Originally, I set my iOS deployment target to 18.1, but now that I'm integrating Foundational Models, I set it to iOS 26.0. Is this ok?

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://aninterestingwebsite.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/

Hi, I'm trying to add FoundationModels to an older project but always get the following error: "Unable to resolve 'dependency' 'FoundationModels' import FoundationModels" The error comes and goes while its compiling and then doesn't run the app. I have my target set to 26.0 (and can't go any higher) and am using Xcode 26 (17E192). Is anyone else having this issue? Thanks, Dan Uff

Overview I'm experiencing a critical issue where TensorFlow-metal and PyArrow seem to be incompatible when installed together in the same environment. Whenever both packages are present, TensorFlow crashes and the kernel dies during execution. Environment Details Environment Details macOS Version: 15.3.2 Mac Model: MacBook Pro Max M3 Python Version: 3.11 TensorFlow Version: 2.19 PyArrow Version: 19.0.0 Issue Description: When both TensorFlow-metal and PyArrow are installed in the same Python environment, any attempt to use TensorFlow results in immediate kernel crashes. The issue appears to be a compatibility problem between these two packages rather than a problem with either package individually. Steps to Reproduce Create a new Python environment: conda create -n tf-metal python=3.11 Install TensorFlow-metal: pip install tensorflow tensorflow-metal Install PyArrow: pip install pyarrow Run the following minimal example: # Create a simple model model = tf.keras.Sequential([ tf.keras.layers.Input(shape=(2,)), tf.keras.layers.Dense(1) ]) model.compile(optimizer='adam', loss='mse') model.summary() # This works fine # Generate some dummy data X = np.random.random((100, 2)) y = np.random.random((100, 1)) # The crash happens exactly at this line model.fit(X, y, epochs=5, batch_size=32) # CRASH: Kernel dies here Result: Kernel crashes with no error message What I've Tried Reinstalling both packages in different orders Using different versions of both packages Creating isolated environments Checking system logs for additional error information The only workaround I've found is to use separate environments for each package, which isn't practical for my workflow as I need both libraries for my data processing and machine learning pipeline. Questions Has anyone else encountered this specific compatibility issue? Are there known workarounds that allow both packages to coexist? Is this a known issue that's being addressed in upcoming releases? Any insights, suggestions, or assistance would be greatly appreciated. I'm happy to provide any additional information that might help diagnose this problem. Thank you in advance for your help! Thank you in advance for your help!

As we described on the title, the model that I have built completely works on iPhone 15 / A16 Bionic, on the other hand it does not run on iPhone 16 / A18 chip with the following error message. E5RT encountered an STL exception. msg = MILCompilerForANE error: failed to compile ANE model using ANEF. Error=_ANECompiler : ANECCompile() FAILED. E5RT: MILCompilerForANE error: failed to compile ANE model using ANEF. Error=_ANECompiler : ANECCompile() FAILED (11) It consumes 1.5 ~ 1.6 GB RAM on the loading the model, then the consumption is decreased to less than 100MB on the both of iPhone 15 and 16. After that, only on iPhone 16, the above error is shown on the Xcode log, the memory consumption is surged to 5 to 6GB, and the system kills the app. It works well only on iPhone 15. This model is built with the Core ML tools. Until now, I have tried the target iOS 16 to 18 and the compute units of CPU_AND_NE and ALL. But any ways have not solved this issue. Eventually, what kindof fix should I do? minimum_deployment_target = ct.target.iOS18 compute_units = ct.ComputeUnit.ALL compute_precision = ct.precision.FLOAT16

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!

When I am doing an uncached load of CoreML model on ANE, I received this warning in Xcode console Type of hiddenStates in function main's I/O contains unknown strides. Using unknown strides for MIL tensor buffers with unknown shapes is not recommended in E5ML. Please use row_alignment_in_bytes property instead. Refer to https://e5-ml.apple.com/more-info/memory-layouts.html for more information. However, the web link does not seem to be working. Where can I find more information about about this and how can I fix it?

