) # get output output = model.predict(data) # check if negative is present print(f"min value: {output.min()}") print(f"is negative present? {np.any(output < 0)}")

Hello, I was successfully able to compile TKDKid1000/TinyLlama-1.1B-Chat-v0.3-CoreML using Core ML, and it's working well. However, I’m now trying to compile the same model using Swift Transformers. With the limited documentation available on the swift-chat and Hugging Face repositories, I’m finding it difficult to understand the correct process for compiling a model via Swift Transformers. I attempted the following approach, but I’m fairly certain it’s not the recommended or correct method. Could someone guide me on the proper way to compile and use models like TinyLlama with Swift Transformers? Any official workflow, example, or best practice would be very helpful. Thanks in advance! This is the approach I have used: import Foundation import CoreML import Tokenizers @main struct HopeApp { static func main() async { print(" Running custom decoder loop...") do { let tokenizer = try await AutoTokenizer.from(pretrained: "PY007/TinyLlama-1.1B-Chat-v0.3") var inputIds = tokenizer("this is the test of the prompt") print("🧠 Prompt token IDs:", inputIds) let model = try float16_model(configuration: .init()) let maxTokens = 30 for _ in 0..<maxTokens { let input = try MLMultiArray(shape: [1, 128], dataType: .int32) let mask = try MLMultiArray(shape: [1, 128], dataType: .int32) for i in 0..<inputIds.count { input[i] = NSNumber(value: inputIds[i]) mask[i] = 1 } for i in inputIds.count..<128 { input[i] = 0 mask[i] = 0 } let output = try model.prediction(input_ids: input, attention_mask: mask) let logits = output.logits // shape: [1, seqLen, vocabSize] let lastIndex = inputIds.count - 1 let lastLogitsStart = lastIndex * 32003 // vocab size = 32003 var nextToken = 0 var maxLogit: Float32 = -Float.greatestFiniteMagnitude for i in 0..<32003 { let logit = logits[lastLogitsStart + i].floatValue if logit > maxLogit { maxLogit = logit nextToken = i } } inputIds.append(nextToken) if nextToken == 32002 { break } let partialText = try await tokenizer.decode(tokens:inputIds) print(partialText) } } catch { print("❌ Error: \(error)") } } }

When the @Generable is applied toward a Swift struct declared within another struct, and when said nested struct is defined as the type of one of the properties of another @Generable type, which is in turn defined as the output format of Foundation Model session, Foundation Model can stuck in a loop trying to create a infinitely nested response, until the context window limit exceeded error is triggered. I have filed feedback FB19987191 with a demo project. Is this expected behavior?

Are there any guidelines for using Foundation Models To generate text for users in response to some canned queries? Should we use a special icon or text to let the user know that Apple Intelligence is generating the text? Should there be a disclaimer like, Apple Intelligence can make mistakes, please check for accuracy, etc?

I have integrated Apple’s Foundation Model into my iOS application. As known, Foundation Model is only supported starting from iOS 26 on compatible devices. To maintain compatibility with older iOS versions, I wrapped the API calls with the condition if #available(iOS 26, *). The application works normally on an iPad running iOS 18 and on a Mac running macOS 26. However, when running the same build on a MacBook Air M1 (macOS 15) through iPad app compatibility, the app crashes immediately upon launch. The main issue is that I cannot debug directly on macOS 15, since the app can only be built on macOS 26 with Xcode beta. I then have to distribute it via TestFlight and download it on the MacBook Air M1 for testing. This makes identifying the detailed cause of the crash very difficult and time-consuming. Nevertheless, I have confirmed that the crash is caused by the Foundation Model APIs.

Does CoreML object detection only support AABB (Axis-Aligned Bounding Boxes) or also OBB (Oriented Bounded Boxes)? If not, any way to do it using Apple frameworks?

I've tried creating a Lora adapter using the example dataset, scripts as part of the adapter_training_toolkit_v26_0_0 (last available) on MacOs 26 Beta 6. import SwiftUI import FoundationModels import Playgrounds #Playground { // The absolute path to your adapter. let localURL = URL(filePath: "/Users/syl/Downloads/adapter_training_toolkit_v26_0_0/train/test-lora.fmadapter") // Initialize the adapter by using the local URL. let adapter = try SystemLanguageModel.Adapter(fileURL: localURL) // An instance of the the system language model using your adapter. let customAdapterModel = SystemLanguageModel(adapter: adapter) // Create a session and prompt the model. let session = LanguageModelSession(model: customAdapterModel) let response = try await session.respond(to: "hello") } I get Adapter assets are invalid error. I've added the entitlements Is adapter_training_toolkit_v26_0_0 up to date?

