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, 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

I'm trying to use Apple's new Visual Intelligence API for recommending content through screenshot image search. The problem I encountered is that the SemanticContentDescriptor labels are either completely empty or super misleading, making it impossible to query for similar content on my app. Even the closest matching example was inaccurate, returning a single label ["cardigan"] for a Supreme T-Shirt. I see other apps using this API like Etsy for example, and I'm wondering if they're using the input pixel buffer to query for similar content rather than using the labels? If anyone has a similar experience or something that wasn't called out in the documentation please lmk! Thanks.

Seeing this error from time to time: Context(debugDescription: "Content contains 4089 tokens, which exceeds the maximum allowed context size of 4096.", underlyingErrors: []) Of course, 4089 is less than 4096 so what is this telling me and how do I work around it? Is the limit actually lower than 4096?

I have been using "apple" to test foundation models. I thought this is local, but today the answer changed - half way through explanation, suddenly guardrailViolation error was activated! And yesterday, all reference to "Apple II", "Apple III" now refers me to consult apple.com! Does foundation models connect to Internet for answer? Using beta 3.

I installed Xcode 26.0 beta and downloaded the generative models sample from here: https://developer.apple.com/documentation/foundationmodels/adding-intelligent-app-features-with-generative-models But when I run it in the iOS 26.0 simulator, I get the error shown here. What's going wrong?

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 can no longer achieve 100% ANE usage since upgrading to MacOS26 Beta 5. I used to be able to get 100%. Has Apple activated throttling or power saving features in the new Betas? Is there any new rate limiting on the API? I can hardly get above 3w or 40%. I have a M4 Pro mini (64GB) with High Power energy setting. MacOS 26 Beta 5.

I’m keep looking around documentation and some sample codes but still haven’t found example of how was used this type of Network Regressor . Does it take some special parameters to perform on ANE , what size,format of DataFrame ?

Hey Tried using a few regular expressions and all fail with an error: Unhandled error streaming response: A generation guide with an unsupported pattern was used. Is there are a list of supported features? I don't see it in docs, and it takes RegExp. Anything with e.g. [A-Z] fails.

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.

We are developing Apple AI for overseas markets and adapting it for iPhone 17 and later models. When the system language and Siri language do not match—such as the system being in English while Siri is in Chinese—it may result in Apple AI being unusable. So, I would like to ask, how can this issue be resolved, and are there other reasons that might cause it to be unusable within the app?

I'm experimenting with the new SpeechTranscriber in macOS/iOS 26, transcribing speech from a prerecorded mp4 file. Speed and quality are amazing! I've told the transcriber to include time indexes. Each run is always exactly one word, which can be very useful. When I look at the indexes the end of one run is always identical to the start of the next run, even if there's a pause. I'd like to identify pauses, perhaps to generate something like phrases for subtitling. With each run of text going into the next I can't do this, other than using punctuation - which might be rather rough. Any suggestions on detecting pauses, or getting that kind of metadata from the transcriber? Here's a short sample, showing each run with the start, end, and characters in the run: 105.9 --> 107.04 I 107.04 --> 107.16 think 107.16 --> 108.0 more 108.0 --> 108.42 lighting 108.42 --> 108.6 is 108.6 --> 108.72 definitely 108.72 --> 109.2 needed, 109.2 --> 109.92 downtown. 109.98 --> 110.4 My 110.4 --> 110.52 only 110.52 --> 110.7 question 110.7 --> 111.06 is, 111.06 --> 111.48 poll 111.48 --> 111.78 five, 111.78 --> 111.84 that 111.84 --> 112.08 you're 112.08 --> 112.38 increasing 112.38 --> 112.5 the 112.5 --> 113.34 50,000? 113.4 --> 113.58 Where 113.58 --> 113.88 exactly