About the Core ML model encryption mention in:https://aninterestingwebsite.com/documentation/coreml/encrypting-a-model-in-your-app Ｗhen I encrypted the model, if the machine is M chip, the model will load perfectly. One the other hand, when I test the executable on an Intel chip macbook, there will be an error: Error Domain=com.apple.CoreML Code=9 "Operation not supported on this platform." UserInfo={NSLocalizedDescription=Operation not supported on this platform.} Intel test machine is 2019 macbook air with CPU: Intel i5-8210Y, OS: 14.7.6 23H626, With Apple T2 Security Chip. The encrypted model do load on M2 and M4 macbook air. If the model is NOT encrypted, it will also load on the Intel test machine. I did not find in Core ML document that suggest if the encryption/decryption support Intel chips. May I check if the decryption indeed does NOT support Intel chip?

Hello everyone, I have a visual convolutional model and a video that has been decoded into many frames. When I perform inference on each frame in a loop, the speed is a bit slow. So, I started 4 threads, each running inference simultaneously, but I found that the speed is the same as serial inference, every single forward inference is slower. I used the mactop tool to check the GPU utilization, and it was only around 20%. Is this normal? How can I accelerate it?

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.

After exerting a custom model with nms=True. In Xcode, the outputs show as: confidence: MultiArray (0 × 5) coordinates: MultiArray (0 × 4) I want to set fixed shapes (e.g., 100 × 5, 100 × 4), but Xcode does not allow editing—the shape fields are locked. The model graph shows both outputs come directly from a NonMaximumSuppression layer. Is it possible to set fixed output dimensions for NMS outputs in CoreML?

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)}")

Area: CoreML / Machine Learning Describe the issue: On iOS 26.4, calling MLModel(contentsOf:configuration:) to load an .mlpackage model hangs indefinitely and eventually kills the app via watchdog. The same model loads and runs inference successfully in under 1 second on iOS 26.3.1. The hang occurs inside eort_eo_compiler_compile_from_ir_program (espresso) during on-device AOT recompilation triggered by MLE5ProgramLibraryOnDeviceAOTCompilationImpl createProgramLibraryHandleWithRespecialization:error:. A C++ exception (__cxa_throw) is thrown inside libBNNS.dylib during the exception unwind, which then hangs inside __cxxabiv1::dyn_cast_slow and __class_type_info::search_below_dst. Swift's try/catch does not catch this — the exception originates in C++ and the process hangs rather than terminating cleanly. Setting config.computeUnits = .cpuOnly does not resolve the issue. MLE5ProgramLibrary initialises as shared infrastructure regardless of compute units. Steps to reproduce: Create an app with an .mlpackage CoreML model using the MLE5/espresso backend Call MLModel(contentsOf: modelURL, configuration: config) at runtime Run on a device on iOS 26.3.1 — loads successfully in <1 second Update device to iOS 26.4 — hangs indefinitely, app killed by watchdog after 60–745 seconds Expected behaviour: Model loads successfully, or throws a catchable Swift error on failure. Actual behaviour: Process hangs in MLE5ProgramLibrary.lazyInitQueue. App killed by watchdog. No Swift error thrown. Full stack trace at point of hang: Thread 1 Queue: com.apple.coreml.MLE5ProgramLibrary.lazyInitQueue (serial) frame 0: __cxxabiv1::__class_type_info::search_below_dst libc++abi.dylib frame 1: __cxxabiv1::(anonymous namespace)::dyn_cast_slow libc++abi.dylib frame 2: ___lldb_unnamed_symbol_23ab44dd4 libBNNS.dylib frame 23: eort_eo_compiler_compile_from_ir_program espresso frame 24: -[MLE5ProgramLibraryOnDeviceAOTCompilationImpl createProgramLibraryHandleWithRespecialization:error:] CoreML frame 25: -[MLE5ProgramLibrary _programLibraryHandleWithForceRespecialization:error:] CoreML frame 26: __44-[MLE5ProgramLibrary prepareAndReturnError:]_block_invoke CoreML frame 27: _dispatch_client_callout libdispatch.dylib frame 28: _dispatch_lane_barrier_sync_invoke_and_complete libdispatch.dylib frame 29: -[MLE5ProgramLibrary prepareAndReturnError:] CoreML frame 30: -[MLE5Engine initWithContainer:configuration:error:] CoreML frame 31: +[MLE5Engine loadModelFromCompiledArchive:modelVersionInfo:compilerVersionInfo:configuration:error:] CoreML frame 32: +[MLLoader _loadModelWithClass:fromArchive:modelVersionInfo:compilerVersionInfo:configuration:error:] CoreML frame 45: +[MLModel modelWithContentsOfURL:configuration:error:] CoreML frame 46: @nonobjc MLModel.__allocating_init(contentsOf:configuration:) GKPersonalV2 frame 47: MDNA_GaitEncoder_v1_3.__allocating_init(contentsOf:configuration:) frame 48: MDNA_GaitEncoder_v1_3.__allocating_init(configuration:) frame 50: GaitModelInference.loadModel() frame 51: GaitModelInference.init() iOS version: Reproduced on iOS 26.4. Works correctly on iOS 26.3.1. Xcode version: 26.2 Device: iPhone (model used in testing) Model format: .mlpackage