I'm trying to train a text classifier model in Create ML. The Create ML app/framework offers five algorithms. I can successfully train the model with all of the algorithms except the BERT transfer learning option. When I select this algorithm, Create ML simply stops the training process immediately after the initial feature extraction phase (with no reported error). What I've tried: I tried simplifying the dataset to just a few classes and short examples in case there was a problem with the data. I tried experimenting with the number of iterations and language/script options. I checked Console.app for logged errors and found the following for the Create ML app: error 10:38:28.385778+0000 Create ML Couldn't read event column - category is invalid. Format string is : <private> error 10:38:30.902724+0000 Create ML Could not encode the entity <private>. Error: <private> I'm not sure if these errors are normal or indicative of a problem. I don't know what it means by the "event" column – I don't have an event column in my data and I don't believe there should be one. These errors are not reported when using the other algorithms. Given that I couldn't get the app to work with BERT, I switched over to the CreateML framework and followed the code samples given in the documentation. (By the way, there's an error in the docs: the line let (trainingData, testingData) = data.stratifiedSplit(on: "text", by: 0.8) should be stratifying on "label", not on "text"). The main chunk of code looks like this: var parameters = MLTextClassifier.ModelParameters( validation: .split(strategy: .automatic), algorithm: .transferLearning(.bertEmbedding, revision: 1), language: .english ) parameters.maxIterations = 100 let sentimentClassifier = try MLTextClassifier( trainingData: trainingData, textColumn: "text", labelColumn: "label", parameters: parameters ) Ultimately I want to train a single multilingual model, and I believe that BERT is the best choice for this. The problem is that there doesn't seem to be a way to choose the multilingual Latin script option in the API. In the Create ML app you can theoretically do this by selecting the Latin script with language set to "Automatic", as recommended in this WWDC video (relevant section starts at around 8:02). But, as far as I can tell, ModelParameters only lets you pick a specific language. I presume the framework must provide some way to do this, since the Create ML app uses the framework under the hood, but I can't see a way to do it. Another possibility is that the Create ML app might be misrepresenting the framework – perhaps selecting a specific language in the app doesn't actually make any difference – for example, maybe all Latin languages actually use the same model under the hood and the language selector is just there to guide people to the right choice (but this is just my speculation). Any help would be much appreciated! If possible, I'd prefer to use the Create ML app if I can get the BERT option to work – is this actually working for anyone? Or failing that, I want to use the framework to train a multilingual Latin model with BERT, so I'm looking for instructions on how to choose that specific option or confirmation that I can just choose .english to get the correct Latin multilingual model. I'm running Xcode 26.2 on Tahoe 21.1 on an M1 Pro MacBook Pro. I have version 6.2 of the Create ML app.

Foundation Models are driving me up the wall. My use case: A news app - I want to summarize news articles. Sounds like a perfect use for the added-in-beta-5 "no guardrails" mode for text-to-text transformations... ... and it's true, I don't get guardrails exceptions anymore but now, the model itself frequently refuses to summarize stuff which in a way is even worse as I have to parse the output text to figure out if it failed instead of getting an exception. I mostly worked that out with my system instructions but still, the refusing to summarize makes it really tough to use. I instructed the model to tell me why it failed if that happens. Examples of various refusals for news articles from major sources: "The article mentions "Visual Lookup" but does not provide details about how it integrates with iOS 26." "The article includes unsafe content regarding a political figure's potential influence over the Federal Reserve board, which is against my guidelines." "the article contains unsafe content." "The article is biased and opinionated and focuses on the author's opinion." (this is despite the instructions specifically asking for a neutral summary - I am asking it to not use bias in the output but it still refuses) I have tons of these. Note that if I don't use the "no guardrails" mode and use a Generable instead, some of these work fine so right now I have to do two passes on much of the content since I never know which one will work. Having a "summary mode" that often refuses to summarize current news articles (the world is not a great place, some of these stories are a bummer) is near worthless.

Hey everyone, Is it possible to generate XML using the “Generable” macro of the Foundation Model Framework?

I’m working on generating images using Image Playground. The code works fine for other styles but fails when using an external provider. I don’t see any other requirements mentioned in the documentation. Has anyone else encountered a similar issue? Here’s the relevant code snippet: https://developer.apple.com/documentation/imageplayground/imageplaygroundstyle/externalprovider?changes=_2 The error message is also not very helpful. It simply states that the creation failed. Note: I have enabled ChatGPT Plus, and the image generation using ChatGPT styles works fine when using the Playground app. do { let creator = try await ImageCreator() let concept = ImagePlaygroundConcept.text("Love") let images = creator.images(for: [concept], style: .externalProvider, limit: 1) for try await image in images { // Handle image break } } catch { // Handle error } I’m using the iOS 26 RC, and when I print creator.availableStyles, it doesn’t display the external Provider. [ImagePlayground.ImagePlaygroundStyle(id: "animation", _representationInfo: nil), ImagePlayground.ImagePlaygroundStyle(id: "emoji", _representationInfo: nil), ImagePlayground.ImagePlaygroundStyle(id: "illustration", _representationInfo: nil), ImagePlayground.ImagePlaygroundStyle(id: "sketch", _representationInfo: nil), ImagePlayground.ImagePlaygroundStyle(id: "messages-background", _representationInfo: nil)]

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!

