Hi, guys. I'm writing about Apple Intelligence and I reached the point I have to explain App Intent Domains https://developer.apple.com/documentation/AppIntents/app-intent-domains but I noticed that there is a note explaining that these services are not available with Siri. I tried the example provided by Apple at https://developer.apple.com/documentation/AppIntents/making-your-app-s-functionality-available-to-siri and I can only make the intents work from the Shortcuts App, but not from Siri. Is this correct. App Intent Domains are still not available with Siri? Thanks

I have been able to train an adapter on Google's Colaboratory. I am able to start a LanguageModelSession and load it with my adapter. The problem is that after one simple prompt, the context window is 90% full. If I start the session without the adapter, the same simple prompt consumes only 1% of the context window. Has anyone encountered this? I asked Claude AI and it seems to think that my training script needs adjusting. Grok on the other hand is (wrongly, I tried) convinced that I just need to tweak some parameters of LanguageModelSession or SystemLanguageModel. Thanks for any tips.

Hi everyone, I believe I’ve encountered a potential bug or a hardware alignment limitation in the Core ML Framework / ANE Runtime specifically affecting the new Stateful API (introduced in iOS 18/macOS 15). The Issue: A Stateful mlprogram fails to run on the Apple Neural Engine (ANE) if the state tensor dimensions (specifically the width) are not a multiple of 32. The model works perfectly on CPU and GPU, but fails on ANE both during runtime and when generating a Performance Report in Xcode. Error Message in Xcode UI: "There was an error creating the performance report Unable to compute the prediction using ML Program. It can be an invalid input data or broken/unsupported model." Observations: Case A (Fails): State shape = (1, 3, 480, 270). Prediction fails on ANE. Case B (Success): State shape = (1, 3, 480, 256). Prediction succeeds on ANE. This suggests an internal memory alignment or tiling issue within the ANE driver when handling Stateful buffers that don't meet the 32-pixel/element alignment. Reproduction Code (PyTorch + coremltools): import torch.nn as nn import coremltools as ct import numpy as np class RNN_Stateful(nn.Module): def __init__(self, hidden_shape): super(RNN_Stateful, self).__init__() # Simple conv to update state self.conv1 = nn.Conv2d(3 + hidden_shape[1], hidden_shape[1], kernel_size=3, padding=1) self.conv2 = nn.Conv2d(hidden_shape[1], 3, kernel_size=3, padding=1) self.register_buffer("hidden_state", torch.ones(hidden_shape, dtype=torch.float16)) def forward(self, imgs): self.hidden_state = self.conv1(torch.cat((imgs, self.hidden_state), dim=1)) return self.conv2(self.hidden_state) # h=480, w=255 causes ANE failure. w=256 works. b, ch, h, w = 1, 3, 480, 255 model = RNN_Stateful((b, ch, h, w)).eval() traced_model = torch.jit.trace(model, torch.randn(b, 3, h, w)) mlmodel = ct.convert( traced_model, inputs=[ct.TensorType(name="input_image", shape=(b, 3, h, w), dtype=np.float16)], outputs=[ct.TensorType(name="output", dtype=np.float16)], states=[ct.StateType(wrapped_type=ct.TensorType(shape=(b, ch, h, w), dtype=np.float16), name="hidden_state")], minimum_deployment_target=ct.target.iOS18, convert_to="mlprogram" ) mlmodel.save("rnn_stateful.mlpackage") Steps to see the error: Open the generated .mlpackage in Xcode 16.0+. Go to the Performance tab and run a test on a device with ANE (e.g., iPhone 15/16 or M-series Mac). The report will fail to generate with the error mentioned above. Environment: OS: macOS 15.2 Xcode: 16.3 Hardware: M4 Has anyone else encountered this 32-pixel alignment requirement for StateType tensors on ANE? Is this a known hardware constraint or a bug in the Core ML runtime? Any insights or workarounds (other than manual padding) would be appreciated.

When using the imagePlaygroundSheet modifier in SwiftUI, the system presets an image playground in a fixed size. Especially on macOS, this modal is rather small and doesn't utilize the available space efficiently. Is there a way to make the modal bigger, or allow the user to resize the dialog? I tried presentationDetents, but this would need to be applied to the content of the sheet, which is provided by the system... I guess this question applies to other system-provided sheets like the photo picker as well.

