Hi, unfortunately I am not able to verify this but I remember some time ago I was able to create CoreML models that had one (or more) inputs with an enumerated shape size, and one (or more) inputs with a static shape. This was some months ago. Since then I updated my MacOS to Sequoia 15.5, and when I try to execute MLModels with this setup I get the following error libc++abi: terminating due to uncaught exception of type CoreML::MLNeuralNetworkUtilities::AsymmetricalEnumeratedShapesException: A model doesn't allow input features with enumerated flexibility to have unequal number of enumerated shapes, but input feature global_write_indices has 1 enumerated shapes and input feature input_hidden_states has 3 enumerated shapes. It may make sense (but not really though) to verify that for inputs with a flexible enumerated shape they all have the same number of possible shapes is the same, but this should not impede the possibility of also having static shape inputs with a single shape defined alongside the flexible shape inputs.

Is foundation models matured enough to take input from the Apple Vision framework to generate responses? Something similar to what google's gemini does although in a much smaller scale and for a very specific niche.

Application is getting Crashed: AXSpeech EXC_BAD_ACCESS KERN_INVALID_ADDRESS 0x000056f023efbeb0 Crashed: AXSpeech 0 libobjc.A.dylib 0x4820 objc_msgSend + 32 1 libsystem_trace.dylib 0x6c34 _os_log_fmt_flatten_object + 116 2 libsystem_trace.dylib 0x5344 _os_log_impl_flatten_and_send + 1884 3 libsystem_trace.dylib 0x4bd0 _os_log + 152 4 libsystem_trace.dylib 0x9c48 _os_log_error_impl + 24 5 TextToSpeech 0xd0a8c _pcre2_xclass_8 6 TextToSpeech 0x3bc04 TTSSpeechUnitTestingMode 7 TextToSpeech 0x3f128 TTSSpeechUnitTestingMode 8 AXCoreUtilities 0xad38 -[NSArray(AXExtras) ax_flatMappedArrayUsingBlock:] + 204 9 TextToSpeech 0x3eb18 TTSSpeechUnitTestingMode 10 TextToSpeech 0x3c948 TTSSpeechUnitTestingMode 11 TextToSpeech 0x48824 AXAVSpeechSynthesisVoiceFromTTSSpeechVoice 12 TextToSpeech 0x49804 AXAVSpeechSynthesisVoiceFromTTSSpeechVoice 13 Foundation 0xf6064 __NSThreadPerformPerform + 264 14 CoreFoundation 0x37acc CFRUNLOOP_IS_CALLING_OUT_TO_A_SOURCE0_PERFORM_FUNCTION + 28 15 CoreFoundation 0x36d48 __CFRunLoopDoSource0 + 176 16 CoreFoundation 0x354fc __CFRunLoopDoSources0 + 244 17 CoreFoundation 0x34238 __CFRunLoopRun + 828 18 CoreFoundation 0x33e18 CFRunLoopRunSpecific + 608 19 Foundation 0x2d4cc -[NSRunLoop(NSRunLoop) runMode:beforeDate:] + 212 20 TextToSpeech 0x24b88 TTSCFAttributedStringCreateStringByBracketingAttributeWithString 21 Foundation 0xb3154 NSThread__start + 732 com.livingMedia.AajTakiPhone_issue_3ceba855a8ad2d1af83655803dc13f70_crash_session_9081fa41ced440ae9a57c22cb432f312_DNE_0_v2_stacktrace.txt 22 libsystem_pthread.dylib 0x24d4 _pthread_start + 136 23 libsystem_pthread.dylib 0x1a10 thread_start + 8

I keep getting the error “An unsupported language or locale was used.” Is there any documentation that specifies the accepted languages or locales in Foundation model?

I am trying to use @AppIntent(schema: .system.search) to search in my app via a Siri voice command, but I want to be able to return a .result that does not open the app, yet still get the model training benefits from the schema. Very new to this, this is my first app, so I would appreciate some guidance. I haven't gotten to the voice part, I tested on Shortcuts. Do I need to do AppIntents without the schema and wait until there is a search schema that does not open the app, or should I be using a different schema? What am I missing?

I am trying to benchmark and see if the Qwen3 1.7B model can run in an iPhone SE 3 [4 GB RAM]. My core problem is - Even with weight quantization the SE 3 is not able to load into memory. What I've tried: I am converting a Torch model to the Core ML format using coremltools. I have tried the following combinations of quantization and context length 8 bit + 1024 8 bit + 2048 4 bit + 1024 4 bit + 2048 All the above quantizations are done with dynamic shape with the default being [1,1] in the hope that the whole context length does not get allocated in memory The 4-bit model is approximately 865MB on disk The 8-bit model is approximately 1.7 GB on disk During load: With the int4 quantization the memory spikes during intitial load a lot. Could this be because many operations are converted to int8 or fp16 as core ML does not perform operations natively on int4? With int8 on the profiler the memory does not go above 2 GB (only 900 MB) but it is still not able to load as it shows the following error. 2GB is the limit where jetsam kills the app for the iPhone SE 3 E5RT: Error(s) occurred compiling MIL to BNNS graph: [CreateBnnsGraphProgramFromMIL]: BNNS Graph Compile: failed to preallocate file with error: No space left on device for path: /var/mobile/Containers/Data/Application/ 5B8BB7D2-06A6-4BAE-A042-407B6D805E7C/Library/Caches /com.tss.qwen3-coreml/ com.apple.e5rt.e5bundlecache/ 23A341/<long key>.tmp.12586_4362093968.bundle/ H14.bundle/main/main_bnns/bnns_program.bnnsir Some online sources have suggested activation quantization but I am unsure if that will have any impact on loading [as the spike is during load and not inference] The model spec also suggests that there is no dequantization happening (for e.g from 4 bit -> fp16) So I had couple of queries: Has anyone faced similar issues? What could be the reasons for the temporary memory spike during LOAD What are approaches that can be adopted to deal with this issue? Any help would be greatly appreciated. Thank you.

