Hello, I'm using videotoolbox superresolution API in MACOS 26: https://developer.apple.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.)

HI, I've been modifying the Camera sample app found here: https://developer.apple.com/tutorials/sample-apps/capturingphotos-camerapreview ... in the processpreview images, I am calling in to the Vision APis to either detect a person or object, then I'm using the segmentation mask to extract the person and composite them onto a different background with some other filters. I am using coreimage to filter the CIImages, and converting and displaying as a SwiftUI Image. When running on my IPhone, it works fine. When running on my Iphone with the debugger, it crashes within a few seconds... Attached is a screenshot. At the top is an 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<>>: This was working fine a couple of days ago.. Not sure why it's popping up now. Am I correct in interpreting this as an LLDB issue? How do I fix it?

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

I just recently updated to iOS 26 beta (23A5336a) to test an app I am developing I running an MLModel loaded from a .mlmodelc file. On the current iOS version 18.6.2 the model is running as expected with no issues. However on iOS 26 I am now getting error when trying to perform an inference to the model where I pass a camera frame into it. Below is the error I am seeing when I attempt to run an inference. at the bottom it says "Failed with status=0x1d : statusType=0x9: Program Inference error status=-1 Unable to compute the prediction using a neural network model. It can be an invalid input data or broken/unsupported model " does this indicate I need to convert my model or something? I don't understand since it runs as normal on iOS 18. Any help getting this to run again would be greatly appreciated. Thank you, processRequest:model:qos:qIndex:modelStringID:options:returnValue:error:: Could not process request ret=0x1d lModel=_ANEModel: { modelURL=file:///var/containers/Bundle/Application/04F01BF5-D48B-44EC-A5F6-3C7389CF4856/RizzCanvas.app/faceParsing.mlmodelc/ : sourceURL=(null) : UUID=46228BFC-19B0-45BF-B18D-4A2942EEC144 : key={"isegment":0,"inputs":{"input":{"shape":[512,512,1,3,1]}},"outputs":{"var_633":{"shape":[512,512,1,19,1]},"94_argmax_out_value":{"shape":[512,512,1,1,1]},"argmax_out":{"shape":[512,512,1,1,1]},"var_637":{"shape":[512,512,1,19,1]}}} : identifierSource=1 : cacheURLIdentifier=01EF2D3DDB9BA8FD1FDE18C7CCDABA1D78C6BD02DC421D37D4E4A9D34B9F8181_93D03B87030C23427646D13E326EC55368695C3F61B2D32264CFC33E02FFD9FF : string_id=0x00000000 : program=_ANEProgramForEvaluation: { programHandle=259022032430 : intermediateBufferHandle=13949 : queueDepth=127 } : state=3 : [Espresso::ANERuntimeEngine::__forward_segment 0] evaluate[RealTime]WithModel returned 0; code=8 err=Error Domain=com.apple.appleneuralengine Code=8 "processRequest:model:qos:qIndex:modelStringID:options:returnValue:error:: ANEProgramProcessRequestDirect() Failed with status=0x1d : statusType=0x9: Program Inference error" UserInfo={NSLocalizedDescription=processRequest:model:qos:qIndex:modelStringID:options:returnValue:error:: ANEProgramProcessRequestDirect() Failed with status=0x1d : statusType=0x9: Program Inference error} [Espresso::handle_ex_plan] exception=Espresso exception: "Generic error": ANEF error: /private/var/containers/Bundle/Application/04F01BF5-D48B-44EC-A5F6-3C7389CF4856/RizzCanvas.app/faceParsing.mlmodelc/model.espresso.net, processRequest:model:qos:qIndex:modelStringID:options:returnValue:error:: ANEProgramProcessRequestDirect() Failed with status=0x1d : statusType=0x9: Program Inference error status=-1 Unable to compute the prediction using a neural network model. It can be an invalid input data or broken/unsupported model (error code: -1). Error Domain=com.apple.Vision Code=3 "The VNCoreMLTransform request failed" UserInfo={NSLocalizedDescription=The VNCoreMLTransform request failed, NSUnderlyingError=0x114d92940 {Error Domain=com.apple.CoreML Code=0 "Unable to compute the prediction using a neural network model. It can be an invalid input data or broken/unsupported model (error code: -1)." UserInfo={NSLocalizedDescription=Unable to compute the prediction using a neural network model. It can be an invalid input data or broken/unsupported model (error code: -1).}}}

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.

@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")

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 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.

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'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?

Problem: We trained a LoRA adapter for Apple's FoundationModels framework using their TAMM (Training Adapter for Model Modification) toolkit v0.2.0 on macOS 26 beta 4. The adapter trains successfully but fails to load with: "Adapter is not compatible with the current system base model." TAMM 2.0 contains export/constants.py with: BASE_SIGNATURE = "9799725ff8e851184037110b422d891ad3b92ec1" Findings: Adapter Export Process: In export_fmadapter.py def write_metadata(...): self_dict[MetadataKeys.BASE_SIGNATURE] = BASE_SIGNATURE # Hardcoded value The Compatibility Check: - When loading an adapter, Apple's system compares the adapter's baseModelSignature with the current system model - If they don't match: compatibleAdapterNotFound error - The error doesn't reveal the expected signature Questions: - How is BASE_SIGNATURE derived from the base model? - Is it SHA-1 of base-model.pt or some other computation? - Can we compute the correct signature for beta 4? - Or do we need Apple to release TAMM v0.3.0 with updated signature?