Greetings, Ive been exerimenting with the new Apple intelligence chat. I want to be able to use my custom LLM and I made that work (I can chat back and forward from the left panel with my server) but I cannot find out how to change the editor contents like chatgpt does. chatgpt is able to change the current editor and, seems like, all files in the pbx. I tried to catch the call with charles with no success. In the OpenIA platform docs it doesnt mention anything that could change the code shown. does anyone know how to achieve this? Is the apple intelliece documentation lacking this features and will it be completed soon? will this features even be open for developers?

When I use ChatGPT in Xcode, the following error is displayed： It was working fine before, but suddenly it became like this, without changing any configuration. Why?

With the release of the newest version of tahoe and MLX supporting RDMA. Is there a documentation link to how to utilizes the libdrma dylib as well as what functions are available? I am currently assuming it mostly follows the standard linux infiniband library but I would like the apple specific details.

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 ?

I have been working on a small CV program, which uses fine-tuned U2Netp model converted by coremltools 8.3.0 from PyTorch. It works well on my iPhone (with iOS version 18.5) and my Macbook (with MacOS version 15.3.1). But it fails to load after I upgraded Macbook to MacOS version 15.5. I have attached console log when loading this model. Unable to load MPSGraphExecutable from path /Users/yongzhang/Library/Caches/swiftmetal/com.apple.e5rt.e5bundlecache/24F74/E051B28C6957815C140A86134D673B5C015E79A1460E9B54B8764F659FDCE645/16FA8CF2CDE66C0C427F4B51BBA82C38ACC44A514CCA396FD7B281AAC087AB2F.bundle/H14C.bundle/main/main_mps_graph/main_mps_graph.mpsgraphpackage @ GetMPSGraphExecutable E5RT: Unable to load MPSGraphExecutable from path /Users/yongzhang/Library/Caches/swiftmetal/com.apple.e5rt.e5bundlecache/24F74/E051B28C6957815C140A86134D673B5C015E79A1460E9B54B8764F659FDCE645/16FA8CF2CDE66C0C427F4B51BBA82C38ACC44A514CCA396FD7B281AAC087AB2F.bundle/H14C.bundle/main/main_mps_graph/main_mps_graph.mpsgraphpackage (13) Unable to load MPSGraphExecutable from path /Users/yongzhang/Library/Caches/swiftmetal/com.apple.e5rt.e5bundlecache/24F74/E051B28C6957815C140A86134D673B5C015E79A1460E9B54B8764F659FDCE645/16FA8CF2CDE66C0C427F4B51BBA82C38ACC44A514CCA396FD7B281AAC087AB2F.bundle/H14C.bundle/main/main_mps_graph/main_mps_graph.mpsgraphpackage @ GetMPSGraphExecutable E5RT: Unable to load MPSGraphExecutable from path /Users/yongzhang/Library/Caches/swiftmetal/com.apple.e5rt.e5bundlecache/24F74/E051B28C6957815C140A86134D673B5C015E79A1460E9B54B8764F659FDCE645/16FA8CF2CDE66C0C427F4B51BBA82C38ACC44A514CCA396FD7B281AAC087AB2F.bundle/H14C.bundle/main/main_mps_graph/main_mps_graph.mpsgraphpackage (13) Failure translating MIL->EIR network: Espresso exception: "Network translation error": MIL->EIR translation error at /Users/yongzhang/CLionProjects/ImageSimilarity/models/compiled/u2netp.mlmodelc/model.mil:1557:12: Parameter binding for axes does not exist. [Espresso::handle_ex_plan] exception=Espresso exception: "Network translation error": MIL->EIR translation error at /Users/yongzhang/CLionProjects/ImageSimilarity/models/compiled/u2netp.mlmodelc/model.mil:1557:12: Parameter binding for axes does not exist. status=-14 Failed to build the model execution plan using a model architecture file '/Users/yongzhang/CLionProjects/ImageSimilarity/models/compiled/u2netp.mlmodelc/model.mil' with error code: -14.

Is it possible to train an Adaptor for the Foundation Models to produce Generable output? If so what would the response part of the training data need to look like? Presumably, under the hood, the model is outputting JSON (or some other similar structure) that can be decoded to a Generable type. Would the response part of the training data for an Adaptor need to be in that structured format?

I am working on a lung cancer scanning app in for iOS with a CoreML model and when I test my app on a physical device, the model results in the same prediction 100% of the time. I even changed the names around and still resulted in the same case. I have listed my labels in cases and when its just stuck on the same case (case 1) My code is below: https://github.com/ShivenKhurana1/Detect-to-Protect-App/blob/main/DetectToProtect/SecondView.swift I couldn't add the code as it was too long so I hope github link is fine!

Documentation on adapter train is lacking any details related to training on dataset with tool calling. And page about tool calling itself only explain how to use it from Swift without any internal details useful in training. Question is how schema should looks like for including tool calling in dataset?