I’m developing an activity classifier that I’d like to input using the JSON format of CoreMotion data. I am getting the error: Unable to parse /Users/DewG/Downloads/Testing/Step1/Testing.json. It does not appear to be in JSON record format. A SequenceType of dictionaries is expected I've verified that the format I am using is JSON via various JSON validators, so I am expecting I'm just holding it wrong. Is there an example of a JSON file with CoreMotion data that I can model after?

After watching the What's new in App Intents session I'm attempting to create an intent conforming to URLRepresentableIntent. The video states that so long as my AppEntity conforms to URLRepresentableEntity I should not have to provide a perform method . My application will be launched automatically and passed the appropriate URL. This seems to work in that my application is launched and is passed a URL, but the URL is in the form: FeatureEntity/{id}. Am I missing something, or is there a trick that enables it to pass along the URL specified in the AppEntity itself? struct MyExampleIntent: OpenIntent, URLRepresentableIntent { static let title: LocalizedStringResource = "Open Feature" static var parameterSummary: some ParameterSummary { Summary("Open \(\.$target)") } @Parameter(title: "My feature", description: "The feature to open.") var target: FeatureEntity } struct FeatureEntity: AppEntity { // ... } extension FeatureEntity: URLRepresentableEntity { static var urlRepresentation: URLRepresentation { "https://myurl.com/\(.id)" } }

Hi, One can configure the languages of a (VN)RecognizeTextRequest with either: .automatic: language to be detected a specific language, say Spanish If the request is configured with .automatic and successfully detects Spanish, will the results be exactly equivalent compared to a request made with Spanish set as language? I could not find any information about this, and this is very important for the core architecture of my app. Thanks!

Hi everyone! 👋 I'm working on a C++ project using TensorFlow Lite and was wondering if anyone has a prebuilt TensorFlow Lite C++ library (libtensorflowlite) for macOS (Apple Silicon M1/M2) that they’d be willing to share. I’m looking specifically for the TensorFlow Lite C++ API — something that lets me use tflite::Interpreter, tflite::FlatBufferModel, etc. Building it from source using Bazel on macOS has been quite challenging and time-consuming, so a ready-to-use .dylib or .a build along with the required headers would be incredibly helpful. TensorFlow Lite version: v2.18.0 preferred Target: macOS arm64 (Apple Silicon) What I need: libtensorflowlite.dylib or .a Corresponding headers (ideally organized in a clean include/ folder) If you have one available or know where I can find a reliable prebuilt version, I’d be super grateful. Thanks in advance! 🙏

The WWDC25: Explore large language models on Apple silicon with MLX video talks about using your own data to fine-tune a large language model. But the video doesn't explain what kind of data can be used. The video just shows the command to use and how to point to the data folder. Can I use PDFs, Word documents, Markdown files to train the model? Are there any code examples on GitHub that demonstrate how to do this?

WWDC25: Combine Metal 4 machine learning and graphics Demonstrated a way to combine neural network in the graphics pipeline directly through the shaders, using an example of Texture Compression. However there is no mention of using which ML technique texture is compressed. Can anyone point me to some well known model/s for this particular use case shown in WWDC25.

I have a question. In China, long pressing a picture in the album can segment the target. Is this model a local model? Is there any information? Can developers use it?