Hey everyone, I am a beginner with developing and using Artificial Intelligence models. How do I integrate my createML image classification with swift. I already have have an ML model and I want to integrate it into a swiftUI app. If anyone could help, that would be great. Thank you, O3DP

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.

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.

import coremltools as ct from coremltools.models.neural_network import quantization_utils # load full precision model model_fp32 = ct.models.MLModel(modelPath) model_fp16 = quantization_utils.quantize_weights(model_fp32, nbits=16) model_fp16.save("reduced-model.mlmodel") I'm testing it with the model from one of Apple's source codes(GameBoardDetector), and it works fine, reduces the model size by half. But there are several problems with my model(trained on CreateML app using Full Network): Quantizing to float 16 does not work(new file gets created with reduced only 0.1mb). Quantizing to below 16 values cause errors, and no file gets created. Here are additional metadata and precisions of models. Working model's additional metadata and precision: Mine's additional metadata and precision:

Has anyone been able to run Tensorflow > 2.15 with Tensorflow Metal 1.1.0 on M3? I tried several times but was not successful. Seems like development on TensorFlow Metal has paused?

I have images, and I annotated with polygon, actually simple trapezoid, so 4 points. I have been trying and trying but can't get Create ML to work. I am trying Object Detection. I am not a real programmer so really would greatly appreciate some guidance to help to get this model created. I think I made a Detectron2 model, and tried to get that converted into a mlmodel I need for xcode but had troubles there also. thank you. { "annotation": "IMG_1803.JPG", "annotations": [ { "label": "court", "coordinates": { "x": [ 187, 3710, 2780, 929 ], "y": [ 1689, 1770, 478, 508 ] } } ] },

Dear Apple Foundation Models Development Team, I am a developer integrating Apple Foundation Models (AFM) into my app and encountered the exceededContextWindowSize error when exceeding the 4096-token limit. Proposal: I suggest Apple develop a tool to estimate the token count of a prompt before sending it to the model. This tool could be integrated into FoundationModels Framework for ease of use. Benefits: A token estimation tool would help developers manage the context window limit and optimize performance. I hope Apple considers this proposal soon. Thank you!

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!

I have a smallish image classifier I've been working on using the Create ML app. For a while everything was going fine, but lately, as the dataset has gotten larger, Create ML seems to stop during the testing phase with no error or test results. You can see here that there is no score in the result box, even though there are testing started and completed messages: No error message is shown in the Create ML app, but I do see these messages in the log: default 14:25:36.529887-0500 MLRecipeExecutionService [0x6000012bc000] activating connection: mach=false listener=false peer=false name=com.apple.coremedia.videodecoder default 14:25:36.529978-0500 MLRecipeExecutionService [0x41c5d34c0] activating connection: mach=false listener=true peer=false name=(anonymous) default 14:25:36.530004-0500 MLRecipeExecutionService [0x41c5d34c0] Channel could not return listener port. default 14:25:36.530364-0500 MLRecipeExecutionService [0x429a88740] activating connection: mach=false listener=false peer=true name=com.apple.xpc.anonymous.0x41c5d34c0.peer[1167].0x429a88740 default 14:25:36.534523-0500 MLRecipeExecutionService [0x6000012bc000] invalidated because the current process cancelled the connection by calling xpc_connection_cancel() default 14:25:36.534537-0500 MLRecipeExecutionService [0x41c5d34c0] invalidated because the current process cancelled the connection by calling xpc_connection_cancel() default 14:25:36.534544-0500 MLRecipeExecutionService [0x429a88740] invalidated because the current process cancelled the connection by calling xpc_connection_cancel() error 14:25:36.558788-0500 MLRecipeExecutionService CreateWithURL:342: *** ERROR: err=24 (Too many open files) - could not open '<CFURL 0x60000079b540 [0x1fdd32240]>{string = file:///Users/kevin/Library/Mobile%20Documents/com~apple~CloudDocs/Binary%20Formations/Under%20My%20Roof/Core%20ML%20Training%20Data/Household%20Items/Output/2025.01.23_12.55.16/Test/Stove/Test480.webp, encoding = 134217984, base = (null)}' default 14:25:36.559030-0500 MLRecipeExecutionService Error: <private> default 14:25:36.559077-0500 MLRecipeExecutionService Error: <private> Of particular interest is the "Too many open files" message from MLRecipeExecutionService referencing one of the test images. There are a total of 2,555 test images, which I wouldn't think would be a very large set. The system doesn't seem to be running out of memory or anything like that. Near the end of the test run there MLRecipeExecution service had 2934 file descriptors open according to lsof. Has anyone else run into this or know of a workaround? So far I've tried rebooting and recreating the Create ML project. Currently using Create ML Version 6.1 (150.3) on macOS 15.2 (24C101) running on a Mac Studio.