Hello Apple Developer Community, I'm exploring the integration of Apple Intelligence features into my mobile application and have a couple of questions regarding the current and upcoming API capabilities: Custom Prompt Support: Is there a way to pass custom prompts to Apple Intelligence to generate specific inferences? For instance, can we provide a unique prompt to the Writing Tools or Image Playground APIs to obtain tailored outputs? Direct Inference Capabilities: Beyond the predefined functionalities like text rewriting or image generation, does Apple Intelligence offer APIs that allow for more generalized inference tasks based on custom inputs? I understand that Apple has provided APIs such as Writing Tools, Image Playground, and Genmoji. However, I'm interested in understanding the extent of customization and flexibility these APIs offer, especially concerning custom prompts and generalized inference. Additionally, are there any plans or timelines for expanding these capabilities, perhaps with the introduction of new SDKs or frameworks that allow deeper integration and customization? Any insights, documentation links, or experiences shared would be greatly appreciated. Thank you in advance for your assistance!

I am follwing this tutorial: https://apple.github.io/coremltools/docs-guides/source/convert-a-torchvision-model-from-pytorch.html I have obtained simialr result using the python code. However when I view it in Xcode, the preview prediction percentage confidence is way off I suspect it is due the the output of the model, which is in percentage already and in Xcode it multiply 100 again leading to this result. Please give me any feedback to fix this, thank you.

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 the play ground I'm trying to bias my LanguageModel to use a tool I registered, but I don't see it actually calling the tool. I'm following the developer video on landmarks itinerary generation tutorial almost verbatim. Is this a prompt engineering thing I'm missing? Or is it possible that I'm injecting my tool wrong?

In WWDC25 Metal 4 released quite excited new features for machine learning optimization, but as we all know the pytorch based on metal shader performance (mps) is the one of most important tools for Mac machine learning area.but on mps introduced website we cannot see any support information for metal4.

I'm using Numbers to build a spreadsheet that I'm exporting as a CSV. I then import this file into Create ML to train a word tagger model. Everything has been working fine for all the models I've trained so far, but now I'm coming across a use case that has been breaking the import process: commas within the training data. This is a case that none of Apple's examples show. My project takes Navajo text that has been tokenized by syllables and labels the parts-of-speech. Case that works... Raw text: Naaltsoos yídéeshtah. Tokens column: Naal,tsoos, ,yí,déesh,tah,. Labels column: NObj,NObj,Space,Verb,Verb,VStem,Punct Case that breaks... Raw text: óola, béésh łigaii, tłʼoh naadą́ą́ʼ, wáin, akʼah, dóó á,shįįh Tokens column with tokenized text (commas quoted): óo,la,",", ,béésh, ,łi,gaii,",", ,tłʼoh, ,naa,dą́ą́ʼ,",", ,wáin,",", ,a,kʼah,",", ,dóó, ,á,shįįh (Create ML reports mismatched columns) Tokens column with tokenized text (commas escaped): óo,la,\,, ,béésh, ,łi,gaii,\,, ,tłʼoh, ,naa,dą́ą́ʼ,\,, ,wáin,\,, ,a,kʼah,\,, ,dóó, ,á,shįįh (Create ML reports mismatched columns) Tokens column with tokenized text (commas escape-quoted): óo,la,\",\", ,béésh, ,łi,gaii,\",\", ,tłʼoh, ,naa,dą́ą́ʼ,\",\", ,wáin,\",\", ,a,kʼah,\",\", ,dóó, ,á,shįįh (record not detected by Create ML) Tokens column with tokenized text (commas escape-quoted): óo,la,"","", ,béésh, ,łi,gaii,"","", ,tłʼoh, ,naa,dą́ą́ʼ,"","", ,wáin,"","", ,a,kʼah,"","", ,dóó, ,á,shįįh (Create ML reports mismatched columns) Labels column: NSub,NSub,Punct,Space,NSub,Space,NSub,NSub,Punct,Space,NSub,Space,NSub,NSub,Punct,Space,NSub,Punct,Space,NSub,NSub,Punct,Space,Conj,Space,NSub,NSub Sample From Spreadsheet Solution Needed It's simple enough to escape commas within CSV files, but the format needed by Create ML essentially combines entire CSV records into single columns, so I'm ending up needing a CSV record that contains a mixture of commas to use for parsing and ones to use as character literals. That's where this gets complicated. For this particular use case (which seems like it would frequently arise when training a word tagger model), how should I properly escape a comma literal?

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.

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?