Hi We're on tensorflow 2.20 that has support now for python 3.13 (finally!). tensorflow-metal is still only supporting 2.18 which is over a year old. When can we expect to see support in tensorflow-metal for tf 2.20 (or later!) ? I bought a mac thinking I would be able to get great performance from the M processors but here I am using my CPU for my ML projects. If it's taking so long to release it, why not open source it so the community can keep it more up to date? cheers Matt

Hello, I'm using videotoolbox superresolution API in MACOS 26: https://aninterestingwebsite.com/documentation/videotoolbox/vtsuperresolutionscalerconfiguration/downloadconfigurationmodel(completionhandler:)?language=objc, when using swift, it's ok, when using objective-c, I get error when downloading model with downloadConfigurationModelWithCompletionHandler: [Auto] MA-auto{_failedLockContent} | failure reported by server | error:[com.apple.MobileAssetError.AutoAsset:MissingReference(6111)] [Auto] MA-auto{_failedLockContent} | failure reported by server | error:[com.apple.MobileAssetError.AutoAsset:UnderlyingError(6107)_1_com.apple.MobileAssetError.Download:47] Download completion handler called with error: The operation couldnxe2x80x99t be completed. (VTFrameProcessorErrorDomain error -19743.)

I opened a new project, iOS app, in XCode and then tabbed into the system_search snippet and built the project and got a build error. I can't imagine this was intended, at least not for new developers to the ecosystem like me. I solved it by tweaking a configuration I don't really understand advised here: https://github.com/apple/swift-openapi-generator/issues/796, hopefully that's a valid workaround

I downloaded Xcode Beta 1 on my mac (did not upgrade the OS). The target OS level of iOS26 and the device simulator for iOS26 is downloaded and selected as the target. When I try a simple Playground in Xcode ( #Playground ) I get a session error. #Playground { let avail = SystemLanguageModel.default.availability if avail != .available { print("SystemLanguageModel not available") return } let session = LanguageModelSession() do { let response = try await session.respond(to: "Create a recipe for apple pie") } catch { print(error) } } The error I get is: Asset com.apple.gm.safety_deny_input.foundation_models.framework.api not found in Model Catalog Is there a way to test drive the FoundationModel code without upgrading to macos26?

Hi all, I'm capturing a photo using AVCapturePhotoOutput, and I've set: let photoSettings = AVCapturePhotoSettings() photoSettings.isDepthDataDeliveryEnabled = true Then I create the handler like this: let data = photo.fileDataRepresentation() let handler = try ImageRequestHandler(data: data, orientation: .right) Now I’m wondering: If depth data delivery is enabled, is it actually included and used when I pass the Data to ImageRequestHandler? Or do I need to explicitly pass the depth data using the other initializer? let handler = try ImageRequestHandler( cvPixelBuffer: photo.pixelBuffer!, depthData: photo.depthData, orientation: .right ) In short: Does ImageRequestHandler(data:) make use of embedded depth info from AVCapturePhoto.fileDataRepresentation() — or is the pixel buffer + explicit depth data required? Thanks for any clarification!

Hello Apple Developer Community, I'm investigating Core ML model loading behavior and noticed that even when the compiled model path remains unchanged after an APP update, the first run still triggers an "uncached load" process. This seems to impact user experience with unnecessary delays. Question: Does Core ML provide any public API to check whether a compiled model (from a specific .mlmodelc path) is already cached in the system? If such API exists, we'd like to use it for pre-loading decision logic - only perform background pre-load when the model isn't cached. Has anyone encountered similar scenarios or found official solutions? Any insights would be greatly appreciated!

I have a mac (M4, MacBook Pro) running Tahoe 26.0 beta. I am running Xcode beta. I can run code that uses the LLM in a #Preview { }. But when I try to run the same code in the simulator, I get the 'device not ready' error and I see the following in the Settings app. Is there anything I can do to get the simulator to past this point and allowing me to test on it with Apple's LLM?

I am trying to test FoundationModels in a Swift Playground in Xcode 26.2, macOS 26.3, and am running into an issue. The following simple code generates an error: import FoundationModels @Generable struct Specifications { @Guide(description: "Search for color") var color: String } I see the following error message in the console: error: AIPlayground.playground:4:8: external macro implementation type 'FoundationModelsMacros.GenerableMacro' could not be found for macro 'Generable(description:)'; plugin for module 'FoundationModelsMacros' not found The Xcode editor does not appear to recognize the @Generable or @Guide macros, despite importing FoundationModels. What step/setting am I missing?

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 get the following dyld error on an iPad Pro with Xcode 26 beta 4: Symbol not found: _$s16FoundationModels20LanguageModelSessionC7prewarm12promptPrefixyAA6PromptVSg_tF Any advice?

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)") } } }

I am trying to create a slightly different version of the content tagging code in the documentation: https://aninterestingwebsite.com/documentation/foundationmodels/systemlanguagemodel/usecase/contenttagging In the playground I am getting an "Inference Provider crashed with 2:5" error. I have no idea what that means or how to address the error. Any assistance would be appreciated.

Hey, I receive GenerableContent as follows: let response = try await session.respond(to: "", schema: generationSchema) And it wraps GeneratedJSON which seems to be private. What is the best way to get a string / raw value out of it? I noticed it could theoretically be accessed via transcriptEntries but it's not ideal.

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?