I've spent way too long today trying to convert an Object Detection TensorFlow2 model to a CoreML object classifier (with bounding boxes, labels and probability score) The 'SSD MobileNet v2 320x320' is here: https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf2_detection_zoo.md And I've been following all sorts of posts and ChatGPT https://apple.github.io/coremltools/docs-guides/source/tensorflow-2.html#convert-a-tensorflow-concrete-function https://developer.apple.com/videos/play/wwdc2020/10153/?time=402 To convert it. I keep hitting the same errors though, mostly around: NotImplementedError: Expected model format: [SavedModel | concrete_function | tf.keras.Model | .h5 | GraphDef], got <ConcreteFunction signature_wrapper(input_tensor) at 0x366B87790> I've had varying success including missing output labels/predictions. But I simply want to create the CoreML model with all the right inputs and outputs (including correct names) as detailed in the docs here: https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/running_on_mobile_tf2.md It goes without saying I don't have much (any) experience with this stuff including Python so the whole thing's been a bit of a headache. If anyone is able to help that would be great. FWIW I'm not attached to any one specific model, but what I do need at minimum is a CoreML model that can detect objects (has to at least include lights and lamps) within a live video image, detecting where in the image the object is. The simplest script I have looks like this: import coremltools as ct import tensorflow as tf model = tf.saved_model.load("~/tf_models/ssd_mobilenet_v2_320x320_coco17_tpu-8/saved_model") concrete_func = model.signatures[tf.saved_model.DEFAULT_SERVING_SIGNATURE_DEF_KEY] mlmodel = ct.convert( concrete_func, source="tensorflow", inputs=[ct.TensorType(shape=(1, 320, 320, 3))] ) mlmodel.save("YourModel.mlpackage", save_format="mlpackage")

In an under-development MacOS & iOS app, I need to identify various measurements from OCR'ed text: length, weight, counts per inch, area, percentage. The unit type (e.g. UnitLength) needs to be identified as well as the measurement's unit (e.g. .inches) in order to convert the measurement to the app's internal standard (e.g. centimetres), the value of which is stored the relevant CoreData entity. The use of NLTagger and NLTokenizer is problematic because of the various representations of the measurements: e.g. "50g.", "50 g", "50 grams", "1 3/4 oz." Currently, I use a bespoke algorithm based on String contains and step-wise evaluation of characters, which is reasonably accurate but requires frequent updating as further representations are detected. I'm aware of the Python SpaCy model being capable of NER Measurement recognition, but am reluctant to incorporate a Python-based solution into a production app. (ref [https://developer.apple.com/forums/thread/30092]) My preference is for an open-source NER Measurement model that can be used as, or converted to, some form of a Swift compatible Machine Learning model. Does anyone know of such a model?

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?

In my quantization code, the line: compressed_model_a8 = cto.coreml.experimental.linear_quantize_activations( model, activation_config, [{'img':np.random.randn(1,13,1024,1024)}] ) has taken 90 minutes to run so far and is still not completed. From debugging, I can see that the line it's stuck on is line 261 in _model_debugger.py: model = ct.models.MLModel( cloned_spec, weights_dir=self.weights_dir, compute_units=compute_units, skip_model_load=False, # Don't skip model load as we need model prediction to get activations range. ) Is this expected behaviour? Would it be quicker to run on another computer with more RAM?

I’m seeing consistent failures using SoundAnalysis live classification when my app moves to the background. Setup iOS 17.x AVAudioEngine mic capture SNAudioStreamAnalyzer SNClassifySoundRequest(classifierIdentifier: .version1) UIBackgroundModes = audio AVAudioSession .record / .playAndRecord, active Audio capture + level metering continue working in background (mic indicator stays on) Issue As soon as the app enters background / screen locks: SoundAnalysis starts failing every second with domain:com.apple.SoundAnalysis, code:2(SNErrorCode.operationFailed) Audio capture itself continues normally When the app returns to foreground, classification immediately resumes without restarting the engine/analyzer Question Is live background sound classification with the built-in SoundAnalysis classifier officially unsupported or known to fail in background? If so, is a custom Core ML model the only supported approach for background detection? Or is there a required configuration I’m missing to keep SNClassifySoundRequest(.version1) running in background? Thanks for any clarification.

I got 3203.23 GFLOPS (FP16) on the M3 Macbook Pro and only 2833.24 GFLOPS (FP16) on the M4 Macbook Air for 4096x4096 matrix multiplications for a PyTorch MPS FP16 Benchmark. Wasn't the performance supposed to be twice as high on the M4 compared to the M3 even with the termal throtling on the Macbook Air? What went wrong?

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?