Hi everyone, I am using Xcode 16.4 in MacOS Sequoia 15.5 with Apple Intelligence turned on. The following code gives the error message in the title: import NaturalLanguage @available(iOS 18.0, *) func testSystemModel() { let model = SystemLanguageModel.default print(model) } What am I missing?

I'm the creator of an app that helps users learn Arabic. Inside of the app users can save words, engage in lessons specific to certain grammar concepts etc. I'm looking for a way for Siri to 'suggest' my app when the user asks to define any Arabic words. There are other questions that I would like for Siri to suggest my app for, but I figure that's a good start. What framework am I looking for here? I think AppItents? I remember I played with it for a bit last year but didn't get far. Any suggestions would be great. Would the new Foundations model be any help here?

It is vital for Apple to refine its OCR models to correctly distinguish between Khmer and Thai scripts. Incorrectly labeling Khmer text as Thai is more than a technical bug; it is a culturally insensitive error that impacts national identity, especially given the current geopolitical climate between Cambodia and Thailand. Implementing a more robust language-detection threshold would prevent these harmful misidentifications. There is a significant logic flaw in the VNRecognizeTextRequest language detection when processing Khmer script. When the property automaticallyDetectsLanguage is set to true, the Vision framework frequently misidentifies Khmer characters as Thai. While both scripts share historical roots, they are distinct languages with different alphabets. Currently, the model’s confidence threshold for distinguishing between these two scripts is too low, leading to incorrect OCR output in both developer-facing APIs and Apple’s native ecosystem (Preview, Live Text, and Photos). import SwiftUI import Vision class TextExtractor { func extractText(from data: Data, completion: @escaping (String) -> Void) { let request = VNRecognizeTextRequest { (request, error) in guard let observations = request.results as? [VNRecognizedTextObservation] else { completion("No text found.") return } let recognizedStrings = observations.compactMap { observation in let str = observation.topCandidates(1).first?.string return "{text: \(str!), confidence: \(observation.confidence)}" } completion(recognizedStrings.joined(separator: "\n")) } request.automaticallyDetectsLanguage = true // <-- This is the issue. request.recognitionLevel = .accurate let handler = VNImageRequestHandler(data: data, options: [:]) DispatchQueue.global(qos: .background).async { do { try handler.perform([request]) } catch { completion("Failed to perform OCR: \(error.localizedDescription)") } } } } Recognizing Khmer Confidence Score is low for Khmer text. (The output is in Thai language with low confidence score) Recognizing English Confidence Score is high expected. Recognizing Thai Confidence Score is high as expected Issues on Preview, Photos Khmer text Copied text Kouk Pring Chroum Temple [19121 รอาสายสุกตีนานยารรีสใหิสรราภูชิตีนนสุฐตีย์ [รุก เผือชิษาธอยกัตธ์ตายตราพาษชาณา ถวเชยาใบสราเบรถทีมูสินตราพาษชาณา ทีมูโษา เช็ก อาษเชิษฐอารายสุกบดตพรธุรฯ ตากร"สุก"ผาตากรธกรธุกเยากสเผาพศฐตาสาย รัอรณาษ"ตีพย" สเผาพกรกฐาภูชิสาเครๆผู:สุกรตีพาสเผาพสรอสายใผิตรรารตีพสๆ เดียอลายสุกตีน ธาราชรติ ธิพรหณาะพูชุบละเาหLunet De Lajonquiere ผารูกรสาราพารผรผาสิตภพ ตารสิทูก ธิพิ คุณที่นสายเระพบพเคเผาหนารเกะทรนภาษเราภุพเสารเราษทีเลิกสญาเราหรุฬารชสเกาก เรากุม สงสอบานตรเราะากกต่ายภากายระตารุกเตียน Recommended Solutions 1. Set a Threshold Filter out the detected result where the threshold is less than or equal to 0.5, so that it would not output low quality text which can lead to the issue. For example, let recognizedStrings = observations.compactMap { observation in if observation.confidence <= 0.5 { return nil } let str = observation.topCandidates(1).first?.string return "{text: \(str!), confidence: \(observation.confidence)}" } 2. Add Khmer Language Support This issue would never happen if the model has the capability to detect and recognize image with Khmer language. Doc2Text GitHub: https://github.com/seanghay/Doc2Text-Swift

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.